Advances in Brain Imaging: A Shift Towards Functional Connectivity

Numerous imaging techniques have been utilised to study the structure and function of the brain, including functional magnetic resonance imaging (fMRI), positron-emission tomography (PET) and X-ray computed tomography (CT). fMRI is a form of blood-oxygen-level dependent (BOLD) contrast imaging which measures the relative proportions of oxyhemoglobin and deoxyhemoglobin – based on the assumption that oxygenated blood flows to active neurons at a greater rate than inactive neurons (the hemodynamic response) – while PET detects gamma rays emitted by a positron-emitting radiotracer introduced into the body and CT combines many X-ray images to construct a 3D representation of the brain. fMRI has typically been used to map brain regions which become activated while the subject is engaged in a particular task, however more recently it has been realised that since brain regions often work in networks (e.g. the default mode network), understanding cognitive processes and behaviours at a neurobiological level requires analysis of the functional connectivity between brain regions constituting functional networks (Menon, 2011). The brain is a unique organ in that, despite its fixed anatomy, local interactions are dynamically modulated to allow a vast functional repertoire (Park & Friston, 2013). Thus, projects such as the Human Connectome Project have begun to map the functional connectivity of the brain using resting-state and task-based fMRI, while recent advances in neuroimaging technology have enabled mental processes and behaviours to be correlated with specific brain structures with greater accuracy and reliability. 

Tong & Pratte (2012) discuss the advances in brain imaging, presenting a wealth of information gathered on a range of mental processes using novel fMRI techniques such as multivoxel pattern analysis. The authors discuss how new techniques allow (to some extent) the ability to “mind read” which of two previously viewed images a subject was imagining, and go as far as to say: “As these methodologies continue to advance, it will become increasingly important to consider the ethical implications of this technology”. This is a bold statement emphasising the fast-evolving nature of brain imaging technology. The authors also review how numerous studies over the past decade have succeeded in decoding various top-down mental processes, such as feature-based attention (Kamitani & Tong, 2005), imagination (Reddy et al., 2010), episodic memory (Rissman & Wagner, 2012) and numerical processing (Knops et al., 2009). For example, Kamitano & Tong (2005) found, using statistical algorithms on fMRI data, that specific fMRI signals in the visual cortex (V1) could reliably predict which of eight stimulus orientations the subject was attending to, indicating that the visual cortex encodes detailed orientation information which can reliably predict subjective perception and suggesting direct orientation mapping at a neurobiological level. Later neuroimaging research by Tong et al. (2012) found that the correlation between such orientation-selective activity patterns and the quality of the sensory input could be directly predicted by the average BOLD amplitude in the brain region of interest using multivariate pattern analysis, offering a reliable model of fMRI pattern classification. This is a prime example of how both brain imaging technology and the way in which researchers use it has advanced over recent years, allowing unprecedented revelations in the neurobiology of specific brain functions.

Tong & Pratte (2012) also highlight methodological issues encountered in brain imaging research. For example, when analysing fMRI data collected while subjects watched humorous events in a video, it transpired that the ventricles were the most statistically informative brain region in predicting a subjects’ urge to laugh – even though it is highly unlikely that the ventricles themselves play a functional role in the cognitive processing of humorous events (Tong & Pratte, 2012). This error is dubbed the “fallacy of reverse inference” (Poldrack, 2006), and is eloquently demonstrated by neuroscientist James Fallon’s conclusion that he must be a psychopath based on PET images of his own brain (Fallon, 2013). Another problem faced is that brain regions are often associated with multiple mental processes, and regions implicated in particular functions tend to vary with experimental variables such as task demands and the specific baseline condition used to identify them (Rabinovich et al., 2012), and even between individual subjects (Kelly et al., 2012). For instance, the hippocampus is known to be involved in the recall of episodic memories, but has also been implicated in the imagining of future events; however, patients with hippocampal damage in some conditions retain unimpaired imagination, suggesting that the hippocampus may not be necessary for it (Martin et al., 2011). Furthermore, there is the issue of causation versus correlation. For example, a famous MRI study by Maguire et al. (2006) found that London taxi drivers had greater grey matter volume in the mid-posterior (but not anterior) hippocampus compared to London bus drivers, who are not required to learn the colossal amount of information which constitutes “the knowledge”. However, the results do not indicate whether these differences are a direct result of the learning of “the knowledge”, or whether subjects showing this particular neuroanatomy are predisposed to becoming London taxi drivers.

Bennett et al. (2009) highlighted some of the common issues faced when analysing fMRI data (e.g. the multiple comparisons problem) by providing evidence that fMRI scans of a dead salmon apparently showed statistically significant brain activity according to commonly-used statistical tests. Subsequent fMRI studies swiftly adopted the corrected comparison methods proposed (Bennett et al., 2009). Additionally, while identifying functional connectivity has recently been recognised as more important than identifying individual brain regions (Menon, 2011), there are limits to how much fMRI can reveal about connectivity. For example, fMRI studies demonstrate that activation of the prefrontal cortex during cognitive evaluation of threatening facial expressions is associated with an attenuated response of the amygdala, apparently indicating a functional network for emotional regulation (Hariri et al., 2003). However, fMRI data does not necessarily show that the amygdala was inhibited by the prefrontal cortex; this is merely inferred. In fact, such negative correlations are often incorrectly interpreted as “inhibitory interactions” (Kelly et al., 2012). Thus, novel techniques / computational models have been developed attempting to overcome these issues.

One such model is the state-space multivariate dynamical systems (MDS) model (Ryali et al., 2011), which takes into account inherent regional differences in the hemodynamic response and focuses on changes in latent signals rather than BOLD signals – which themselves do not necessarily measure the underlying neural activity. More recently optogenetic fMRI (ofMRI) – which combines optogenetic control of neural circuits with fMRI, enabling more direct investigation of connectivity in vivo (Lee, 2012) – when combined with the MDS model was found to be reliable method for identifying functional interactions between brain regions (Ryali et al., 2016).

Figure 1: ofMRI: optically-driven local excitation in defined rodent neocortical cells drives positive BOLD. a. Experimental schematic: transduced cells (triangles) and blue light delivery shown in M1 motor cortex. b. ChR2-EYFP expression in M1. c. ofMRI hemodynamic response during 6 consecutive epochs of optical stimulation. d. BOLD activation is observed at the site of optical stimulation. Adapted from Lee (2012).

Another advancement in statistical analysis being increasingly employed in fMRI studies is the shift from the identification of regions of interest (ROI) to the “parcellation” of whole brain resting-state fMRI data into spatially coherent regions of homogenous functional connectivity (e.g. by cluster analysis) (Craddock et al., 2012). This type of analysis offers several advantages, including providing a measure of the stability of resting-state functional networks between individual subject data and across grouped subjects data (Bellec et al., 2010). Variations between subjects in these intrinsic functional connectivity based “parcellations” does not appear to be correlated with structural variations; rather, they follow intrinsic variations in functional connectivity evoked by specific tasks (Mennes et al., 2010). Thus, studies are beginning to examine links between these task-evoked variations in resting-state functional connectivity and specific behaviours/mental processes (Kelly et al., 2012; Adelstein et al., 2011). Such novel methods of statistical analysis hold promise in elucidating stable functional networks across individuals, with the aim of explaining cognitive functions at a neurobiological level.

