Tag Archives: cognitive neuroscience

Neuroscientists Make Declaration On Animal Consciousness

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Scientists have officially acknowledged that birds have consciousness, and can experience emotions.

On 7/7/2012 a group of prominent neuroscientists signed a declaration supporting the view that non-human animals experience consciousness. The statement claims to be a ‘re-evaluation of previously held preconceptions’. It states that:

‘Convergent evidence indicates that non-human animals have the neuroanatomical, neurochemical and neurophysiologial substrates of conscious states, along with the capacity to exhibit intentional behaviours‘.

Unfortunately the declaration doesn’t define clearly what exactly the ‘consciousness’ they are referring to is. Instead the text switches between referring to different elements of conscious experience, such as arousal (e.g. levels of sleep and attentiveness), conscious decision making, perceptual distortions (e.g. hallucinations) and the experience of emotional states. As the concept of consciousness is a notoriously difficult one to define, the lack of an operational definition makes the declaration somewhat difficult to interpret.

A further peculiarity of the declaration is that it states something which I suspect the vast majority of scientists working in the fields of neuroscience, psychology and animal behaviour have believed for some time. Indeed I suspect a significant proportion of the ‘general public’ would accept that most animals have some level of conscious understanding, especially mammals. The declaration isn’t therefore heralding a breakthrough in scientific understanding, even if it does contradict certain religious and philosophical standpoints that propose consciousness as a uniquely human characteristic.

Despite these reservations, the declaration may prove to be of importance. It focuses on the commonalities between different animals in the neural structures supporting various conscious experiences, and discusses the implications this may have for understanding the development of consciousness through evolution. It represents an official acknowledgement that a larger range of animals experience consciousness that many may have previously believed, based off the proposition that absence of a cerebral cortex does not preclude conscious thought. Those animals considered ‘conscious’ can therefore include non-mammalian creatures such as insects and cephalopods. The declaration may hopefully lead to greater discussion of both the nature of consciousness, and the relationship between humans and other animals.  More importantly it may facilitate political changes to ensure the more humane treatment of animals.

A full text of the declaration can be found at http://fcmconference.org/img/CambridgeDeclarationOnConsciousness.pdf

Consciousness In The Brain

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You see, but you do not observe…

A Scandal in Bohemia, The Adventures of Sherlock Holmes:  Arthur Conan Doyle

Can neuroscience provide an explanation as to how the brain enables us to consciously process information?

What is the distinction between seeing and observing? The term ‘seeing’ suggests a passive process, whereas observation clearly requires something additional; the attention to a particular detail or details within the visual scene, the extraction of salient information and perhaps the further evaluation of that information. Neuroscience has made great strides in understanding the functioning of our basic sensory mechanisms, such as those that allow seeing. This work has reached such a level that we are now coming close to being able to create ‘bionic eyes’; mechanical replicas which can mimic the workings of damaged parts of the visual system (1). However is it a much harder task to fully understand the myriad of different ‘higher order’ functions that serve to differentiate observation from merely seeing. These functions are the reason that human experience is much more than the sum of the output from our sensory systems. At the heart of this problem is the need to understand the phenomenon of consciousness. Consciousness can be difficult to define precisely, with different philosophers breaking consciousness down into different sets of features (2) producing concepts that, perhaps inevitably, tend to be somewhat vague and potentially overlapping. However the most fundamental aspect of consciousness would appear to be our ability to experience awareness of (certain) sensory information, and to impose our higher order abilities on that information. In short, given that the majority of sensory processing is performed outside of consciousness, how is it that certain information can be sectioned off and subject to processes such as attention, evaluation and reflection, and how is it that we are aware of both the selected data, and the cognitive processes we perform on it?

Brain waves and synchronisation
The simplest way of addressing the issue of consciousness is to compare the response of the brain during circumstances where the level of consciousness awareness is different. It has long been known that states of consciousness (such as wakefulness, sleep and coma) are marked by differences in the pattern of ‘brain waves’; the oscillating electrical signals that are produced by the brain. It would seem sensible therefore to assume that such changes in the pattern of brain waves reflect, at least in part, changes in the functioning of the mechanism that enables consciousness. Similar changes in brain oscillations are also seen in a wide variety of different brain areas during performance of cognitive tasks, which of course also require the conscious processing of information. In general cognitive processes appear not only to alter the power of such oscillations, but also to evoke an increase in synchronisation between these oscillations (such that the phase difference between the signals generated from the brain areas activated by the task remains constant over time). Such synchronisation is believed to allow communication between disparate brain areas; so-called ‘communication through coherence’ (3). If one takes the simple example of one neuronal population passing a signal to another, then to provide the greatest likelihood of that signal being received, the sending neurons must all fire at the same time (hence the oscillating nature of brain waves) thus maximising the signal sent to the receiving neurons. However the timing of this signal is also important. To maximise the chance of the signal being propagated, the firing of the sending neurons must be timed so that the signal arrives at a time when the receiving neurons are optimally receptive to the signal (or alternatively, if inhibition of signalling is required, at a time when the receiving neurons are optimally insensitive of the signal). Therefore when different brain areas need to communicate in order to facilitate cognitive processing their pattern of neuronal firing much achieve coherence, so they tend to synchronise with (for unidirectional, excitation signals at least) the conduction delay between the two areas being equal to the phase difference between the two oscillating signals.

