Exploring Mysticism and Parapsychology. This blog is also an attempt to promote awareness of a Modern Universal Paradigm known as Multi-Dimensional Science. It offers a "Scientific" testable Hypothesis for a more "objective" understanding of claimed Psychic and Spiritual Phenomena. A link to this subject should be found on this page or alternatively it can be found easily via a word search.Please note that the Internet articles here may not always reflect the views of the Blogger.
Professor Michio Kaku is one of the world’s most thoughtful theoretical physicists. A cofounder of string field theory -- a unique blend of string theory and the advanced-algebra topic called fields -- he has also proved adept at explaining his work to the rest of us, via highly readable science books. His latest work, The Future of the Mind, uses his brand of theoretical physics -- a beguiling mix of quantumness and consciousness -- to push past the current frontiers of neuroscience. Biographile spoke with this bravely unusual mind about where today’s most daring scientific research is taking our brains. Our sit-down adventure included storied science notions like telekinesis, implantable knowledge, memory erasure, and even sentient robots. Biographile: In your introduction you stress that you are a physicist, not a neuroscientist. What are some of the things a physicist adds to the world of neuroscience? Michio Kaku: Within the past two decades, more has been learned about the brain than in all of human history, largely due to advances in physics. An avalanche of brain scanners, such as EEG, fMRI, PET, CAT, DBS, TES, TCM, etc. have, for the first time in history, revealed the intimate details of our thoughts. So physicists have created the instruments that have allowed neuroscientists to probe the thoughts of the living brain. Moreover, advances in physics can blaze entirely new trails for the next generation of neuroscientists to follow. For example, MRI machines were invented by physicists, and hence they know how to make them even more powerful. Physicists can now make MRI machines the size of a briefcase. The smallest MRI possible, using the laws of physics, is the size of a cell phone, which is like the “tricorder” in Star Trek. Pocket-size brain scanners could revolutionize the field. Also, physicists have a different way of viewing the question of “consciousness,” one of the most difficult questions in science. Using a physicist’s perspective, I even give an entirely new definition of consciousness in my book. BIOG: The future you present here is an exciting one, filled with mind-reading, telekinesis, implanted memories, and altered states of consciousness. What do you think are the biggest advantages to such research? MK: The most immediate advantage of these technologies is to relieve human suffering. Already, people who are totally paralyzed, who are living souls trapped inside a vegetable of a body, are now being given the gift of movement. Chips connected to their brains allow them to manipulate mechanical arms, surf the web, write e-mails, play video games, control household appliances. Anything that we can do via a computer, they can do as well. Eventually, this technology will become widely available. We will be able to walk into a room and immediately control all the chips hidden in that room. We will be like magicians, able to control everything around us mentally. We might also be able to control robots with superhuman bodies (like in the movies “Surrogates” and “Avatar”) so that we can live on other planets, explore the heavens, work in dangerous environments, or have powerful exoskeletons like in “Iron Man.” BIOG: What are some of the dangers we have to look out for along the way? MK: All science is a double-edged sword. One edge can cut against ignorance, poverty, and disease, the other side can cut against innocents. One downside to this technology raises the question of privacy. Our thoughts are our most private part of who we are. We don’t want our thoughts to be read by strangers from a distance. However, reading a person’s thoughts over a distance is extremely difficult, since radio and electrical waves dampen extremely fast outside the brain. Even in controlled laboratory situation, sensors must be placed directly on the scalp. Signals quickly become lost in the gibberish of the environment once you leave the scalp. So mind-reading by strangers is unlikely. But there are real problems. To protect our privacy, we must also learn self-control. Eventually, the internet might be replaced by a “brain-net,” in which emotions, memories, and sensations are routinely sent to our Facebook friends. We will have to learn a new set of social skills so that these brain-net messages don’t come back to haunt us. So if we let our thoughts go viral, we must be sure that they don’t have unintended consequences. BIOG: What do you think will be the most exciting brain science breakthrough in the next five years? MK: One of the most exciting developments in the coming years will be a better understanding of mental illness, which is one of the greatest source of human suffering, going back to the dawn of humanity. Using brain scans of schizophrenics, for example, one can actually see areas of the brain light up that are used when we talk to ourselves. However, in schizophrenics, this happens without their permission or knowledge. They are literally talking to themselves, but are convinced the voices come from external sources, such as aliens. Similarly, one can use brain scans to see precisely where the brain goes awry in bipolar disorder, obsessive-compulsive disorder, depression, etc. A cure is still far away, but this technology allows us to see precisely how the brain is malfunctioning. BIOG: In this book you also explore artificial intelligence, with looks at self-aware and ethical robots. It was hard for me not to think of shows like the “Battlestar Galactica” remake. What roles do you see these robots playing in our future? MK: Fifty years