Furthermore, recent advances in MRI imaging techniques allow full brain scans to be completed up to 2-3 times faster. This is a significant advantage since it allows for greater statistical definition of neuronal networks (i.e clearer identification of functionally relevant networks), as well as enhanced visualisations of structural connections in the brain such as white matter tracts (Feinberg & Setsompop, 2013). Similarly, the development of high-field strength MRI scanners is expected to dramatically advance our understanding of the pathology of multiple sclerosis (MS) (Filippi, 2014). Advances in brain imaging techniques and how they are used is also benefitting the study of many other pathological behaviours such as those of autism or schizophrenia. Rather than focusing on pathology of individual brain regions, studies are increasingly focusing on aberrant interactions between specific distributed neural networks as well as more general disturbances in functional connectivity (Menon, 2011). For example, one study found that schizophrenic subjects show less integrated but more diverse functional connectivity during task-based behavioural measures compared to controls, even suggesting a possible advantage of the “schizophrenia connectome” (Lynall et al., 2010). Recent neuroimaging studies of autism spectrum disorder (ASD) have also shifted the focus to identifying abnormal connectivity (Vissers et al., 2012), with some studies reporting “underconnectivity” in frontal regions and others reporting “overconnectivity” – likely as a result of the specific methodological variables/analysis type in each study (Nair et al., 2014). Studies are beginning to examine the development of normal functional connectivity in the brain throughout adolescence and the effect of genes and the environment in the development of abnormal connectivity (Blakemore, 2012). However, while these advances are promising, there remain some limitations; for example, different mental diseases often affect the same resting-state networks, bringing into question the specificity of the findings of neuroimaging studies (Barkhof et al., 2014).

Figure 2: EEG waveform representations to spatial/map representations (and analyses).
From Michel & Murray (2012)

Recent advances in signal analysis have also allowed electroencephalography (EEG) to be used as an efficient brain imaging technique, particularly useful in the investigation of abnormal temporal dynamics in functional networks at the millisecond range. Michel & Murray (2012) argue that the full potential of EEG has been underestimated, proposing that proper analysis of the electric field potentials from each electrode can provide spatio-temporal information which may be useful as an adjunct to fMRI studies. Diffusion tensor imaging (DTI) – a form of MRI which tracks the diffusion of water molecules along white matter tracts – has also been used effectively in a recent connectome analysis study of major depressive disorder (MDD), finding reduced structural connectivity in regions constituting the default mode network as well as the frontal cortex, thalamus and caudate regions, thought to be involved in emotional and cognitive processing (Korgaonkar et al., 2014). However, structural connectivity does not necessarily predict functional connectivity.

With regard to connectome projects, Ohno et al. (2016) review recent advances in the acquisition and analysis of large “connectomic” data sets, while concluding that the combination of different imaging modalities and further advances in the (automated) analysis of data may revolutionise our understanding of the structural and functional connectome, elucidating neural mechanisms underlying not only basic sensory/motor functions and disease but also higher mental processes such as consciousness and intelligence.

Recent brain imaging studies have shown a marked paradigm shift from attempting to localise mental processes/behaviours to individual brain regions to identifying whole-brain structural and functional connectivity patterns which may become aberrant in various neurological/psychiatric disorders. As brain imaging technologies continue to advance, researchers continue to develop novel methods to exploit them to their full potential, allowing unprecedented advances in the neurobiology of specific cognitive functions and behaviours.

References: 

Adelstein, J. S., Shehzad, Z., Mennes, M., DeYoung, C. G., Zuo, X.-N., Kelly, C., Margulies, D. S., Bloomfield, A., Gray, J. R., Castellanos, F. X. and Milham, M. P. (2011) ‘Personality Is Reflected in the Brain’s Intrinsic Functional Architecture’, Valdes-Sosa, M. (ed.), PLoS ONE, 6(11), p. e27633.

Barkhof, F., Haller, S. and Rombouts, S. A. R. B. (2014) ‘Resting-State Functional MR Imaging: A New Window to the Brain’, Radiology, 272(1), pp. 29–49.

Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H. and Evans, A. C. (2010) ‘Multi-level bootstrap analysis of stable clusters in resting-state fMRI’, NeuroImage, 51(3), pp. 1126–1139.

Bennett, C., Miller, M. and Wolford, G. (2009) ‘Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: an argument for multiple comparisons correction’, NeuroImage (Organization for Human Brain Mapping 2009 Annual Meeting), 47, Supplement 1, p. S125.

Blakemore, S.-J. (2012) ‘Imaging brain development: The adolescent brain’, NeuroImage, 61(2), pp. 397–406.

Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P. and Mayberg, H. S. (2012) ‘A whole brain fMRI atlas generated via spatially constrained spectral clustering’, Human brain mapping, 33(8), p. 10.1002/hbm.21333.

Fallon, J. (2013) The Psychopath Inside: A Neuroscientist’s Personal Journey into the Dark Side of the Brain, New York, Penguin Publishing Group.

Feinberg, D. A. and Setsompop, K. (2013) ‘Ultra-fast MRI of the human brain with simultaneous multi-slice imaging’, Frontiers of In Vivo and Materials MRI Research, 229, pp. 90–100.

Filippi, M. (2014) ‘Recent advances in MS neuroimaging’, Multiple Sclerosis and Related Disorders, 3(6), p. 767.

Hariri, A. R., Mattay, V. S., Tessitore, A., Fera, F. and Weinberger, D. R. (2003) ‘Neocortical modulation of the amygdala response to fearful stimuli’, Biological Psychiatry, 53(6), pp. 494–501.

Kamitani, Y. and Tong, F. (2005) ‘Decoding the visual and subjective contents of the human brain’, Nat Neurosci, 8(5), pp. 679–685.

Kelly, C., Biswal, B., Craddock, R. C., Castellanos, F. X. and Milham, M. P. (2012) ‘Characterizing variation in the functional connectome: promise and pitfalls’, Trends in cognitive sciences, 16(3), p. 10.1016/j.tics.2012.02.001.

Knops, A., Thirion, B., Hubbard, E. M., Michel, V. and Dehaene, S. (2009) ‘Recruitment of an Area Involved in Eye Movements During Mental Arithmetic’, Sciencexpress, pp. 1–4.

Korgaonkar, M. S., Fornito, A., Williams, L. M. and Grieve, S. M. (2014) ‘Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis’, Molecular and Neural Systems In Depression, 76(7), pp. 567–574.

Lee, J. H. (2012) ‘Informing brain connectivity with optogenetic functional magnetic resonance imaging’, NeuroImage, 62(4), pp. 2244–2249.

Lynall, M.-E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Müller, U. and Bullmore, E. (2010) ‘Functional connectivity and brain networks in schizophrenia’, The Journal of neuroscience : the official journal of the Society for Neuroscience, 30(28), pp. 9477–9487.

Maguire, E. A., Woollett, K. and Spiers, H. J. (2006) ‘London taxi drivers and bus drivers: A structural MRI and neuropsychological analysis’, Hippocampus, 16(12), pp. 1091–1101.

Martin, V. C., Schacter, D. L., Corballis, M. C. and Addis, D. R. (2011) ‘A role for the hippocampus in encoding simulations of future events’, Proceedings of the National Academy of Sciences of the United States of America, 108(33), pp. 13858–13863.

Mennes, M., Kelly, C., Zuo, X.-N., Di Martino, A., Biswal, B. B., Castellanos, F. X. and Milham, M. P. (2010) ‘Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity’, NeuroImage, 50(4), pp. 1690–1701.

Menon, V. (2011) ‘Large-scale brain networks and psychopathology: a unifying triple network model’, Trends in Cognitive Sciences, 15(10), pp. 483–506.

Michel, C. M. and Murray, M. M. (2012) ‘Towards the utilization of EEG as a brain imaging tool’, NeuroImage, 61(2), pp. 371–385.

Nair, A., Keown, C. L., Datko, M., Shih, P., Keehn, B. and Müller, R.-A. (2014) ‘Impact of methodological variables on functional connectivity findings in autism spectrum disorders’, Human Brain Mapping, 35(8), pp. 4035–4048.