Global Neuronal Workspace
As the cognitive tasks that produce neural synchrony all require conscious processing of some sort, we would expect that the experience of consciousness in general must rely on changes in synchrony between brain areas. Indeed studies that have directly compared conscious vs non conscious processing (e.g. comparing instances where the same stimulus is consciously perceived versus instances where it is not) have found an increase in synchronisation between distant cortical sites not directly related to the processing of the relevant sensory information (e.g. 4). Evidence from several MRI studies suggests that the location of these synchronising sites is consistent across different tasks, involving a specific set of areas in the frontal and parietal lobes as well as the thalamo-cortical circuits that control the flow of sensory information to and from the cortex (see 5 for a review). The relevance of this finding to consciousness is supported by evidence that the source of the altered brain response between different states of consciousness appears to be generated by a similar set of areas (6). This has led to the idea that these brain areas represent a ‘global neuronal workspace’ (GNW: 5,7) that supports consciousness. The GNW system is thought to be able to orchestrate synchronisation between different sensory processing areas in such a way as to allow certain sensory representations to be amplified and maintained, while inhibiting others. As synchronisation facilitates neuronal communication it may allow the specific information being held within different sensory areas to form a single, multi-sensory representation within the workspace, explaining how the conscious experience of perception is of a unified sensation, despite the fact that information from each sense is analysed separately (8 – the ‘perceptual binding’ problem). In addition the parietal and frontal areas of the GNW contain a large number of neurons with long axons which allow these areas to project information to a wide variety of disparate brain areas. This in turn is thought to allow them to make the representation held within the GNW available to the areas of the brain involved in higher processing functions. In effect the amplified representation that is maintained by the GNW is also broadcast to these other processing sites, thus allowing higher order processing of conscious information. It is this selection and amplification of a specific representation, and it’s subsequent global availability (to other brain areas) which we experience as consciousness. The concept of synchronous firing and a global neuronal workspace may also help explain other aspects of the conscious experience, such as metacognition (our ability to perform mental processing on the outputs of other mental processing e.g. to know what we know). Metacognition may simply be the conscious component of a much larger perceptual system that is continuously reflecting on our own activity and its likely consequences (9). The metacognition we experience consciously may therefore simply be the instances where this process reaches conscious access via the GNW and is therefore exposed to other higher order processing functions.

The consequences a neural explanation of consciousness
The study of the neural basis of consciousness is an exciting, but complex subject. It also however raises significant philosophical questions. The idea that consciousness is merely a manifestation of the firing patterns of neurons and their arrangement vis-a-vis each other is not a particularly controversial conclusion from a neuroscience perspective, as one would expect every aspect of human cognition to manifest via changes in brain physiology. However the topic is controversial in general because it suggests that if something as core to our being, to our experience of being ‘human’, as consciousness is in fact solely reliant on biological mechanisms, then concepts such as the mind,  the soul and free are redundant. If there is no ‘ghost in the machine’ driving our conscious behaviour then are we really nothing more than just a collection of tissue; are we really just, in effect, extremely complex machines? The consequences of this discussion has important implications for philosophy and morality (for an interesting discussion on this topic see 10). More optimistically however, the ability to understand the biological underpinnings of consciousness can lead to greater understanding of the basis of neurological disorders that cause the loss of conscious abilities, and of psychiatric symptoms that relate to the disruption of consciousness. For example many people suffering from forms of psychosis can experience what could be termed failures of consciousness, such that patterns of conscious thought become disordered, or that they may feel that their thoughts are being read or even controlled by others. An understanding as to how the brain generates consciousness is surely an important step in identifying what has gone wrong in these situations, and potentially how they can be remedied.

                                                                                                                                                   

Image ‘Idea and Creative Concept’ by ‘Mr Lightman’, courtesy of freedigitalphotos.net http://www.freedigitalphotos.net/images/view_photog.php?photogid=3921

References
1. Mathieson et al (2012). Photovoltaic retinal prosthesis with high pixel density. Nature Photonics, 6, 391-397. http://www.nature.com/nphoton/journal/v6/n6/full/nphoton.2012.104.html
 2. Gok, S.E., and Sayan, E. (2012) A philosophical assessment of computational models of consciousness. Cognitive Systems Research 17–18 (2012) 49–62. http://www.sciencedirect.com/science/article/pii/S1389041711000635
3 Fries, P. (2005) A mechanisms for cognitive dynamics: neuronal communication through neuronal coherence. Trends in cognitive sciences. 9(10) 474-480. http://www.sciencedirect.com/science/article/pii/S1364661305002421
4. Doesburg, S.M., Green, J.J., McDonald, J.J., and Ward, L.M. (2009). Rhythms of consciousness: Binocular rivalry reveals large-scale oscillatory network dynamics mediating visual perception. PLoS ONE 4, e6142. http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0006142
 5. Dehaene, S. and Changeux, J.P., (2011). Experimental and Theoretical Approaches
to Conscious Processing. Neuron 70, 201-227. http://www.cell.com/neuron/abstract/S0896-6273%2811%2900258-3
6. Boly, M et al (2008) Intrinsic brain activity in altered states of consciousness – How conscious is the default mode of brain function? Annals of the New York Academy of Sciences. 1129, 119-129. http://www.ncbi.nlm.nih.gov/pubmed/18591474
7. Dehaene, S. & Naccache, L. (2001) Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework, Cognition 79 1–37. http://www.jsmf.org/meetings/2003/nov/Dehaene_Cognition_2001.pdf
8. Varela, F., Lachaux, J.P., Rodriguez, E., and Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2, 229–239. http://www.nature.com/nrn/journal/v2/n4/abs/nrn0401_229a.html
9. Timmermans, B., Schilbach, L., Pasquali, A., and Cleeremans, A. (2012) Higher order thoughts in action: consciousness as an unconscious re-description process. Phil. Trans. R. Soc. B (2012) 367, 1412–1423. http://rstb.royalsocietypublishing.org/content/367/1594/1412.abstract
10. http://www.time.com/time/magazine/article/0,9171,1580394-1,00.html