ago, scientists made a mistake thinking that the brain was like a digital computer. Yet , the brain has no programming, no Pentium chip, no central processor, no Windows, no operating system, no subroutines, etc. We now realize that the brain is entirely different; it’s a learning machine, a neural network of some sort, that rewires itself after learning every new task. This means that our robots are simple adding machines when compared to the brain. Currently, our most advanced robots have the intelligence of a bug. (But even bugs can rapidly hide, find food, and mates, etc., which our robots cannot.) In the coming decades, robots will be as smart as a mouse, rat, rabbit, dog, or cat, and eventually as smart as a monkey. So they will inevitably play a role in our lives. In fact, robotics may eventually become an industry larger than the automobile industry today, performing the 3 D’s, i.e., jobs that are dirty, dreary, and dangerous. But when robots become commonplace, we will have to bond with them, so they will have to understand our emotions as well. They will have to recognize changes in our face and voice, allowing them to understand our emotional state, and then have a menu of responses to these emotions. Emotions also help us assign a value judgment on everything, which robots do not have. For example, robots will have to know whom and what to save in case of an emergency, and hence will have to make snap-value judgments. In fact, there is a whole branch of artificial intelligence theory, called “friendly AI,” which analyzes the programs necessary to make robots acceptable to humans. BIOG: An emphasis on the role of the mind over that of the body or spirit is bound to make some people uncomfortable. What can you say to reassure them that in this brain-centered future we’ll still be human, only better? MK: The object of the multimillion dollar Human Connectome Project is to map every neural pathway the brain, to have a complete blueprint of every neural connection. This may mean we will eventually have Brain 2.0, i.e. a chip containing every pathway of our brain. In this case, the question is: Can the mind exist independent of the body? Indeed, can the mind become immortal? This a favorite theme in Hollywood (see the latest Superman movie, where Superman’s father does not die on Krypton, but lives on as a conscious holographic computer program). One day, we might have something similar, a computer program that simulates the consciousness of loved ones who have passed away. But does this mean that we are immortal? Probably not. It will probably be clear that the loved one is deceased, and that the program you are talking to is only a very good simulation of that person. We may take comfort in talking to a simulation that is remarkably real, but we will know that it is a simulation. BIOG: One of my favorite subjects -- quantum consciousness -- appears at the end of your book. Could you briefly explain the concept and expound on how it’s tied to theoretical physics? MK: In my book, I give an entirely new definition of consciousness which describes the consciousness of animals and human alike. My theory is testable, reproducible, falsifiable, and even measurable. This definition in particular focuses on the consciousness of animals and humans. However, there is also another type of consciousness, which is sometimes called cosmic consciousness, which goes to the heart of the quantum theory (my specialty). It is so sensitive that even Nobel Laureates today are not in uniform agreement. Basically, the quantum theory (which I teach to our grad students, and which is the most successful physical theory of all time) says that you have make an observation to determine the state of any object (e.g., atoms, electrons, laser beams). Before you observe something, it exists in a never-never-land world, being neither here nor there. (For example, this means that a cat in a closed box is neither dead nor alive in this nether state, before it is observed.) But once you make an observation, you know precisely the state of the cat (e.g., it is alive.) So, in some sense, an observation was necessary for the cat to exist. But observations imply consciousness. Only conscious beings can make an observation. Hence, it seems that consciousness is more fundamental that reality, and that a cosmic consciousness is necessary to observe the universe so that the universe can exist. The greatest minds of science have struggled with this question, without a final resolution. But in my book, I give you a critique of the various bizarre solutions that have been proposed. As J.B.S. Haldane once said, the universe is not only queerer than we suppose, it is queerer than we can suppose. BIOG: If you could be granted telepathy, telekinesis, or control over your dreams, which one would you choose and why? MK: One of the most practical devices would [instead] be one that allows us to upload memories. For the first time in history, this was recently achieved in mice. This means that we might eventually upload entire subjects (such as mathematics and physics) into the mind. Not only would we be able to assimilate entirely new college level subjects, it would allow workers to learn new skills necessary to function in a technological society. Millions of workers, instead of being left behind, would be able to keep up with the latest advances. This could have a profound impact on the economy. One reason why income inequality seems to be increasing is because the economy itself is changing. Entry-level factory work, which was once the conveyor belt that moved unskilled workers into the middle class, is no longer available. The technical skills of workers have not kept up with the rapid advances in science, leaving those workers in the dust. And universities often graduate students to live in the world of 1950. One cure for this problem is increased education, especially technical training. But in the long term, perhaps uploading memories might be the permanent solution to this problem.
