Category Archives: Neuroscience

Cancer, Stress, Genomic Treatments & Steve Jobs

Of the billions of letters making up our life-code a few simple mistakes can cause a cell to stop following the rules, multiply endlessly and destroy it’s host eco-system, you. What can introduce such mistakes in our DNA? The riskiest time being when DNA is copied, a completely molecular-mechanical processes, something that happens billions of times each day in our bodies. It then comes as no surprise that given enough time the likelihood of cancer only increases. However, life is so incredible that it’s managed to develop ingenious ways to spell-check and prevent these mistakes. One of the more rockstar mechanisms are Telomeres, buffer regions at the ends of our chromosomes.

Telomere Caps

Because replication of our chromosomes begins near the center and works itself towards the ends, the two strands of DNA tend not to match up just right at the edges. This is where telomeres reside with their repetitive regions. The idea is it wouldn’t matter if we loose some of them, which is what happens, telomeres shorten over time. Recent research however, has shown strong correlation between the degree of shortening and disease risk, specifically diseases with genomics causes, i.e., cancer. In 2009 the Nobel Prize for Physiology or Medicine was awarded to Elizabeth Blackburn of UCSF for her work in telomeres, including demonstration of the relationship between telomere length, mental stress and cancer.

Genomic Instability and an Increased Incidence of Spontaneous Cancer in Aging mTR−/− Mice

As far as clinical applications of genomics for cancers, two conclusions can be drawn. For prevention, the importance of mental stress in the mechanism of DNA replication. For diagnostics and treatments, the adoption of DNA sequencing both in risk-assessment, i.e., measuring telomere lengths and the use of pharmacogenomics in picking a drug regimen for subjects. Availability of such sequencing technologies is for the moment confined to localities of active genomics industries like San Francisco and the greater Cambridge, Massachusetts region, though often unknown to those in need.
This made me all the more flustered with the failure of clinicians to win Steve Jobs’ battle against cancer. As a case study, he clearly had the resources and lived in an area that is the birthplace of the genomics industry. Jobs was a meticulous individual who was very involved with his work, which leaves us to question his ability to deal with stress and the role that played in his remission & recurrence rates. Another lingering question remains, are the “best” oncologists money can buy, necessarily those who would employ sequencing techniques? Perhaps as time goes on we will learn more about his treatment regiment but for me it shines a bright light in the gap between what researchers see as possible and what clinicians feel comfortable utilizing.

Impartial comparative analysis of measurement of leukocyte telomere length/DNA content by Southern blots and qPCR. Nucleic Acids Res. 2011 Aug 8. Aviv A, Hunt Sc, Lin J, Cao X, Kimura M, Blackburn E.

Longevity, Stress Response, and Cancer in Aging Telomerase-Deficient MiceKarl Lenhard Rudolph1, Sandy Chang, Han-Woong Lee, Maria Blasco, Geoffrey J Gottlieb, Carol Greider, Ronald A DePinho

Leukocyte Telomere Length in Major Depression: Correlations with Chronicity, Inflammation and Oxidative Stress – Preliminary Findings. Wolkowitz OM, Mellon SH, Epel ES, Lin J, Dhabhar FS, et al. 2011 PLoS ONE 6(3): e17837.

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Neurophysiology of Meditation, 2 of 2

These articles have taken steps to identify and understand physiological differences in well-focused minds compared to lay people, it is analogous to a study showing that professional athletes have more muscle mass, in a manner communicating to those of us who seek to perform better at either a physical sport or become better problem solvers, that the mind, like the body must be trained and shaped to overcome difficult challenges. The papers in these two posts converge in that both studies show meditation increases activity and over long-term practice, cause structural changes in regions associated with focus and concentration.

Fig 1 Larger GM volumes in meditators (co-varied for age). Views of the right orbito-frontal cortex, right thalamus, and left inferior temporal gyrus, where GM is larger in meditators compared to controls. The color intensity represents T-statistic values at the voxel level.

Where the last post attempts to capture a snapshot of the mind during a meditative act the paper in the following post attempts to show structural changes caused by long-term, regular meditation. The underlying anatomical correlates of long-term meditation-Larger hippocampal and frontal volumes of gray matter, by Luders, et al., asked a simple question: does regular meditation over many years cause any neuroanatomical changes in the meditator.

Image from National Geographic magazine

To find the answer the authors took 22 meditators with mean meditation experience of 24.18 years and acquired images of their brains using MRI. The images were then passed through Voxel-based GM volume analysis, at a local and global level. Next the images passed through Parcellated volume analysis software, combined the various software analysis would help to distinguish grey matter volume differences between the 22 long-term meditators and 22 control subjects with no meditation experience. As a result, this would to some degree, help the authors identify regions with grey matter (GM) differences, however it is not so clear how those changes can be specifically attributed to meditation alone. The data in figure 1 reveals increased GM differences in areas shown as activated by meditation in previous studies. The authors believe the results of this study provides enough positive data to continue to examine the relationship between meditation and GM volume, they nevertheless do acknowledge that on a global level there was no GM difference, only on a local level.