The addicted brain

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Addictive behaviours include, but are not limited to, the abuse of drugs

If you’ve got the money honey, we’ve got your disease — Guns n’ Roses: Welcome to the Jungle

One of the key challenges of cognitive neuroscience is to gain an understanding of the neural mechanisms behind the various psychiatric disorders that can blight mankind. Knowledge of the how various brain mechanisms work in health, and how, and in what way, they become defective is crucial for the development of neurological treatments for such conditions. Such an approach doesn’t imply tacit acceptance of the idea that all behaviour is guided by changes in the brain, or that psychiatric problems are solely of a biological origin. Indeed it is well established that social and psychological factors can drive changes in brain function (for example purely cognitive therapies can alter the patterns of neural firing [1]). What understanding the neural basis of disease does allow, is the development of better methods of tackling such conditions at a neurological level, which is important because in many patients the social and psychological factors that have triggered their condition may prove to be either impractical or impossible for clinicians to alter (e.g. changing the structure of society).

Addiction is an extremely prevalent problem in modern society. Alcohol and opiate addictions alone are estimated to affect 15million Europeans, costing around 65 million Euros a year in both health and non-health related costs (2). Addiction can be defined as the persistent, compulsive dependence on a behavior or substance (3) and therefore spans not just drug dependencies, but also ‘behavioural addictions’ such as gambling, overeating, sex addiction and compulsive shopping (oniomania).  Although the definition of addiction is reasonably straightforward, the process of addiction needs to be broken down into its constituent cognitive parts before it can be fully understood. Addiction, and indeed all psychiatric problems, are not unitary constructs; they reflect abnormalities in several different facets of human cognition. For example unipolar depression can involve not just low mood, but also failure to respond to pleasurable experiences (anhedonia), low energy, anxiety and loss of appetite. Breaking down such conditions into their components parts is crucial if we are to be able to understand how they develop and how they can be treated. From a clinical perspective, focusing on the array of symptoms rather than the overall condition can help identify sub-types of the condition, which in turn can allow treatments to be modified to address the particular set of symptoms presented by an individual patient.

So which cognitive processes may be at fault when an individual becomes addicted? While opinions vary on this subject, in general it can be said that addiction involves abnormalities in the following interconnected processes:

  • Reward processing
  • Motivation and learning
  • Decision Making
  • Cognitive control
  • Insight

By their nature addictive behaviours have, at least initially, a rewarding effect. Moreover these effects are felt both by those who later become addicted and those that do not. Clearly therefore something in the processing of rewarding events must either change during addiction, or be naturally defective in the addicted individual. Unfortunately, while there are a number of different theories concerning how reward processing is disrupted in addiction, the exact nature of the deficiency is as yet uncertain. For example do people become sensitized to a drug and thus gradually require more to be able to maintain a balanced physiological state, or are people at risk of addiction more naturally prone to negative emotions and therefore have a greater tendency to seek out rewarding stimuli despite the risk? Despite this uncertainty around the exact nature of the cognitive deficiencies in reward processing, neurological research has revealed that experience of reward (e.g. intoxication) is strongly associated with activity within circuits of the brain that make use of the neurotransmitter dopamine (neurotransmitters are the chemicals that facilitate communication between different neurons in the brain). This dopaminergic system encompasses subcortical areas directly related to processing of motivationally relevant stimuli, such as the striatum and amygdala, as well as cortical areas such as the prefrontal cortex which are involved in the prediction of future reward, the evaluation of existing rewards and decision making (4). Various addictive drugs appear to alter the balance of dopamine within this system, usually increasing it, presumably creating the feeling of high associated with drug taking. Over the long term an ‘exhaustion’ effect may occur, whereby the brain is unable to maintain its previous tonic (standard) level of dopamine because of the effect on dopamine levels of frequent performance of the  addictive behaviour. This may then lead to the withdrawal state and to a situation where the addicted user becomes trapped in a cycle of repeating the addictive behaviour, not to achieve the high that the behaviour was initially associated with, but merely to maintain a acceptable tonic level of dopamine, thus avoid the ‘low’ that occurs with withdrawal from the behaviour.  Other neurotransmitter systems which also innervate similar brain areas, such as the noradrenergic system, also play a part in addiction, although they have in general been less widely studied regarding their role in reward processing.

Stimuli that are not directly rewarding, but are predictive or otherwise associated with the positive effects of the addictive behaviour, act to induce cravings for the addictive behaviour. The processing of such ‘addictive cues’, in comparison to similar stimuli unassociated with the addiction, tend to provoke greater activity in a wide variety of brain areas including those involved in the actual processing of reward, alongside frontal-cortical circuits involved in the regulation of thoughts and actions,  and areas involved in memory, sensory processing and the engagement of motor actions (5). This suggests that contextual factors that induce cravings can not only evoke brain activity in the reward centers of the brain, but also engage greater perceptual processing and attention, and even trigger motor activity, presumably in preparation for seeking out or performing the addictive behavior. Dysfunctions within these circuits are likely to have a knock-on effect on the processes such as learning and memory. Persistent performance of the addictive behaviour after exposure to addictive cues will lead to a strengthening of the association between the cue and the behaviour, and between both the cue and behaviour and the subsequent hedonic effects of the reward. The strengthening of such associations can lead to a behaviour that was previously under conscious control becoming habitual. The more habitual or automatic a behaviour becomes, the more effort is required to control it, and ultimately the more likely the behaviour is to be performed regardless of its utility in a particular circumstance. In short, it becomes compulsive. Indeed the ease with which a behaviour can become habitual may distinguish addicts from those who remain ‘casual users’.