White matter tracts within a human brain, as visualized by MRItractography.
A connectome is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram". More broadly, a connectome would include the mapping of all neural connections within an organism's nervous system.
The production and study of connectomes, known as connectomics, may range in scale from a detailed map of the full set of neurons and synapses within part or all of the nervous system of an organism to a macro scale description of the functional and structural connectivity between all cortical areas and subcortical structures. The term "connectome" is used primarily in scientific efforts to capture, map, and understand the organization of neural interactions within the brain.
Research has successfully constructed the full connectome of one animal: the roundwormC. elegans (White et al., 1986,[1] Varshney et al., 2011[2]). Partial connectomes of a mouse retina [3] and mouse primary visual cortex [4] have also been successfully constructed. Bock et al.'s complete 12TB data set is publicly available at Open Connectome Project.
The ultimate goal of connectomics is to map the human brain. This effort is pursued by the Human Connectome Project, sponsored by the National Institutes of Health, whose focus is to build a network map of the human brain in healthy, living adults.
In 2005, Dr. Olaf Sporns at Indiana University and Dr. Patric Hagmann at Lausanne University Hospital independently and simultaneously suggested the term "connectome" to refer to a map of the neural connections within the brain. This term was directly inspired by the ongoing effort to sequence the human genetic code—to build a genome. "Connectomics" (Hagmann, 2005) has been defined as the science concerned with assembling and analyzing connectome data sets.[5]
In their 2005 paper, The Human Connectome, a structural description of the human brain, Sporns et al. wrote:
To understand the functioning of a network, one must know its elements and their interconnections. The purpose of this article is to discuss research strategies aimed at a comprehensive structural description of the network of elements and connections forming the human brain. We propose to call this dataset the human "connectome," and we argue that it is fundamentally important in cognitive neuroscience and neuropsychology. The connectome will significantly increase our understanding of how functional brain states emerge from their underlying structural substrate, and will provide new mechanistic insights into how brain function is affected if this structural substrate is disrupted.[6]
In his 2005 Ph.D. thesis, From diffusion MRI to brain connectomics, Hagmann wrote:
It is clear that, like the genome, which is much more than just a juxtaposition of genes, the set of all neuronal connections in the brain is much more than the sum of their individual components. The genome is an entity it-self, as it is from the subtle gene interaction that [life] emerges. In a similar manner, one could consider the brain connectome, set of all neuronal connections, as one single entity, thus emphasizing the fact that the huge brain neuronal communication capacity and computational power critically relies on this subtle and incredibly complex connectivity architecture.[5]
Pathways through cerebral white matter can be charted by histological dissection and staining, by degeneration methods, and by axonal tracing. Axonal tracing methods form the primary basis for the systematic charting of long-distance pathways into extensive, species-specific anatomical connection matrices between gray matter regions. Landmark studies have included the areas and connections of the visual cortex of the macaque (Felleman and Van Essen, 1991)[7] and the thalamo-cortical system in the feline brain (Scannell et al., 1999).[8] The development of neuroinformatics databases for anatomical connectivity allow for continual updating and refinement of such anatomical connection maps. The online macaque cortex connectivity tool CoCoMac (Kötter, 2004)[9] is a prominent example of such a database.
In the human brain, the significance of the connectome stems from the realization that the structure and function of the human brain are intricately linked, through multiple levels and modes of brain connectivity. There are strong natural constraints on which neurons or neural populations can interact, or how strong or direct their interactions are. Indeed, the foundation of human cognition lies in the pattern of dynamic interactions shaped by the connectome.
However, structure-function relationships in the brain are unlikely to reduce to simple one-to-one mappings. In fact, the connectome can evidently support a great number of variable dynamic states, depending on current sensory inputs, global brain state, learning and development. Some changes in functional state may involve rapid changes of structural connectivity at the synaptic level, as has been elucidated by two-photon imaging experiments showing the rapid appearance and disappearance of dendritic spines (Bonhoeffer and Yuste, 2002).[10]
Despite such complex and variable structure-function mappings, the connectome is an indispensable basis for the mechanistic interpretation of dynamic brain data, from single-cell recordings to functional neuroimaging.