The future for neurophysiological research of focus and the clarity of thought relies significantly on better imaging technology; we must be able to see what pathways are becoming activated, when and during which thoughts. With increased complexity in our everyday lives, less time and more tasks to complete, being able to focus on the everyday problems and the overarching issues that are inherent with existence will become more relevant, research such as this may help to aid individuals and societies alike.

Luders E, Toga AW, Lepore N, & Gaser C (2009). The underlying anatomical correlates of long-term meditation: larger hippocampal and frontal volumes of gray matter. NeuroImage, 45 (3), 672-8 PMID: 19280691


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Neurophysiology of Meditation, 1 of 2

Fig 1. Expert meditators & non-meditators asked to focus on a dot for extended time. A) 12 expert meditators had greater overlap of increased activation of attention-related brain regions. B) 12 non-meditators had less overlap and activation. Orange hues equal higher correlation between individuals & activation. Blue hues equal little to no correlation between regions of activation.

We have all found ourselves struggling to concentrate on a thought through all the chatter and imagery in our minds. Often, it is a work related problem, other times we try to understand our relationships and throughout our lives we attempt to ponder existence itself. The predicament with focus and the clarity of thought presents itself as an enticing case for study at a neurophysiological level. Meditation as a set of techniques that requires the practitioner to regularly conduct thought exercises or hold steady attention on an internal/external stimuli, can help to identify & understand neural structures implicated in concentration. There are several recent research papers which provide some excellent insight into the contemporary study of how the brain behaves and is ultimately changed through meditation. In Neural correlates of attentional expertise in long-term meditation practitioners by J. A. Brefczynski-Lewis & A. Lutz, et al. the authors scanned the brains of 3 groups as they were asked to concentrate on a dot, using fMRI.  The groups consisted of 16 non-meditators (NM), 11 non-meditators who were given an incentive of $50 if they were able to hold their attention (INM) and 14 expert Buddhist meditators (EM). Furthermore, the EM group was sub-divided into those with most hours of practice, with a mean of 44,000 hours (MHEM) and those meditators with less, mean of 19,000 hours (LHEM);each subgroup containing 4 meditators. Compared to NMs & INMs, EMs were found to have increased activation of attention-related brain regions of interest (ROI), while simultaneously having far less activation of regions unassociated with the task at hand, Fig 1. One of the most interesting results came from the comparison between expert meditator groups MHEMs and LHEMs; whereas LHEMs showed increased activation of ROIs & decreased activation of unassociated regions, MHEMs showed less activation of all brain regions while maintaining the most attention.

Fig 2. Bar graphs for amplitude of activation in the ‘‘early’’ part of the meditation block (the first 10 sec, excluding the first 2 sec because of hemodynamic delay) and the ‘‘late’’ part of the meditation block (120 sec to 200 sec)

What this all means- with a decent amount of practice one can cause greater activation of attention-related regions of the brain, while simultaneously reducing the level of “chatter” and the activation of unrelated brain regions. More interestingly, we see that even amongst expert meditators those with a mean 44,000 hours of meditative practice shows far less activation of all brain regions, including attention related ROIs, compared with meditators who have half as much practice(Fig 2); this demonstrates networks involved in meditation become optimized with increased use, that is it requires less activation, less resources to have the same concentration. Whatever goal one has, having greater focus with more ease will ensure greater success. This paper gives some live data of what is happening in the brain as one performs meditative tasks while showing us that those with extensive practice have a significantly different response, hinting at structural changes. What those changes could possibly be, is discussed in part two of this post.

Brefczynski-Lewis JA, Lutz A, Schaefer HS, Levinson DB, & Davidson RJ (2007). Neural correlates of attentional expertise in long-term meditation practitioners. Proceedings of the National Academy of Sciences of the United States of America, 104 (27), 11483-8 PMID: 17596341

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Practice makes perfect: Dendritic Pruning & LTP

Dendritic trees

It was about two years ago today, when I began to get a clearer picture of how the cells of the brain physically change as a result of our thoughts, experiences and actions. The mind is a result of a network of billions of individual brain cells, neurons, that exchange information with each other; much of the communication between neurons occurs through structures known as dendrites. These tree-like structures emerge from the main body of a neuron, the tips of each branch exchanging information with other neurons. As we develop habits & recurring thoughts, the neurons who communicate the most with each other begin to strengthen the dendritic branches that connect them. Simultaneously, the connections between neurons who don’t often speak with one another begins to atrophy, the dendrites start to prune themselves. These processes together encompass the much larger idea of neuroplasticity, the idea that our brain physically changes and adapts.

Dendritic Pruning

One of the best examples of this concept can be seen when drendritic trees of young mammals are compared with adults, we see extensive branching on the younger brains relative to the adults, supporting the idea that with experience unused connections are pruned off. One proposed mechanism by which the brain is capable of such adaptability is long term potentiation (LTP). The speed of neural communication is largely attributed to the electro-chemical nature of the transmission, allowing for large chunks of information to be rapidly shared across networks of millions of cells every second. LTP is a specific pattern of signaling between neurons where hundreds of bursts of electrical currents of a particular frequency are sent between two neurons; resulting in enhanced communication between the two neurons and strengthening of the dendrites involved, from that point onward. There’s a decent amount of speculation currently, looking to LTP as the mechanism by which our neurons prune their dendritic trees. What this means for you & me: the more we repeat an action or thought, the more the neurons involved in that process communicate with one other, resulting in a streamlining & strengthening of the connections between them; by the same token, routine will cause the number of neurons who can communicate with each other to degrade, possibly limiting what we can learn and understand as we age.