In addition to the neural circuits involved in reward and learning, the frontal areas of the brain which are also activated by both the addictive behaviour itself and during craving are crucial in the process of addiction. Such areas are broadly believed to be involved in ‘cognitive control’; they act to regulate activity from the more primal, sub-cortical brain areas which are involved in motivation, emotional and learning. This effectively meaning that they provide control over thoughts and behavior. Perhaps unsurprisingly, the (partially separate) systems within the frontal cortex that are involved in decision making and in inhibiting pre-potent (i.e. habitual or natural) responses are both found to be deficient in addicted populations, thus explaining why addicts make decisions that are counter-productive to their health, even when they are fully aware of the likely consequences of their actions (8). Increasing sensitivity, or reactivity from the subcortical reward circuits, coupled with a weakening of the control exerted on them by the frontal control areas is likely to be behind the habituation of addictive behavior, and the subsequent failure to regulate that behavior. In some senses the addict (or more accurately, the frontal control areas of the addict’s brain) loses control over their instinctive behavior.

One of the most serious problems with addiction can be what is termed ‘insight’ or the ability to understand that you are ill. Lack of insight is a severe challenge for clinicians as it can be nearly impossible to effectively implement any treatment when the patient is unaware that the treatment is needed. Again frontal areas, most notably the Insula and anterior cingulate cortices, appear to be crucially involved in the lack of insight (6). The Insula is involved in monitoring internal body states (interoceptive awareness) and producing the ‘subjective experience’ relating to this. It also is involved in deriving salience from sensory information and, along with the anterior cingulate, influencing behavior accordingly (7) thus providing a crucial system for the expression of the effect of addictive cues on behaviour. Addiction-induced dysfunctions in this system may therefore lead to an inability to properly process and respond to changes in body state caused by the performance of (or withdrawal from) the addictive behavior, and may stop the individual from fully appreciating that addictive cues are provoking the cravings which are driving the addictive behaviour. Thus insight into the problematic nature of their condition is lost to the individual.

This article represents a very brief overview of the sorts of cognitive and neural structures involved in addiction. It isn’t unfortunately possible to do justice to the full scope of research into addictive behavior in a short article. What should be clear however is that drug abuse can induce changes in a multitude of different interconnected neural circuits, affecting a multitude of different cognitive functions. This effect can be somewhat different depending on the drug of abuse, but nevertheless also applies to a significant extent to non-drug addictions, implying that such neurological changes can occur without the direct influence of external chemical agents. It follows that these changes must therefore be at least partly the consequence of purely internal, cognitive shifts in the workings of the brain, which do, of course, also occur in drug based additions, thus exacerbating the natural neurochemical effects of the drug. Despite the complexity of the processes involved, increased understanding of the neurological and cognitive basis of addiction should enable, in time, more advanced and effective treatments to be designed. Future research into addiction will also hopefully enable ‘markers’ for the condition to be identified; biological or cognitive indices that predict those who are at potential risk of addiction. This in turn would improve our ability to take preventative measures to reduce the prevalence of this debilitating problem.

 

References

1) Porto, P. R., Oliveira, L., Mari, J., Volchan, E., Figueira, I., & Ventura, P. (2009). Does Cognitive Behavioral Therapy Change the Brain? A Systematic Review of Neuroimaging in Anxiety Disorders. Journal of Neuropsychiatry and Clinical Neurosciences, 21(2), 114-125. http://neuro.psychiatryonline.org/article.aspx?articleID=103678

2) Olesen, J., Gustavsson, A., Svensson, M., Wittchen, H. U., Jonsson, B., Grp, C. S., et al. (2012). The economic cost of brain disorders in Europe. European Journal of Neurology, 19(1), 155-162.  http://onlinelibrary.wiley.com/doi/10.1111/j.1468-1331.2011.03590.x/full

3) http://medical-dictionary.thefreedictionary.com/addiction

4) Parvaz, M. A., Alia-Klein, N., Woicik, P. A., Volkow, N. D., & Goldstein, R. Z. (2011). Neuroimaging for drug addiction and related behaviors. Reviews in the Neurosciences, 22(6), 609-624. http://www.bnl.gov/medical/Personnel/Rita-Goldstein/files/Parvaz_RNS2011.pdf

5) Yalachkov, Y., Kaiser, J., & Naumer, M. J. (2012). Functional neuroimaging studies in addiction: Multisensory drug stimuli and neural cue reactivity. Neuroscience and Biobehavioral Reviews, 36(2), 825-835. http://www.sciencedirect.com/science/article/pii/S0149763411002119

(6) Goldstein, R. Z., Craig, A. D., Bechara, A., Garavan, H., Childress, A. R., Paulus, M. P., et al. (2009). The Neurocircuitry of Impaired Insight in Drug Addiction. Trends in Cognitive Sciences, 13(9), 372-380. http://www.sciencedirect.com/science/article/pii/S1364661309001466

7) Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct, 214(5-6), 655-667. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2899886/

8 ) Duka, T., Crombag, H. S., & Stephens, D. N. (2011). Experimental medicine in drug addiction: towards behavioral, cognitive and neurobiological biomarkers. Journal of Psychopharmacology, 25(9), 1235-1255.  http://jop.sagepub.com/content/25/9/1235.short

Can a neuroscientist read your mind?

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Are the contents of your mind really 'confidential' or will your thoughts one day be accessible to others?