The term "connectome" was more recently popularized by Sebastian Seung's "I am my Connectome" speech given at the 2010 TED conference, which discusses the high-level goals of mapping the human connectome, as well as ongoing efforts to build a three-dimensional neural map of brain tissue at the microscale.[11] In 2012, Seung published the book Connectome: How the Brain's Wiring Makes Us Who We Are.
Brain networks can be defined at different levels of scale, corresponding to levels of spatial resolution in brain imaging (Kötter, 2007, Sporns, 2010).[12][13] These scales can be roughly categorized as microscale, mesoscale and macroscale. Ultimately, it may be possible to join connectomic maps obtained at different scales into a single hierarchical map of the neural organization of a given species that ranges from single neurons to populations of neurons to larger systems like cortical areas. Given the methodological uncertainties involved in inferring connectivity from the primary experimental data, and given that there are likely to be large differences in the connectomes of different individuals, any unified map will likely rely on probabilistic representations of connectivity data (Sporns et al., 2005).[6]
Mapping the connectome at the "microscale" (micrometer resolution) means building a complete map of the neural systems, neuron-by-neuron. The challenge of doing this becomes obvious: the number of neurons comprising the brain easily ranges into the billions in more highly evolved organisms. The human cerebral cortex alone contains on the order of 1010 neurons linked by 1014 synaptic connections.[14] By comparison, the number of base-pairs in a human genome is 3×109. A few of the main challenges of building a human connectome at the microscale today include: (1) data collection would take years given current technology; (2) machine vision tools to annotate the data remain in their infancy, and are inadequate; and (3) neither theory nor algorithms are readily available for the analysis of the resulting brain-graphs. To address the data collection issues, several groups are building high-throughput serial electron microscopes (Kasthuri et al., 2009; Bock et al. 2011). To address the machine-vision and image-processing issues, the Open Connectome Project is alg-sourcing (algorithm outsourcing) this hurdle. Finally, statistical graph theory is an emerging discipline which is developing sophisticated pattern recognition and inference tools to parse these brain-graphs (Goldenberg et al., 2009).
A "mesoscale" connectome corresponds to a spatial resolution of hundreds of micrometers. Rather than attempt to map each individual neuron, a connectome at the mesoscale would attempt to capture anatomically and/or functionally distinct neuronal populations, formed by local circuits (e.g. cortical columns) that link hundreds or thousands of individual neurons. This scale still presents a very ambitious technical challenge at this time and can only be probed on a small scale with invasive techniques or very high field MRI on a local scale.
A connectome at the macroscale (millimeter resolution) attempts to capture large brain systems that can be parcellated into anatomically distinct modules (areas, parcels or nodes), each having a distinct pattern of connectivity. Connectomic databases at the mesoscale and macroscale may be significantly more compact than those at cellular resolution, but they require effective strategies for accurate anatomical or functional parcellation of the neural volume into network nodes (for complexities see, e.g., Wallace et al., 2004).[15]
Mapping the connectome at the cellular level[edit]
Current non-invasive imaging techniques cannot capture the brain's activity on a neuron-by-neuron level. Mapping the connectome at the cellular level in vertebrates currently requires post-mortem microscopic analysis of limited portions of brain tissue. Non-optical techniques that rely on high-throughput DNA sequencing have been proposed recently by Tony Zador (CSHL).[citation needed]
Traditional histological circuit-mapping approaches rely on imaging and include light-microscopic techniques for cell staining, injection of labeling agents for tract tracing, or reconstruction of serially sectioned tissue blocks via electron microscopy (EM). Each of these classical approaches has specific drawbacks when it comes to deployment for connectomics. The staining of single cells, e.g. with the Golgi stain, to trace cellular processes and connectivity suffers from the limited resolution of light-microscopy as well as difficulties in capturing long-range projections. Tract tracing, often described as the "gold standard" of neuroanatomy for detecting long-range pathways across the brain, generally only allows the tracing of fairly large cell populations and single axonal pathways. EM reconstruction was successfully used for the compilation of the C. elegans connectome (White et al., 1986).[1] However, applications to larger tissue blocks of entire nervous systems have traditionally had difficulty with projections that span longer distances.