A: Neuron of Child | B: Neuron of Adult

More than ever before, we can observe how our physical minds change as a result of our actions and have a measurable candidate for the mechanism behind this adaptability. If the 20th century belonged to physics, the last several decades to genomics, it may not be a stretch to see the recent future of science be dominated with answering the questions of neuroplasticity and our minds as a physical structure.

Yi Zuo, Guang Yang, Elaine Kwon & Wen-Biao Gan (2005). Long-term sensory deprivation prevents dendritic spine loss in primary somatosensory cortex Nature, 436 : 10.1038/nature0371
Kelly D. Hartle, Matthew S. Jeffers, Tammy L. Ivanc (2010). Changes in dendritic morphology and spine density in motor cortex of the adult rat after stroke during infancy. Synapse, 9999 (9999A) : 10.1002/syn.20767
Daniel McGowan (2006). Pruning processes Nature Reviews Neuroscience : 10.1038/nrn1997


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BOLD fMRI, a clear new view of the brain

Hemoglobin carries the oxygen to our cells, which use it as energy. Our neurons use incredible amounts of energy when they fire electrical currents, which create our thoughts, actions, memories and senses. When a neuron fires, it takes up large amounts of oxygen from nearby hemoglobin molecules. As oxygen leaves it causes a change in the iron-rich structure of hemoglobin, which can be detected by Magnetic Resonance Imaging.

Blood-oxygen-level dependent fMRI allows us to see where in the brain oxygen is being consumed, correlating it with nearby neurons firing. This allows us now to literally map the brain based on activity. Which part of the brain is active during certain thoughts? Memories? Motor actions? BOLD fMRI can and has answered many of these questions. Contemporary studies with this technology has touched the edge of what we once thought possible, from algorithms that can scan our brains to guess what our eyes are seeing; to showing how meditation decreases the number of neurons firing in random areas of the mind.  As we begin to settle into a comfortable pace of understanding and uncovering the functions of the brain in-terms of neurotransmitters and receptors, functional imaging of the mind provides a new horizon of understanding the mind as a complete neural network.

Aguirre, G. (2002). Experimental Design and the Relative Sensitivity of BOLD and Perfusion fMRI NeuroImage, 15 (3), 488-500 DOI: 10.1006/nimg.2001.0990
Kay KN, Naselaris T, Prenger RJ, & Gallant JL (2008). Identifying natural images from human brain activity. Nature, 452 (7185), 352-5 PMID: 18322462

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A Breakdown of a Groundbreaking Article

Finding treatments for Parkinson’s disease helps more than just those afflicted with the illness. The mere act of studying the disorder and looking for a cure has increased mans understanding of the physiological structure of the brain and it’s relation to movement of the body.

This article by Kim et al, was published in Nature, vol 418. These researchers are interested in deriving dopamine neurons from embryonic stem cells (ES cells);   Parkinson’s disease is caused by the loss of neurons that produce dopamine.

Showing ES cells with Nurr1 has positive results for multiple markers of dopamine production

To quantitatively measure how much dopamine these ES cells could produce the researchers stained for tyrosine hydroxylase (TH), which  catalyzes the conversion of L-tyrosine to dihydroxyphenylalanine (DOPA), the precursor for dopamine. Nuclear receptor related-1 (Nurr1) is a transcription factor that has a role in the differentiation of midbrain precursors into dopamine neurons.

In the study ES cell lines expressing Nurr1 are compared to native dopaminergic neurons and WT ES cells. Nurr1 ES cells outperform both comparative cell lines in TH stains, showing greater dopamine production.

Once the authors have demonstrated that their ES cell line with Nurr1 can produce dopamine just as well native dopamine producing neurons, they move on to graft the newly created cell lines to show that they don’t lose their capabilities within an animal model. At the very base, this study demonstrates the ability of embryonic stem cells to be turned into neurons capable of producing specific compounds, just as well native neurons.

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Some thoughts on the mind

What do we currently know about the cellular structure of the brain? How does it affect our concept of reality? A quick & basic summary:3 views of the neuron

The cells of the brain, neurons, have a structure very different from the rest of the body.

At the center of the neuron is the soma, the core. From here branch-like structures called dendrites emerge. The axon, is a single stem which can extend from the soma to very distant spaces, ranging from inches to several feet.

Every thought, feeling, perception, or memory causes an electrical potential to be generated at the soma, passed down the axon and then transferred to other neur ons through dendrites. There are millions of connections between neurons, turning the whole brain into one large network.

This is the stage on which our reality unfolds, everything we learn, everything we feel, all movements and all thoughts occurs through the medium of our neural network.

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