Ohno, N., Katoh, M., Saitoh, Y. and Saitoh, S. (2016) ‘Recent advancement in the challenges to connectomics’, Microscopy, 65(2), pp. 97–107.

Park, H.-J. and Friston, K. (2013) ‘Structural and Functional Brain Networks: From Connections to Cognition’, Science, 342(6158), [online] Available from: http://science.sciencemag.org/content/342/6158/1238411.abstract.

Poldrack, R. A. (2006) ‘Can cognitive processes be inferred from neuroimaging data?’, Trends in Cognitive Sciences, 10(2), pp. 59–63.

Rabinovich, M. I., Friston, K. J. and Varona, P. (2012) Principles of Brain Dynamics: Global State Interactions, Cambridge, Massachusetts, MIT Press.

Reddy, L., Tsuchiya, N. and Serre, T. (2010) ‘Reading the mind’s eye: Decoding category information during mental imagery’, NeuroImage, 50(2), pp. 818–825.

Rissman, J. and Wagner, A. D. (2012) ‘Distributed Representations in Memory: Insights from Functional Brain Imaging’, Annual Review of Psychology, 63, pp. 101–28.

Ryali, S., Shih, Y.-Y. I., Chen, T., Kochalka, J., Albaugh, D., Fang, Z., Supekar, K., Lee, J. H. and Menon, V. (2016) ‘Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions’, NeuroImage, 132, pp. 398–405.

Ryali, S., Supekar, K., Chen, T. and Menon, V. (2011) ‘Multivariate dynamical systems models for estimating causal interactions in fMRI’, NeuroImage, 54(2), pp. 807–823.

Tong, F., Harrison, S. A., Dewey, J. A. and Kamitani, Y. (2012) ‘Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex’, NeuroImage, 63(3), pp. 1212–1222.

Tong, F. and Pratte, M. S. (2012) ‘Decoding Patterns of Human Brain Activity’, Annual Review of Psychology, 63(1), pp. 483–509.

Vissers, M. E., X Cohen, M. and Geurts, H. M. (2012) ‘Brain connectivity and high functioning autism: A promising path of research that needs refined models, methodological convergence, and stronger behavioral links’, Neuroscience & Biobehavioral Reviews, 36(1), pp. 604–625.

How does Ca2+ trigger the release of neurotransmitters?

From gene expression to cell differentiation; from neurotransmitter release to synaptic plasticity – Ca2+ is a ubiquitous ion which plays a vital role in a variety of processes throughout the nervous system, and indeed throughout the majority of biological systems. Here we will focus on just one of the many roles of Ca2+ in the nervous system – the triggering of neurotransmitter/neuropeptide release from vesicles in the pre-synaptic terminal into the synaptic cleft. The role of Ca2+ in neurotransmitter release has been at the forefront of neuroscience research for decades, leading to the accumulation of a substantial amount of scientific literature on the subject.

Voltage-clamp experiments on the giant synapse of the squid found a direct correlation between the amount of neurotransmitter released and the amount of Ca2+ that enters the pre-synaptic terminal, and that neurotransmitter release was inhibited when pre-synaptic Ca2+ channels were blocked (Llinás et al., 1972). Furthermore, subsequent experiments showed that, even in the absence of an action potential, microinjection of Ca2+ into the pre-synaptic terminal is sufficient to trigger neurotransmitter release, and microinjection of Ca2+ chelators prevents neurotransmitter release upon the arrival of an action potential (Hall, 1992). Thus, research began to focus increasingly on the mechanisms by which Ca2+ is able to trigger the release of neurotransmitters.

The first step to triggering release of neurotransmitters is the packaging of small synaptic vesicles (~40-60nm in diameter) with neurotransmitter molecules, which is carried out via active transport (Purves et al., 2001). The vesicles then move towards the active zone – the site of neurotransmitter release – and are docked close to the plasma membrane and primed so that they are ready to respond to Ca2+. An action potential in the pre-synaptic neuron then causes an influx of Ca2+ at the pre-synaptic terminal – due to the opening of voltage-gated Ca2+ channels – and it is this Ca2+ influx which triggers a series of reactions leading ultimately to the fusion of the synaptic vesicles with the plasma membrane and the release of their contents (neurotransmitter molecules) across the membrane and into the synaptic cleft. The neurotransmitter molecules will then diffuse across the synaptic cleft and bind to receptor proteins on the plasma membrane of the post-synaptic neuron, leading ultimately to the generation of an action potential in that cell. The membrane proteins forming the Clathrin-coated vesicle are then endocytosed and may be recycled locally to form new synaptic vesicles – known as the kiss-and-run pathway. Alternatively, the vesicles may remain at the active zone and be reacidified and refilled with neurotransmitter, whilst remaining within the readily releasable pool – known as the kiss-and-stay pathway.

At rest, there is a higher concentration of Ca2+ ions outside the neuron. As an action potential passes down the axon and arrives at the pre-synaptic nerve terminal, voltage-gated Ca2+ channels open, allowing Ca2+ to enter through the channels down its concentration gradient. As Ca2+ ions diffuse into the terminal, they begin to accumulate around the mouth of open Ca2+ channels at a radius spanning tens of nanometres (Simon & Llinás, 1985). The rate at which they reach and bind to Ca2+–binding proteins – the next step in the neurotransmitter release signalling cascade – is of course variable, and depends on the spatial arrangement of Ca2+ channels relative to the Ca2+–binding proteins. The release of fast-acting neurotransmitters such as glutamate or acetylcholine (ACh) was originally thought to be mediated via Ca2+ ‘nanodomains’ – a domain of concentrated Ca2+ ions spanning less than 100 nanometres which interact directly with Ca2+–binding proteins in close proximity – as opposed to Ca2+ ‘microdomains’ – a cluster of Ca2+ channels constituting a domain of Ca2+ ions extending over >100 nanometres and thus interacting with Ca2+–binding proteins within an area spanning around 1μm2. The functional relevance of this is that nanodomain coupling provides greater efficacy and more crucially, significantly greater speeds of synaptic transmission than microdomain coupling (Eggermann et al., 2012). This dogma was proposed in light of evidence that BAPTA (a fast-acting Ca2+ chelator) inhibited neurotransmitter release from the presynaptic terminal with far greater potency than EGTA (a slower-acting Ca2+ chelator), indicating that the Ca2+–binding protein triggering neurotransmitter release binds Ca2+ rapidly and is located very close to Ca2+ channels (Adler et al., 1991). Likewise, it was assumed that the release of slower-acting neurotransmitters is mediated via Ca2+ microdomains.

However, later research on the mammalian Calyx of Held synapse contradicted this, providing strong evidence that microdomain coupling mediates the very rapid release of neurotransmitter at this synapse (Borst & Sakmann, 1996). Comparisons of the number of effective vesicles released in response to postsynaptic currents with the presynaptic Ca2+ current during an action potential indicated that, for each vesicle released, over 60 Ca2+ channels were opened. Furthermore, concentrations of EGTA as low as 1mM significantly inhibited neurotransmitter release – making it almost as potent as BAPTA at this synapse. Therefore, the results concluded that Ca2+ must be diffusing across a greater distance, and that the activation of multiple channels is necessary for rapid neurotransmitter release at this synapse – i.e. the release is mediated via microdomain coupling. Later research corroborated that while vesicles appear to be randomly distributed at the active zone, Ca2+ channels are clustered, with vesicles averaging around 100 nanometres from the Ca2+ domains (Meinrenken et al., 2002). Furthermore, the release probability of a population of vesicles varies during an action potential (Sakaba & Neher, 2001), likely due to the varying concentrations of Ca2+ reaching vesicles across a microdomain. This may in part account for the time-variances observed in neurotransmitter release across different pre-synaptic terminals.