Media reports into recent research have claimed that neuroscientists are now effectively able to perform ‘mind reading’. Such reporting inevitable raises ethical questions about what applications such research might eventually be put to, and, judging by some of the comments that the on-line versions of these articles have provoked, have alarmed some people regarding the eventual path that such research might take. But how accurate is the claim that neuroscientific techniques can read minds?

Early this year an article in the Guardian  ( http://www.guardian.co.uk/science/2012/jan/31/mind-reading-program-brain-words ) reported that:

‘Scientists have picked up fragments of people’s thoughts by decoding the brain activity caused by words that they hear.’

Reporting on the same experiment the Daily Mail ( http://www.dailymail.co.uk/sciencetech/article-2095214/As-scientists-discover-translate-brainwaves-words–Could-machine-read-innermost-thoughts.html ) claimed:

 ’It’s a staggering development that could have tremendous implications….judges could use mind-reading machines to find out if murder suspects are telling the truth….mind reading devices might be used to eavesdrop covertly on the most private thoughts and dreams.’

The experiment in question, conducted by Dr Brian Pasley and colleagues (1) involved the recruitment of patients who were to undergo brain surgery. The researchers placed electrodes upon the auditory areas of the brain during the period when the patients’ skulls were open and their cerebral cortex exposed. They then played the patients a sequence of different words and recorded the electrical activity generated by the auditory cortex in response to this speech. Using complex modeling procedures they were able to reconstruct the spoken words solely from the neural signals recorded by the electrodes. Furthermore they were able to successfully apply this model to the electrical responses generated by a separate set of words that had not been used in creation of the model (e.g. which were in effect ‘novel’ to the model) suggesting that the model could theoretically be applied to reconstruct any speech heard by the patient.

While these results are undoubtedly impressive, has the media coverage of them been accurate? In terms of the Guardian’s report, their claim that this represents a decoding of ‘fragments of thoughts’ seems to depend on a rather broad definition of the term ‘thoughts’. What the research did was to reconstruct auditory stimuli that the auditory cortex was in the process of analysing. What has been achieved therefore is the decoding, at a detailed level, of the perceptual process, NOT the reading of internally generated thoughts. This is a significant step away from ‘decoding thoughts’ as the  process being decoded is entirely dependent on the presentation of an external stimulus. This doesn’t therefore represent ‘mind reading’ because the same result could theoretically be achieved without reference to the brain, e.g. by taking measurements from the relevant sensory organ or by just observing the sensory stimulus itself (2). Even if the research did represent mind reading, there seems little justification for the Daily Mail’s claim that the research could lead to ‘covert eavesdropping’. It should be obvious that the methodology required not only the opening up of the participant’s skull, but also the co-operation of the participant in allowing data to be taken for the construction of the model. Furthermore what is not mentioned by either article is that the reconstructed words were not actually intelligible to a human listener, but had to be ‘recognised’ via a speech recognition algorithm (an example of the reconstructed speech can be heard here:  http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001251#s5).

Actual Mind Reading?

While the results of Dr Pasley’s study required the participant’s brains to be exposed, other neuroimaging methods are not so intrusive, and could therefore be considered closer to the covert mind-reading reported by the Mail. Magnetic Resonance Imaging (MRI) allows brain activity to be measured in a non-invasive way, so that no surgery of any kind is required (although lying down in a scanner which costs millions of pounds and is the size of a small boat, is still required, making it far from ‘covert’!). MRI studies have produced some equivalent results to that of Pasley’s study, but using visual stimuli; with images (3) and short movies (4) having been reconstructed purely from data obtained from MRI scans. Of course such results don’t represent mind reading any more than Dr Pasley’s study, since they reflect a reconstruction of external sensory information. However other MRI studies have produced results that have allowed scientists to predict processes occurring within a participant’s brain that are not directly tied to the characteristic of external stimuli. A couple of studies by Yukiyasu Kamitani and Frank Tong (5,6) have shown that models can be created that allow an observer to identify to which stimulus a participant is (covertly) attending to. In effect these studies, and others like them, use the output from the perceptual processing mechanisms of the brain to identify how ‘top-down’ influences (such as expectation and attention) are driving perception. Strictly speaking they represent mindreading as although the mental processes in question are still involved in analysing external stimuli, it is not necessarily possible to garner the information provided by the MRI data in any other way (short of asking the person themselves). This is because the ‘top-down influences’ in question arise internally from the brain, rather than being a function of the external stimulus. Neuroimaging has enabled the concept of mind reading to be taken further however, into the realms of decoding mental events that don’t rely on any external stimulation at all. Recent studies have found that it is possible to decode what broad categories of objects someone is imagining, in the absence of any coincident external stimulation (7) although the performance level of the model is reasonably modest (~ 50%). Similarly, it also appears that the results of basic decision making processes can be identified from brain activity, with decisions relating to which button to press and when to press it (8) and whether a participant in lying (9) being decipherable using models constructed in a similar way to those already described. Interestingly the neural information that allows these decisions to be decoded occurs many seconds BEFORE the decision has actually been made, highlighting how conscious actions are likely driven by brain processes that are outside conscious awareness, rather than being the result of conscious ‘free will’. Most recently such work has been extended to more complex scenarios, with MRI data being used to predict at what point in solving an algebraic problem a child is at, and whether they are performing the calculation correctly (10).

The possibility of covert mind reading?