Thus, very rapid exocytosis can be initiated by concentrations of Ca2+ as low as 5-10μM, or as high as 100-200μM (Augustine, 2001). There are clearly marked variations in the concentrations of Ca2+ required by different pre-synaptic terminals to trigger neurotransmitter release. There are two main factors which determine these variations – the first of which is the spatial organisation of Ca2+ channels in the pre-synaptic active zones, as discussed above. The second is the rate at which Ca2+ binds to various Ca2+–binding proteins which modulate neurotransmitter release. Indeed, Meinrenken’s model proposed that the only step in the neurotransmitter release cascade whose speed is dependent on Ca2+ concentration is the binding of Ca2+ to the “calcium sensor” – Synaptotagmin (Meinrenken et al., 2002).

Before discussing Synaptotagmin, perhaps the most crucial Ca2+–binding protein for vesicle fusion / exocytosis of neurotransmitter, it is important to consider other proteins involved earlier in the neurotransmitter release cascade which depend on the binding of Ca2+ to function. During the release process, there are two significant Ca2+ influxes – one which leads to vesicle docking/priming at the active zone, and a second which activates fusion proteins via Ca2+–dependent processes at the active zone, leading to exocytosis of neurotransmitter into the synaptic cleft.

Following the first influx of Ca2+, Synapsin – a protein which binds both to the cytoskeleton and to vesicles, thus preventing vesicles from moving to the active zone before a Ca2+ signal arrives – is phosphorylated by Ca2+/calmodulin-dependent protein kinase II (CaM kinase II) (Ceccaldi et al., 1995), due to a conformational change in CaM kinase II in which an auto-inhibitory sequence is directly relieved by Ca2+/calmodulin. The phosphorylation of Synapsin releases vesicles from the cytoskeleton, allowing them begin their journey towards the active zone. Rab3A/Rab3B (GTP-binding proteins) are thought to then guide vesicles towards the active zone and aid in preparation for docking – a process in which vesicles and pre-synaptic membrane phospholipids arrange into a fusion-ready state (Leenders et al., 2001).

Following the second influx of Ca2+, proteins in the membrane of the vesicle, including Synaptobrevin and Synaptophysins, are thought to be involved in the formation of a ‘fusion pore’ – essentially a cytoplasmic bridge connecting the lumen inside the vesicle with the extracellular synaptic cleft, through which exocytosis will occur – by interacting with proteins in the pre-synaptic terminal plasma membrane such as Syntaxin, SNAP-25 and the putative Physophilins (Woodman, 1997; Thomas & Betz, 1990). Crucially, in the absence of the second Ca2+ influx, no fusion pore is formed.

Synaptotagmin – another protein found on the vesicle membrane – owes its reputation as the ‘calcium sensor’ for neurotransmitter release to the presence of its Ca2+ binding domains which, when bound to Ca2+, allows the protein to bind to phospholipids in the plasma membrane. While not participating directly in the fusion of the vesicle membrane with the plasma membrane, Synaptotagmin may help to overcome the electrostatic repulsion between the two membranes (Domanska et al., 2010), as well as trigger conformational changes in other proteins necessary for fusion. Research found that mutations of Synaptotagmin’s C-terminus Ca2+–binding domain C2A caused a reduction in neurotransmitter release which was directly correlated with the Ca2+–dependent binding of Synaptotagmin to phospholipids of the plasma membrane (Fernández-Chacón et al., 2001), indicating the importance of this step in the release of neurotransmitter. Additionally, mutations of the C2B Ca2+–binding domain in vivo (Drosophila), inhibits neurotransmitter release by disrupting the Ca2+–dependent self-oligomerisation of Synaptotagmins (Fukuda et al., 2000), which is crucial to Synaptotagmins function in triggering vesicle fusion by initiating the assembly of ‘SNARE’ (soluble NSF attachment protein receptor) complexes (Littleton, Bai, et al., 2001) – the next step in the neurotransmitter release cascade. The molecular model in which Synaptotagmin triggers neurotransmitter release in a Ca2+–dependent manner through interactions with the plasma membrane phospholipid bilayer and the initiation of SNARE complex formation is supported by evidence that the speed at which Synaptotagmin carries out these reactions in response to Ca2+ is rapid enough for the kinetic constraints of synaptic vesicle fusion (Davis et al., 1999).

Synaptotagmin is thought to initiate SNARE complex formation by binding to Syntaxin (a.k.a. t-SNARE) (de Wit et al., 2009) – a protein found in the pre-synaptic plasma membrane. Thus, this interaction is also crucial in the targeting of the vesicle membrane to the plasma membrane. Mutations preventing the binding of Syntaxin lead to reduced neurotransmitter release (Wu et al., 1999). The interaction is highly Ca2+–dependent, since it is the binding of Ca2+ to Synaptotagmin which increases its affinity for Syntaxin approximately hundredfold (Chapman et al., 1995). The formation of the SNARE complex also involves N-ethylmaleimide sensitive fusion proteins (NSF) and cytoplasmic SNAPs (soluble NSF attachment proteins) (Morgan & Burgoyne, 2009).

The three main components of the SNARE complex are Syntaxin (t-SNARE), Synaptobrevin (a.k.a. v-SNARE, or vesicle associated membrane proteins (VAMP1/2)) and SNAP-25 (Synaptosomal-associated protein 25). Syntaxin is found on the plasma membrane, along with SNAP-25, while Synaptobrevin is found on the vesicle membrane. Once in proximity, these three proteins each contribute α-helices which progressively wrap around each other to form a ‘coiled-coil’ quaternary structure – SNAP-25 contributes two α-helices, while Syntaxin and Synaptobrevin each contribute one (Chapman et al., 1994). The biomechanical wrapping of these α-helices pulls the two membranes closer together, since Syntaxin is associated with the plasma membrane and Synaptobrevin with the vesicle membrane. This, along with Synaptotagmin’s interactions with the plasma membrane (and undoubtedly numerous other reactions), bring the two membranes close enough so that proteins on the vesicle membrane – including Synaptophysins – may interact with proteins on the plasma membrane – perhaps including Physophilins – and form a fusion pore, through which neurotransmitter can diffuse upon sufficient dilation. The SNARE complex is then disassembled by NSF, an ATPase (Littleton, Barnard, et al., 2001). However, since none of the known SNARE-mediated reactions are directly influenced by Ca2+ (Augustine, 2001), none of this can occur unless Synaptotagmin is first bound to Ca2+. The SNARE reactions essentially rely on this binding – hence Synaptotagmin’s reputation as the ‘calcium sensor’ for neurotransmitter release. Furthermore, the binding of Synaptotagmin to SNAP-25 is essential for Ca2+–dependent neurotransmitter exocytosis (Zhang et al., 2002). A further, interesting role of SNAP-25 is its ability to inhibit the Ca2+ sensitivity of Synaptotagmin in GABAergic neurons (Verderio et al., 2004), functioning to regulate intracellular Ca2+ dynamics and possibly the amount of neurotransmitter released in response to Ca2+.

Finally, a further role of Synaptotagmin is to displace the protein ‘complexin’ from the SNARE complex. Complexin effectively blocks neurotransmitter release by incorporating an α-helix domain into the SNARE complex α-helices, preventing the biomechanical ‘zippering’ and the formation of the ‘coiled-coil’ structure, effectively inhibiting vesicle fusion (Giraudo et al., 2009). Ca2+–bound Synaptotagmin binds to the SNARE complex, causing the inhibitory ‘clamp’ effect of complexin to be relieved (Schaub et al., 2006), allowing proper SNARE complex formation and successful vesicle fusion. Thus, the ‘clamping’ effect of complexins serves to prevent spontaneous vesicle fusion in the absence of Ca2+, allowing greater control of neurotransmitter release. Furthermore, mutations of complexin in Caenorhabditis elegans cause not only a two-fold increase in vesicle fusion in the absence of Ca2+ but also an almost complete loss of effective fusion in response to a Ca2+ influx; thus it was proposed that complexin may be involved in the stabilisation of docked vesicles at the plasma membrane (Hobson et al., 2011). Similar findings were found in experiments with Drosophila complexin-/- mutants, along with indications that complexin may have a further role in regulating the size of both immediate/readily releasable vesicle pools (Jorquera et al., 2012). Additional proteins such as Munc18 bind to SNARE complexes and are crucial for vesicle fusion – with Munc13 removing an inhibitory clamp of Munc18 in the SNARE complex in a similar Ca2+–dependent manner as above (Rizo & Südhof, 2012).

Note that both Synaptotagmin’s interaction with the SNARE complex to relieve the ‘clamping’ effect of complexin and Synaptotagmin’s interaction with the phospholipids of the plasma membrane are Ca2+–dependent. However, different isoforms of Synaptotagmin require varying concentrations of Ca2+ to bind to phospholipids/Syntaxin (Li et al., 1995), suggesting that the use of different Synaptotagmin isoforms in synapses found across different neuronal circuits may play a role in specificity / precise modulation of neurotransmitter release.

Thus, the release of fast acting, small neurotransmitters (e.g. ACh, glutamate, GABA) from small synaptic vesicles (SSVs) is highly dependent on local changes in Ca2+ concentration at Ca2+ channel microdomains, with exocytosis triggered by a cascade of reactions essentially beginning with the phosphorylation of Synapsin by Ca2+–activated CaM kinase II, and the binding of Ca2+ to Synaptotagmin. However, such local changes in Ca2+ concentration are insufficient to cause the release of larger signalling molecules such as neuropeptides, which are carried in large dense-core vesicles (LDCVs) (~90-250nm in diameter) as opposed to SSVs (~35-50nm).

While the exocytosis of neuropeptides from LDCVs has long been known to be Ca2+ dependent (Iversen et al., 1978), the exact mechanisms by which LDCVs fuse with the pre-synaptic plasma membrane and release neuropeptides are unknown. Exocytosis from LDCVs requires greater, sustained Ca2+ currents – first to mobilise the vesicles towards the active zone, then to trigger fusion and exocytosis (Südhof, 2008). The first step is necessary since LDCVs, unlike SSVs, are not found localised around active zones; instead, they are scattered around the pre-synaptic nerve terminal (Salio et al., 2006). However, the precise mechanisms by which LDCVs move to the active zone remain unknown, although some of the mechanisms may be shared with SSVs. Exocytosis of peptides from Chromaffin granules – organelles similar to LDCVs and found in neuroendocrine cells of the adrenal gland (Borges et al., 2010) – has been extensively studied and is known to involve SNARE and Munc18 proteins, similar to SSV exocytosis. A known mechanistic difference is the Synaptotagmin isoforms involved in triggering release. Chromaffin granule exocytosis requires both Synaptotagmin-I – also involved in SSV exocytosis – and Synaptotagmin-VII – not involved in SSV exocytosis (Südhof, 2008). However, it is uncertain whether the Ca2+ sensor for all LDCVs is Synaptotagmin. Furthermore, in addition to Ca2+ influx via voltage-gated Ca2+ channel microdomains, some neuropeptides (e.g. oxytocin) can also be released from dendrites, triggered by the mobilisation of Ca2+ from intracellular stores (Ludwig et al., 2002).

One protein essential for the fusion of LDCVs with the pre-synaptic plasma membrane is ‘Ca2+-dependent activator protein for secretion’ (CAPS). CAPS contains a domain which binds to phospholipids of the plasma membrane, and another domain which is thought to associate with LDCVs – and functions in parallel with SNARE proteins (Grishanin et al., 2002). Since LDCVs are not localised to Ca2+ channel microdomains, and Ca2+ is rapidly buffered as it travels into and throughout the cell, yet the Ca2+ concentration required for LDCV exocytosis is significantly lower than is required for SSV exocytosis (Verhage et al., 1991), the proteins mediating neuropeptide release must have a significantly higher affinity for Ca2+ than those mediating neurotransmitter release from SSVs. Thus, it is thought that neuropeptides are released in response to a large, general increase in Ca2+ concentration throughout the cytoplasm. Such general changes in Ca2+ concentration require high-frequency/burst-patterned firing, in contrast to the single action potentials which can trigger SSV exocytosis. It is thought that the requirement for numerous, successive depolarisations leading to larger accumulations of intracellular Ca2+ is due to the longer latencies observed in the release of neuropeptides (30-2000ms) compared to neurotransmitters (0.3-1ms) (Bergquist & Ludwig, 2009). In any case, the mechanisms underlying the release of neuropeptides from LDCVs remains a key focus of modern neuroscience.

Thus, Ca2+ appears to be the key driver for triggering vesicle mobilisation, docking/priming, fusion and ultimately neurotransmitter release into the synaptic cleft, highlighting its importance in the functioning of neurons throughout the nervous system. While some of the key mechanistic players in the release of neurotransmitters have been identified – Ca2+ channel microdomains, Synapsin, Rab3A/B, Synaptotagmin (the ‘calcium sensor’), SNARE proteins including Synaptobrevin, Syntaxin and SNAP-25, Synaptophysin, Munc18/13 and complexin – the mechanisms underlying the release of neuropeptides are not yet fully understood. Nonetheless, the Ca2+–dependent release of neurotransmitters and neuropeptides from the pre-synaptic terminal remains a rapidly-changing, exciting area of study – and likely will be for years to come.


References: 

Adler, E. M., Augustine, G. J., Duffy, S. N. and Charlton, M. P. (1991) ‘Alien intracellular calcium chelators attenuate neurotransmitter release at the squid giant synapse’, The Journal of Neuroscience, 11(6), pp. 1496–1507.

Augustine, G. J. (2001) ‘How does calcium trigger neurotransmitter release?’, Current Opinion in Neurobiology, 11(3), pp. 320–326.

Bergquist, F. and Ludwig, M. (2009) ‘Neuropeptide Release’, 1st ed. Encyclopedia of Neuroscience, Academic Press.

Borges, R., Pereda, D., Beltrán, B., Prunel, M., Rodríguez, M. and Machado, J. D. (2010) ‘Intravesicular Factors Controlling Exocytosis in Chromaffin Cells’, Cellular and Molecular Neurobiology, 30(8), pp. 1359–1364.

Borst, J. G. G. and Sakmann, B. (1996) ‘Calcium influx and transmitter release in a fast CNS synapse’, Nature, 383, pp. 431–434.

Ceccaldi, P.-E., Grohovaz, F., Benfenati, F., Chieregatti, E. and Greengard, P. (1995) ‘Dephosphorylated Synapsin I Anchors Synaptic Vesicles to Actin Cytoskeleton: An Analysis by Videomicroscopy’, The Journal of Cell Biology, 128(5), pp. 905–912.

Chapman, E. R., An, S., Barton, N. and Jahn, R. (1994) ‘SNAP-25, a t-SNARE which binds to both syntaxin and synaptobrevin via domains that may form coiled coils.’, The Journal of Biological Chemistry, 269, pp. 27427–27432.

Chapman, E. R., Hanson, P. I., An, S. and Jahn, R. (1995) ‘Ca2+ Regulates the Interaction between Synaptotagmin and Syntaxin 1’, The Journal of Biological Chemistry, 270, pp. 23667–23671.

Davis, A. F., Bai, J., Fasshauer, D., Wolowick, M. J., Lewis, J. L. and Chapman, E. R. (1999) ‘Kinetics of Synaptotagmin Responses to Ca2+ and Assembly with the Core SNARE Complex onto Membranes’, Neuron, 24(2), pp. 363–376.