Clearly the aforementioned examples reflect mind reading, but do they represent the top of a ‘slippery slope’ that will lead to technology that will allow the sort of covert eavesdropping envisioned by the Daily Mail? The first impediment to such technology is the process of neuroimaging itself. MRI scanners are far from being portable enough to allow forced or covert application of brain scanning. Furthermore MRI scanning involves the production of a large magnetic field and the firing of electromagnetic pulses towards the object being imaged, both functions that would be totally impractical outside a controlled, isolated environment. Other neuroimaging methods, such as EEG, function by recording the electrical remnants of brain activity from outside the skull, and are therefore cheaper and more portable than MRI. However they lack the spatial resolution that would be required for any sophisticated mind reading application, and in any case they are extremely sensitive to external noise, again making them unsuitable for use outside of controlled environments.

Even if we assume that future technological advances would allow systems to be developed that would enable covert collection brain activity data, would such technology enable your innermost thoughts to be deciphered? There are a number of reasons to doubt that this would be possible. Current mind reading models are only able to distinguish between very broad categories of thoughts, or between very coarse categories of decisions (e.g. lie/truth, attending to one or other stimulus). To be able to read the specific details of an individual’s thoughts you would need models that distinguished between the literally billions of different things that someone could be thinking about, and the multitude of different decisions that they could make. To even create such models would involve the co-operation of individuals in a data collection process that would take an incalculable length of time. Even if such data were collected, and the subsequent required level of computation to create accurate models were possible, the ability to generalize such models to the brain activity of other individuals would rely on an assumption that every person’s brain being identical in terms of where different individual thoughts and memories are stored. This seems extremely unlikely, and is in fact counter to what we know about individual differences in brain anatomy and function. Thus while it is possible to aggregate data across participant to produce mind-reading for coarse decisions, it would be impossible to replicate such a method to distinguish between more subtle categories of thought. Even in situations where co-operation of the participant is attained, and only a coarse distinction between different psychological states is required, such mind reading techniques are problematic. Taking the example of the mooted ‘MRI Lie detector’ such a system will always be somewhat unreliable because, just like the current physiological lie detectors, they could be easily deceived if the participant can train themselves to act as if the truth is a lie (or vice versa). This is because the brain activity which is associated with lying most likely relates to the emotional and cognitive processes involved in creating a false story, rather than to lying per se. It follows that simply engaging in these same emotional and cognitive processes while telling the truth should produce neural activity which mimics that produced by a lie. If even the decoding of simple decisions can be subverted easily, it would seem impossible that attempts at more subtle discriminations of different thoughts would not be subject to even greater uncertainty. Finally it is important to note that all the forms of mind reading reviewed here are the result of probabilistic calculations. The parts of the brain that are deemed active at a certain point in time are the result of statistical computations as to whether a small signal is reflective of task-related neural activity or noise. Likewise the classification of such activity as belonging to one category of thought/decision over another is also based off probabilistic inference. There is no certainty in such a process; in fact it is fraught with uncertainty.

To conclude it seems very unlikely that neuroimaging methods will ever be able to perform the sort of mind reading predicted by scare stories in the press. In some cases such methods may not even represent a particular improvement on the sort of mind reading applications that already exist. What the mind reading research discussed in this article does allow is a greater understanding of how the brain works, which in turn provides insight into how the brain achieves the myriad feats it performs so frequently with apparent ease. The most fruitful practical application of such knowledge is likely to be in the treatment of patients with brain damage. For example the limited mind reading functions possible from existing neuroimaging methods may allow technology to be developed that would allow patients who suffer from brain damage to the extent that they cannot communicate using their peripheral nervous system, some primitive form of communication through their brain activity. In contrast your private thought and memories are likely to remain safe from the prying eyes of neuroscientists!

Image (top right) courtesy of Idea Go:  http://www.freedigitalphotos.net/images/view_photog.php?photogid=809

References

(1) Pasley BN, David SV, Mesgarani N, Flinker A, Shamma SA, et al. (2012) Reconstructing Speech from Human Auditory Cortex. PLoS Biol 10(1): e1001251. doi:10.1371/journal.pbio.1001251 http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001251

(2) Tong, F. & Pratte, M.S. (2012) Decoding Patterns of Human Brain Activity. Annual Review of Psychology, 63: 483-509.  http://www.ncbi.nlm.nih.gov/pubmed/21943172

(3)  Miyawaki, Y. Uchida, H. et al (2008) Visual Image Reconstruction from Human Brain Activity using a Combination of Multi-scale Local Image Decoders.. Neuron 60, 915–929, http://iopscience.iop.org/1742-6596/197/1/012021

(4)  Nishimoto, S., Vu, A.T., et al (2011) Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies. Current Biology 21, 1641–1646 http://www.sciencedirect.com/science/article/pii/S0960982211009377

(5) Kamitani Y, Tong F. 2005. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8:679–85  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1808230/

(6) Kamitani Y, Tong F. 2006. Decoding seen and attended motion directions from activity in the human visual cortex. Curr. Biol. 16:1096–102 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1635016/

(7) Reddy, L., Tsuchiya, N. & Serre, T. (2010). Reading the mind’s eye: Decoding category information during mental imagery. Neuroimage. 50(2) 818-825  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2823980/

(8) Soon CS, Brass M, Heinze HJ, Haynes JD. 2008. Unconscious determinants of free decisions in the human brain. Nat. Neurosci. 11:543–45  http://www.nature.com/neuro/journal/v11/n5/full/nn.2112.html

(9) Davatzikos C, Ruparel K, Fan Y, Shen DG, Acharyya M, et al. 2005. Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage 28:663–68  http://www.sciencedirect.com/science/article/pii/S1053811905005914

(10) Anderson, J.R. (2012) Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model algorithms. Neuropsychologia, 50(4) 487-498. http://www.sciencedirect.com/science/article/pii/S0028393211003605

 

Anxiety enhances sense of smell

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By Maria Panagiotidi

Anxious people have a heightened sense of smell, when it comes to sniffing out a threat, according to a new study by Elizabeth Krusemark and Wen Li from the University of Wisconsin-Madison in the US. The results of their study will be published online in the journal Chemosensory Perception.