Domanska, M. K., Kiessling, V. and Tamm, L. K. (2010) ‘Docking and Fast Fusion of Synaptobrevin Vesicles Depends on the Lipid Compositions of the Vesicle and the Acceptor SNARE Complex-Containing Target Membrane’, Biophysical Journal, 99(9), pp. 2936–2946.

Eggermann, E., Bucurenciu, I., Goswami, S. P. and Jonas, P. (2012) ‘Nanodomain coupling between Ca2+ channels and sensors of exocytosis at fast mammalian synapses’, Nature Reviews Neuroscience, 13, pp. 7–21.

Fernández-Chacón, R., Königstorfer, A., Gerber, S. H., García, J., Matos, M. F., Stevens, C. F., Brose, N., Rizo, J., Rosenmund, C. and Südhof, T. C. (2001) ‘Synaptotagmin I functions as a calcium regulator of release probability’, Nature, 410, pp. 41–49.

Fukuda, M., Kabayama, H. and Mikoshiba, K. (2000) ‘Drosophila AD3 mutation of synaptotagmin impairs calcium-dependent self-oligomerization activity’, FEBS Letters, 482(3), pp. 269–272.

Giraudo, C. G., Garcia-Diaz, A., Eng, W. S., Chen, Y., Hendrickson, W. A., Melia, T. J. and Rothman, J. E. (2009) ‘Alternative Zippering as an On-Off Switch for SNARE-Mediated Fusion’, Science, 323(5913), pp. 512–516.

Grishanin, R. N., Klenchin, V. A., Loyet, K. M., Kowalchyk, J. A., Ann, K. and Martin, T. F. J. (2002) ‘Membrane Association Domains in Ca2+-dependent Activator Protein for Secretion Mediate Plasma Membrane and Dense-core Vesicle Binding Required for Ca2+-dependent Exocytosis’, The Journal of Biological Chemistry, 277, pp. 22025–22034.

Hall, Z. W. (1992) An Introduction to Molecular Neurobiology, Sunderland, Massachusetts, Sinauer Associates, [online] Available from: http://www.cell.com/cell/comments/0092-8674(93)90154-I (Accessed 29 December 2015).

Hobson, R. J., Liu, Q., Watanabe, S. and Jorgensen, E. M. (2011) ‘Complexin Maintains Vesicles in the Primed State in C. elegans’, Current Biology, 21(2), pp. 106–113.

Iversen, L. L., Iversen, S. D., Bloom, F., Douglas, C., Brown, M. and Vale, W. (1978) ‘Calcium-dependent release of somatostatin and neurotensin from rat brain in vitro’, Nature, 273, pp. 161–163.

Jorquera, R. A., Huntwork-Rodriguez, S., Akbergenova, Y., Cho, R. W. and Littleton, J. T. (2012) ‘Complexin Controls Spontaneous and Evoked Neurotransmitter Release by Regulating the Timing and Properties of Synaptotagmin Activity’, The Journal of Neuroscience, 32(50), pp. 18234–18245.

Leenders, A. G. M., Lopes da Silva, F. H., Ghijsen, W. E. J. M. and Verhage, M. (2001) ‘Rab3A Is Involved in Transport of Synaptic Vesicles to the Active Zone in Mouse Brain Nerve Terminals’, Molecular Biology of the Cell, 12, pp. 3095–3102.

Li, C., Ullrich, B., Zhang, J. Z., Anderson, R. G. W., Brose, N. and Südhof, T. C. (1995) ‘Ca2+-dependent and -independent activities of neural and non-neural synaptotagmins’, Nature, 375, pp. 594–599.

Littleton, J. T., Bai, J., Vyas, B., Desai, R., Baltus, A. E., Garment, M. B., Carlson, S. D., Ganetzky, B. and Chapman, E. R. (2001) ‘Synaptotagmin Mutants Reveal Essential Functions for the C2B Domain in Ca2+-Triggered Fusion and Recycling of Synaptic Vesicles In Vivo’, The Journal of Neuroscience, 21(5), pp. 1421–1433.

Littleton, J. T., Barnard, R. J. O., Titus, S. A., Slind, J., Chapman, E. R. and Ganetzky, B. (2001) ‘SNARE-complex disassembly by NSF follows synaptic-vesicle fusion’, Proceedings of the National Academy of Sciences, 98(21), pp. 12233–12238.

Llinás, R., Blinks, J. R. and Nicholson, C. (1972) ‘Calcium Transient in Presynaptic Terminal of Auid Giant Synapse: Detection with Aequorin’, Science, 176(4039), pp. 1127–1129.

Ludwig, M., Sabatier, N., Bull, P. M., Landgraf, R., Dayanithi, G. and Leng, G. (2002) ‘Intracellular calcium stores regulate activity-dependent neuropeptide release from dendrites’, Nature, 418, pp. 85–89.

Meinrenken, C. J., Borst, J. G. G. and Sakmann, B. (2002) ‘Calcium Secretion Coupling at Calyx of Held Governed by Nonuniform Channel–Vesicle Topography’, The Journal of Neuroscience, 22(5), pp. 1648–1667.

Morgan, A. and Burgoyne, R. D. (2009) ‘NSF and SNAPs’, 1st ed. Encyclopedia of Neuroscience, Academic Press, [online] Available from: http://www.sciencedirect.com/science/article/pii/B9780080450469013711 (Accessed 26 December 2015).

Purves, D., Augustine, G. J., Fitzpatrick, D., Katz, L. C., LaMantia, A.-S., McNamara, J. O. and Williams, S. M. (eds.) (2001) Neuroscience, 2nd ed. Sunderland, MA, Sinauer Associates.

Rizo, J. and Südhof, T. C. (2012) ‘The Membrane Fusion Enigma: SNAREs, Sec1/Munc18 Proteins, and Their Accomplices—Guilty as Charged?’, Annual Reviews, 28, pp. 279–308.

Sakaba, T. and Neher, E. (2001) ‘Quantitative Relationship between Transmitter Release and Calcium Current at the Calyx of Held Synapse’, The Journal of Neuroscience, 21(2), pp. 462–476.

Salio, C., Lossi, L., Ferrini, F. and Merighi, A. (2006) ‘Neuropeptides as synaptic transmitters’, Cell and Tissue Research, 326(2), pp. 583–598.

Schaub, J. R., Lu, X., Doneske, B., Shin, Y.-K. and McNew, J. A. (2006) ‘Hemifusion arrest by complexin is relieved by Ca2+-synaptotagmin I’, Nature Structural & Molecular Biology, 13, pp. 748–750.

Simon, S. M. and Llinás, R. R. (1985) ‘Compartmentalization of the submembrane calcium activity during calcium influx and its significance in transmitter release’, Biophysical Journal, 48(3), pp. 485–498.

Südhof, T. C. (2008) Pharmacology of Neurotransmitter Release, Starke, K. (ed.), Handbook of Experimental Pharmacology, Springer.

Thomas, L. and Betz, H. (1990) ‘Synaptophysin binds to physophilin, a putative synaptic plasma membrane protein’, Journal of Cell Biology, 111, pp. 2041–2052.

Verderio, C., Pozzi, D., Pravettoni, E., Inverardi, F., Schenk, U., Coco, S., Proux-Gillardeaux, V., Galli, T., Rossetto, O., Frassoni, C. and Matteoli, M. (2004) ‘SNAP-25 Modulation of Calcium Dynamics Underlies Differences in GABAergic and Glutamatergic Responsiveness to Depolarization’, Neuron, 41(4), pp. 599–610.

Verhage, M., McMahon, H. T., Ghijsen, W. E. J. M., Boomsma, F., Scholten, G., Wiegant, V. M. and Nicholls, D. G. (1991) ‘Differential release of amino acids, neuropeptides, and catecholamines from isolated nerve terminals’, Neuron, 6(4), pp. 517–524.

de Wit, H., Walter, A. M., Milosevic, I., Gulyás-Kovács, A., Riedel, D., Sørensen, J. B. and Verhage, M. (2009) ‘Synaptotagmin-1 Docks Secretory Vesicles to Syntaxin-1/SNAP-25 Acceptor Complexes’, Cell, 138(5), pp. 935–946.

Woodman, P. G. (1997) ‘The roles of NSF, SNAPs and SNAREs during membrane fusion’, BBA Molecular Cell Research, 1357(2), pp. 155–172.

Wu, M. N., Fergestad, T., Lloyd, T. E., He, Y., Broadie, K. and Bellen, H. J. (1999) ‘Syntaxin 1A Interacts with Multiple Exocytic Proteins to Regulate Neurotransmitter Release In Vivo’, Neuron, 23(3), pp. 593–605.

Zhang, X., Kim-Miller, M. J., Fukuda, M., Kowalchyk, J. A. and Martin, T. F. J. (2002) ‘Ca2+-Dependent Synaptotagmin Binding to SNAP-25 Is Essential for Ca2+-Triggered Exocytosis’, Neuron, 34(4), pp. 599–611.

Did Your Brain Make You Do It?

There seems to be a phenomenon prevalent across much of Western society. People don't like to accept responsibility for their own actions when they've done something wrong. They'd much rather say it that their actions were due to "a complex sequence of chemical reactions within my brain", over which they, supposedly, had no control, and therefore it's not their fault - they're innocent. Of course, the premise is true - we, and all our actions, are the result of various biochemical reactions throughout our bodies. However, that doesn't mean that they're not in our control. That's loosely analogous to saying, "Oops, my car lost control and killed someone - but it was the road conditions; there was nothing I could do." (Yes, it makes no sense)

But what about if our driver had lost control, but instead of killing someone, had collided with another car and changed it's course, when that other car would otherwise have hit a pedestrian? It's unlikely then that the driver would attribute the events to road conditions out of their control. No, they would claim it was their own fast thinking, bravery and heroism that saved the pedestrian's life.

It's nothing new, this phenomenon. It's part of a standard sixth-form psychology course, dubbed 'situational factors' vs 'dispositional factors', or 'self-serving bias'.

However, this likely only applies to Western societies - more collectivist societies would likely attribute their errors to themselves (that is, if they knew that their every move is the result of the workings of the brain). That is because they have a more utilitarian approach; they care more about the good of the society than the consequences they themselves may face. 

My point is, it's a cultural phenomenon, not a neuroscientific one. It's about whether people see themselves as a living being forming a part of a group of living beings for which they are partly responsible; or as an individual biological organism reacting with other individual biological organisms. Of course, both views are true - thus, neither are valid as an argument. You can never say "my brain made me do it" and you can never say "it was my fault, not my brain's", since both are equally true. It just depends how you look at it. 

Pointless argument, really. 

(N.B. I'm referring to adults here, not adolescents whose brains may or may not have fully developed self-control abilities. But this poses a further question - who decides what "fully developed self-control abilities" are? All brains are different, ergo, people have varying "self-control abilities". Do people turn 18 and suddenly reach a baseline level of self-control?)

Common parasite that lives in the bodies of 10 - 20% of Americans linked to a sevenfold higher risk of attempted suicide

http://www.medicalnewstoday.com/articles/249230.php

 

Testing positive for a common parasite that lives in the bodies of 10 - 20% of Americans is linked to a sevenfold higher risk of attempted suicide, according to new research.

I found this article pretty interesting since it furthers the hypothesis that depression is a biological disorder with real, physiological, biochemical roots. It proposes that inflammation and other effects within the brain caused by a common parasite which up to 1 in 5 people host can lead to a drastically increased "risk of attempted suicide".

However, I would like to know exactly what this means. The supposed "risk" is measured on a "suicide assessment scale" - but surely the risk of suicide is something which is subjective to each person. Also, the perception of the "risk of suicide" in the subject could vary greatly depending on the background and/or mental health of the person evaluating the risk.

One must also bear in mind that the sample only included 84 people - 54 attempted suicide patients and 30 controls. All were adults. I'd expect to see a sample of at least a few hundred for a study like this.

Nonetheless, this paves the road for a whole new area of study - the physiological effects of parasites on the brain and the psychological impact of these effects.

Doubts regarding research suggesting that “A Very Sugary Diet Makes You Stupid”

Read the article(s):
http://www.medicalnewstoday.com/articles/245531.php
http://newsroom.ucla.edu/portal/ucla/this-is-your-brain-on-sugar-ucla-233992.aspx

I have some doubts about the conclusions reached in this research.

“As a control, the animals were fed on standard rat feed for five days before the fructose diet started. They were also trained on a maze twice per day and tested to see how well they performed. They also placed visual markers in the maze to help the rats remember their way around.”

 Gomez-Pinilla recounts his experience of testing the rats after six weeks on the sugary diet:

    “The second group of rats navigated the maze much faster than the rats that did not receive omega-3 fatty acids … The DHA-deprived animals were slower, and their brains showed a decline in synaptic activity. Their brain cells had trouble signaling each other, disrupting the rats’ ability to think clearly and recall the route they’d learned six weeks earlier.”

Maybe, rather than omega-3 fatty acids negating a negative effect of fructose on synaptic activity, omega-3 combined with fructose may have enhanced activity and protected from damage to the synapses, leading to the rats’ increased performance in the maze tests.

“Our findings suggest that consuming DHA regularly protects the brain against fructose’s harmful effects …”

The researchers appear to have arrived at the conclusion that fructose (in abundance?) may have negative effects on cognitive activity and memory. I don’t believe that the results of this experiment necessarily point to this conclusion.

Both groups of rats were fed fructose, with the second group also being fed omega-3 fatty acids in the form of flaxseed oil and docosahexaenoic acid (DHA)
There should have been a further control group which was not fed fructose at all, to compare the other two groups against. This would determine whether fructose had any effect on the rat’s brain and performance in the maze tests, prior to investigating any effect that omega-3 fatty acids may have in “negating” this effect. Instead, the researchers gave fructose solutions to both groups of rats.

The UCLA article also suggests that the first group of rats, who did not receive omega-3 fatty acids, may have developed a resistance to insulin:  

"The DHA-deprived rats also developed signs of resistance to insulin, a hormone that controls blood sugar and regulates synaptic function in the brain. A closer look at the rats’ brain tissue suggested that insulin had lost much of its power to influence the brain cells."

"He suspects that fructose is the culprit behind the DHA-deficient rats’ brain dysfunction. Eating too much fructose could block insulin’s ability to regulate how cells use and store sugar for the energy required for processing thoughts and emotions."

I believe that this is the more appropriate route for the experiment to proceed. However, it is unclear whether it is fructose itself that is responsible for the DHA-deprived rat’s lower performance, or an interaction between insulin and fructose in the absence of omega-3 fatty acids.

More research should be done to determine an effect of fructose on the rats’ brain and performance, compared against a baseline, control group of rats who are not fed fructose solutions.

Nonetheless, it is known that omega-3 fatty acids protect the brain and enhance cognitive function and memory. However, it is not correct to conclude from this article that fructose has any negative effect on the brain.

The Future of Neuroscience: Changing The Brain to Enhance…

I thought I’d write a post concerning my disapproval of this article. It seems to be a running theme that I disagree with articles from PsychCentral.

“Changing The Brain to Enhance Well-Being, Happiness” 

http://psychcentral.com/news/2012/04/19/changing-our-brain-to-enhance-well-being-happiness/37566.html

The article basically states what has been long known – that physical exercise, certain forms of psychological counseling (for some people) and meditation can all increase our well-being. That’s all well and good.