The sense of smell is an essential tool for survival in animals. It allows them to detect, locate and identify predators in the surrounding environment. In fact, the olfactory-mediated defence system is so important in animals, that the mere presence of predator odours can evoke potent fear and anxiety responses.

Smells also evoke powerful emotional responses in humans. Krusemark and Li hypothesized that in humans, detection of a particular bad smell may signal danger of a noxious airborne substance, or a decaying object that carries disease. Also, they speculated that the level of response to the above could underlie phobias or anxiety related disorders.

The researchers tested their hypotheses by combining assessment of state-level anxiety, psychophysical testing, and functional magnetic resonance imaging (fMRI) techniques.  They recruited 14 young adult participants who were exposed to three types of odours: neutral pure odor, neutral odor mixture, and negative odor mixture. The participants were asked to detect the presence or absence of an odour in an MRI scanner. During scanning, the researchers also measured skin conductance response (a measure of arousal level), and monitored the subjects’ breathing patterns. After completing the odour detection task, the participants were asked to rate their current level of anxiety using a standardised clinical test.

The authors found that as anxiety levels rose, so did the subjects’ ability to discriminate negative odours accurately – suggesting a ‘remarkable’ olfactory acuity to threat in anxious subjects. The same pattern was found in the skin conductance results which showed that anxiety also heightened emotional arousal to smell-induced threats.

Krusemark and Li uncovered amplified communication between the sensory and emotional areas of the brain in response to negative odours, particularly in anxiety. This increased connectivity could be responsible for the heightened arousal to threats.

These findings could help researchers elucidate the aetiology of the unfortunate and debilitating symptoms that perpetuate anxiety disorders.

 

Reference:

Krusemark EA & Li W (2012). Enhanced olfactory sensory perception of threat in anxiety: an event-related fMRI study. Chemosensory Perception. DOI 10.1007/s12078-011-9111-7

You can find the article here: http://www.springerlink.com/content/a268t518p1x59v68/

Humans as data sources!

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I have recently begun collecting data for an experiment.  Data collection is the ‘bread and butter’ of science, without it there is no data, and therefore no results, conclusions or theories. While scientists can collect data from almost anything, as I am involved in behavioural science the data I require almost always comes from people; volunteers who agree to participate in an experiment. Using human participants (volunteers were previously referred to as ‘subjects’, but this term was dropped because it suggests that the volunteer is ‘subject’ to the experiment, rather than a willing participant) as your main data source produces additional (or at least different) problems to that presented from other data sources. I presume that in natural sciences, materials are ordered from a supplier, and therefore can be (hopefully) acquired to a predetermined timescale at a predictable cost. This is not the case with using participants, whose availability depends on the willingness of the local (normally student) population to submit to your study. Likewise whereas physical data-sources presumably perform reasonably consistently (i.e. putting the same quantity of lithium into the same quantity of water will always produce similar results, as long as other relevant variables are held constant) the same cannot be said for humans. The performance of two participants, tested under identical conditions, can vary drastically, even when the participants are from very similar backgrounds. Similarly an individual participant’s performance can vary widely during an experiment as concentration and motivation fluctuate. These factors produces a large amount of variance in the resulting data that is not due to the experimental manipulations the study is designed to investigate. The consequence of this is that the amount of data that needs to be collected in order to overcome such variance, and therefore provide a valid result, increases.

The variability in human performance also generates the further problem of generalisation. How can you be sure that the participants you have used in your study provide data that can be generalised to humans in general, given that individuals vary widely on how they perform the task? Larger samples (more data collection!) can make a sample more representative, but as undergraduates are usually the easiest source of data, inevitably most studies involving humans utilise samples that are non-representative of the general population to a greater or lesser extent. You could write an entire book on the issues around sampling and generalisation (indeed many have (1)) suffice to say that when you read any behavioural science research, especially that which is weighted towards the ‘social science’ end of the spectrum, it is worth considering the sort of people who may have participated in the research, and how that may effect the results that were found.

There are other, more basic problems with using humans as a data source.  Participants may fail to show up for the study, they may fail to understand what is required of them in ways that you couldn’t predict, they may even not take the experiment seriously, making little effort or deliberately producing nonsensical data. In physical science I suspect the main problem that can occur with an experiment is equipment failure. This is also a danger with behavioural experiments, but ‘participant failure’ is often a more pressing concern.

A final issue with using humans as a data source is that any study involving humans requires ethical approval, meaning that the research design is scrutinized by a committee prior to data collection for anything that might be deemed unacceptable. Ethical procedures are in place for a good reason, as in the past certain scientists were subjecting volunteers to all sorts of unpleasant and/or morally dubious procedures in the name of science (2). However perhaps inevitably ethical checks tend towards the cautious in terms of their application. While for many behavioural and social science research, ethical approval is merely a formality, it can restrict scientific enquiry for those of us that are interested in the facets of human behaviour that can only be evoked through manipulations of the participant’s emotional state or physical comfort.

So, given that I have just spent 700 words complaining about the problems of using humans as data sources,  why have I chosen a career path which relies so heavily on collecting data from humans? Well there are some advantages of performing research on humans. Most importantly humans are (to me at least) the most interesting subject in science. You can keep your chromatography, your mutagenesis and your particle accelerators, nothing they produce will ever be as interesting to me as investigations into human mind and behaviour. The variability in human performance which causes us so many problems is actually the main reason the subject of psychology is so interesting. A second advantage to behavioural research is that it allows you to meet a lot of different people who volunteer for your study for a variety of different reasons. The fact that certain people are prepared to give up their time and submit themselves to the often unpleasant or tedious tasks that make up your research project has helped reaffirm my faith in human nature after years of working in soul-destroying office jobs. Apart form anything else, the actual data collection part of a behavioural study certainly helps to break up a research process which would otherwise mainly consist of reading journal articles and staring at a matrix of numbers on a computer screen.