Then comes the part I don’t like: 

“The study reflects a major transition in the focus of neuroscience from disease to well-being.”

I think neuroscience is a great, fascinating subject which has a promising outlook for the near future, with beneficial applications such as the treatment of disease and the study of the human brain/mind. However, when we start using neuroscience to improve our “well-being”, we introduce a plethora of potential dangers and moral issues.

The goal is “to use what we know about the brain to fine-tune interventions that will improve well-being, kindness, altruism. Perhaps we can develop more targeted, focused interventions that take advantage of the mechanisms of neuroplasticity to induce specific changes in specific brain circuits.”

Not only is this sort of research reducing the time spent researching treatment for diseases such as Alzheimer’s and Parkinson’s (which is much more important at the current time) it also represents the start of a revolution – the designer-brain revolution. Digressing from the article (although relevant), it won’t be long until we can purchase “add-ons” to enhance our well-being, intellect, kindness, altruism etc. “Add-ons” could also be developed to add “additional-features” to the brain, much like add-ons for Firefox.

People will be purchasing these add-ons to enhance their ability; to gain the upper-hand and improve their lives. However, it can be dangerous to modify nature, especially when it comes to the brain. Since the brain is such a complex organ and is fundamental to our conscious existence, tinkering with it could be dangerous in both the short-term and long-term. Of course there will be years of testing before these add-ons are released, but every brain could react differently, and we might not know the long-term dangers until it’s too late.

More importantly, the moral implications are huge. I can imagine various religious groups objecting to the “designer-brain” revolution on the grounds that it is “playing God”. Although I’m not religious myself, I can see where they’re coming from. We are, in effect, tinkering with thousands of years of evolution. Sure, there are many ways in which the human brain/body can be improved for the better. This, however, is a far beyond therapeutic applications.

For a start, the first to get their hands brains on these “add-ons” will surely be the rich. Instantly, we can see that those in power with modified super-brains could leave us all slaves to the authority. Politics would undoubtedly see a shift to the right. However, once these “add-ons” become more readily available, anyone will be able to buy them. At first, it’ll require surgery to install them; but soon enough, you’ll be able to install them yourself at home. Also, much like add-ons for Firefox, there could be a whole market of 3rd-party add-ons (“Make Me Happy V1.0″, designed by “dodgydesigner666″ on “BrainBay”, for example). Whether illegal or not, a black market of brain add-ons would undoubtedly lead to numerous deaths. Plus, your purchased add-ons could be riddled with viruses which upload your thoughts/personal informations to a crook’s (or government’s) inbox. This might be taking the computer-brain analogy a little too far, but you see my point.

Back to the ethical implications, the “designer-brain” revolution could lead to a break-down of society. People would be purchasing these add-ons to “better” themselves intellectually. This would lead to a social divide between those who can access the add-ons (who would become super-intelligent, with the highest-earning careers) and those who can’t (who, well, wouldn’t). People might also purchase these add-ons to improve their well-being. I’m not sure how to put this, but that just doesn’t seem right. There are reasons we don’t always feel great. Negative emotions can be a positive thing – they can help us to realise errors we may have made, and thus we can begin to work on amending them. With these add-ons, we may not feel the need to amend our mistakes, and they’d be repeated. For example, if a person experiences negative emotions as a result of failure, these emotions will (eventually) give them the motivation to make the change, and work on amending their mistakes and achieving to the best of their abilities. Also, to me, achieving to the best of our ability is something we should have to work for. If one person can purchase an add-on to increase their chances of success, then of course that’s unfair on those who haven’t  purchased the add-ons, whether due to choice or not.

This brings me onto my next point. If people are purchasing these add-ons and becoming super-intelligent, sooner or later people will realise that they need to buy them in order to keep up. It doesn’t become a choice anymore, it becomes an obligation to artificially modify your brain. There comes a time when free-will is out the window. With everyone installing “add-ons” into their brains, who’s to say their designers couldn’t be paid to design the add-ons so that their users can be manipulated, and their personal information shared with crooks/the government? We like to think that these things couldn’t, and wouldn’t, happen – but in reality, of course they can.

As Dieter Birnbacher, a philosopher at the University of Düsseldorf in Germany, says: 

"There are risks in technological self-improvement that could jeopardise human dignity. One potential problem arises from altering what we consider to be “normal”: the dangers are similar to the social pressure to conform to idealised forms of beauty, physique or sporting ability that we see today. People without enhancement could come to see themselves as failures, have lower self-esteem or even be discriminated against by those whose brains have been enhanced”, Birnbacher says.

He stops short of saying that enhancement could “split” the human race, pointing out that society already tolerates huge inequity in access to existing enhancement tools such as books and education.

Everybody will enhance theirself to fit what they believe to be correct – what they believe is best for them and society. However, this would drastically affect relations between different cultures – some cultures will be much more advanced than others, and cultures would be much more separated than they are today. This would not only jeopardise international relations, but also the global economy.

I realise that some of my points may be a little far-fetched, but nonetheless, you can see my point. This is all potentially possible.

The world as we know it is changing. (Can you keep up? Buy the latest add-on to inhibit your anxieties and denial and induce a zombified state of acquiescence)

Window installed into a live brain

For the first time, we can peer through a glass window into a live brain and see the individual neurons up close.

What if we had a glass window into the brain that lets us look inside? For the first time ever, a team of physicists, chemists and biologists has done just that. Led by a microscopy pioneer, they peered into a living mouse's brain using powerful technology.

"You can look into the brain and see a true neuron in action," said physicist Stefan Hell, who leads the Max Planck Institute of Biophysical Chemistry's Department of NanoBiophotonics. His team's achievement is described in the latest issue of the journal Science.

Hell is well-known in the field for inventing a super-resolution "stimulated emission depletion" or STED microscope in the 1990s that can distinguish among features in living samples on a scale so small that general wisdom said it would be impossible.

With that microscope, Hell and his colleagues at Max Planck can discern features down to 70 nanometers in the living brain -- four times beyond what had been the physical limit.

An electron microscope can show powerful levels of detail, but only on dead cells mounted and prepared just so. Recently Hell's team took a live mouse that had been genetically modified so its neurons produce a fluorescent agent. They placed the mouse under anesthesia, opened its skull, and replaced part of the bone with a glass window.

Then, the STED microscope lens was attached to the window so light could be focused on an upper layer of the live mouse's brain. Operating almost like an ultra-precise spotlight, the microscope only illuminated individual neurons carrying the fluorescent marker. All the other cells were dark, letting the neurons shine. (The mouse survived the procedure.)

The resulting images from the live brain have an unprecedented level of clarity, Hell said. Little protrusions with thin necks and a cup-like shape at the end can be seen on the neurons. These "dendritic spines" are the input, the place where a neuron receives signals from a synapse.

Since their technique requires a transgenic animal whose neurons fluoresce, Hell said the plan isn't to make such a window for any human brains. However, there is potential to use this approach for research into treating or even preventing certain neurological diseases.

"I think we can learn a lot about what's going wrong, for example, at a synapse in certain cases," Hell said. "That door is now slammed wide open because one can access a level of functionality, a level of detail that is really critical."

Next, Hell said he and his colleagues want to help neuroscientists use their method to learn about brain functions that are still poorly understood. They would also like scientists to advance research into potential treatments by studying malfunctioning synapses in live animal brains. As physicists, he added, his team will work on producing sharper images.

From Discovery News

Images of a real human brain

This website features real images of a human brain and some of its sections (including the cerebrum, cerebellum and hippocampus), detailed and labelled photos from different angles, as well as MRI scans.

http://www.anatomie-amsterdam.nl/sub_sites/anatomie-zenuwwerking/123_neuro/start.htm