I’ll be coming to the end of the data collection process soon. I will then have weeks of grappling with the resultant data to look forward to!! As a final plea, if there are any men out there who fancy participating in my research then get in contact, as I still need a few human ‘data sources’ to complete my study!

(1) Rao (2000) Sampling methodologies with applications. Chapman & Hall
(2) See the early chapters of Naomi Klein’s book “The Shock Doctrine” (Penguin, 2008) for a description of some particularly unethical experiments performed in the US.

What is cognitive neuroscience, and why should anyone care?

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I often have trouble explaining to people what I am doing for my PhD. This is not a consequence of the topic being so fiendishly complex that no-one else can understand it. Instead it comes from a fact that the area of study seems to fall between several difference subject areas. When I tell people that I am doing my PhD within the Neuroscience department I imagine this provokes images of test-tubes, microscopes and pipettes, and perhaps associations with genetics, animal testing and stem cells. In reality I have little knowledge or experience of any of these topics, having last done ‘traditional’ lab work while I was at secondary school. If you asked me to dissect something, I would probably run a mile! When I instead say that I work within the psychiatry department this probably brings up an altogether different set of images, of drug therapies, ECT and perhaps of ‘talking therapies’ such as CBT (cognitive behavioural therapy). In fact both the above statements regarding my PhD are true, as the Psychiatry department sits within the Neuroscience department, but neither appear to give an accurate impression of what I actually do.

The best description of my area of research is ‘cognitive neuroscience’, but what does this mean? Cognitive Neuroscience relates to the study of the neural basis of behaviour. Roughly, it bridges the gap between biological sciences, and behavioural sciences such as psychology and psychiatry. It attempts to determine how the brain achieves the legion of processes that it performs – crudely ‘what part of the brain does what’! Cognitive neuroscience has only been seen as a separate area of study relatively recently, partly because the advanced brain imaging techniques which the discipline now heavily relies on have only been developed within the last 30 years (according to Wikipedia the term ‘cognitive neuroscience’ itself was coined in the back of a taxi in 1979!!). However scientists from various disciplines have been trying to understand how the brain functions, using whatever methods were available, since at least the 19th century.

Cognitive Neuroscience relies heavily on work done within behavioural sciences, which have served to define how human behaviour and cognition can be classified into concepts that can be studied. Unsurprisingly therefore, cognitive neuroscience research normally involves the application of a behavioural task which has already been utilised without the use of brain imaging techniques. One question this raises is what does knowing how the brain achieves it function tell us that purely behavioural science does not?  Psychologists have been ably investigating the details of mental processes for well over a century without knowing (or even caring) what part(s) of the brain are involved. The knowledge that spatial processing is largely dependent on the Hippocampus is not necessary for studying the intricacies and individual differences in spatial processing. So what does an understanding of the neural basis of mental processes achieve?

Firstly understanding the neural basis of a mental process can help distinguish between different theories relating to how that process is performed. Behavioural data is often not sufficient to distinguish between competing theories (e.g. whether a particular process is performed in totality, or whether it is split into components processes that are dealt with separately, and whether such component processes are performed in parallel or in series). Neuroimaging data can be used to provide strong evidence in relation to these questions (1).  Secondly cognitive neuroscience can provide insight into areas of cognition that were difficult or impossible to address without neuroimaging techniques. For example much work has been done on trying to understand what the brain does ‘at rest’ (i.e. when no task is being performed, effectively ‘mind wandering’) which can allow us to understand how the brain might work as an self-contained integrative mechanism. As, by definition, non-task related mental processes can’t be manipulated systematically, it is hard to investigate these processes from a purely behavioural standpoint. Similarly neuroimaging has enabled scientists to begin to uncover the neural basis of ‘consciousness’, raising interesting questions about how our experience of the world is constructed (3). These achievements of cognitive neuroscience help elucidate the nature of human thought and behaviour, shedding light on why we act the way that we do. 

On a larger scale, understanding how the brain is able to processes such a large variety of information, and produce such a wide variety of responses, can help guide the design of artificial intelligence systems intended to mimic human abilities, facilitating advances in medicine and engineering. Finally, and perhaps most importantly, knowing how the brain produces certain responses can lead to the development of interventions to alter the functioning of the appropriate brain areas when those responses become problematic (e.g. during mental health disorders). One of the major aims of cognitive neuroscience is to identify the neural deficiencies that mark various psychiatry and neurodegenerative disorders. From this information it becomes potentially possible to identify methods of combating such deficiencies. Indeed biological interventions are being developed that can target specific brain areas, potentially offering great hope for improving the therapeutic treatment of mental disorders.  

References

(1) Jonides et al (2006). What has Functional Neuroimaging told us about the Mind? So many examples, so little space. Cortex, 42, 414-417 http://www-personal.umich.edu/~jjonides/pdf/2006_3.pdf

(2) Van den Heuval & Pol (2010) Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519-534 http://www.sciencedirect.com/science/article/pii/S0924977X10000684

(3) Dehaene & Changeux (2011) Experimental and Theoretical Approaches to Conscious Processing. Neuron, 70. 200-225 http://www.sciencedirect.com/science/article/pii/S0896627311002583