Recent advances in mapping neural connectivity at the cellular level offer significant new hope for overcoming the limitations of classical techniques and for compiling cellular connectome data sets (Livet et al., 2007; Lichtman et al., 2008).[16][17][18] Using Brainbow, a combinatorial color labeling method based on the stochastic expression of several fluorescent proteins, Lichtman and colleagues were able to mark individual neurons with one of over 100 distinct colors. The labeling of individual neurons with a distinguishable hue then allows the tracing and reconstruction of their cellular structure including long processes within a block of tissue.
In March 2011, the journal Nature published a pair of articles on micro-connectomes: Bock et al.[4] and Briggman et al.[3] In both articles, the authors first characterized the functional properties of a small subset of cells, and then manually traced a subset of the processes emanating from those cells to obtain a partial subgraph. In alignment with the principles of open-science, the authors of Bock et al. (2011) have released their data for public access. The full resolution 12TB dataset from Bock et al. is available at the Open Connectome Project.
In 2012, a Citizen science project called EyeWire began attempting to crowdsource the mapping of the connectome through an interactive game.[19]
Scaling up ultrastructural circuit mapping to the whole mouse brain is currently underway (Mikula, 2012).[20]
An alternative approach to mapping connectivity was recently proposed by Zador and colleagues (Zador et al., 2012).[21] Zador's technique, called BOINC (barcoding of individual neuronal connections) uses high-throughput sequencing to map neural circuits. Briefly, the approach consists of (1) labelling each neuron with a unique DNA barcode; (2) transferring barcodes between synaptically coupled neurons (for example using PRV); and (3) fusion of barcodes to represent a synaptic pair. This approach has the potential to be cheap, fast, and extremely high-throughput.
Established methods of brain research, such as axonal tracing, provided early avenues for building connectome data sets. However, more recent advances in living subjects has been made by the use of non-invasive imaging technologies such as diffusion magnetic resonance imaging and functional magnetic resonance imaging (fMRI). The first, when combined with tractography allows reconstruction of the major fiber bundles in the brain. The second allows the researcher to capture the brain's network activity (either at rest or while performing directed tasks), enabling the identification of structurally and anatomically distinct areas of the brain that are functionally connected.
Notably, the goal of the Human Connectome Project, led by the WU-Minn consortium, is to build a structural and functional map of the healthy human brain at the macro scale, using a combination of multiple imaging technologies and resolutions.
Tractographic reconstruction of neural connections via DTI
Over the past few years, several investigators have attempted to map the large-scale structural architecture of the human cerebral cortex. One attempt exploited cross-correlations in cortical thickness or volume across individuals (He et al., 2007).[22] Such gray-matter thickness correlations have been postulated as indicators for the presence of structural connections. A drawback of the approach is that it provides highly indirect information about cortical connection patterns and requires data from large numbers of individuals to derive a single connection data set across a subject group.
Other investigators have attempted to build whole-brain connection matrices from diffusion imaging data. One group of researchers (Iturria-Medina et al., 2008)[23] has constructed connectome data sets using diffusion tensor imaging (DTI)[24][25] followed by the derivation of average connection probabilities between 70-90 cortical and basal brain gray matter areas. All networks were found to have small-world attributes and "broad-scale" degree distributions. An analysis of betweenness centrality in these networks demonstrated high centrality for the precuneus, the insula, the superior parietal and the superior frontal cortex. Another group (Gong et al. 2008)[26] has applied DTI to map a network of anatomical connections between 78 cortical regions. This study also identified several hub regions in the human brain, including the precuneus and the superior frontal gyrus.
Hagmann et al. (2007)[27] constructed a connection matrix from fiber densities measured between homogeneously distributed and equal-sized regions of interest (ROIs) numbering between 500 and 4000. A quantitative analysis of connection matrices obtained for approximately 1000 ROIs and approximately 50,000 fiber pathways from two subjects demonstrated an exponential (one-scale) degree distribution as well as robust small-world attributes for the network. The data sets were derived from diffusion spectrum imaging (DSI) (Wedeen, 2005),[28] a variant of diffusion-weighted imaging[29][30] that is sensitive to intra-voxel heterogeneities in diffusion directions caused by crossing fiber tracts and thus allows more accurate mapping of axonal trajectories than other diffusion imaging approaches (Wedeen, 2008).[31] The combination of whole-head DSI datasets acquired and processed according to the approach developed by Hagmann et al. (2007)[27] with the graph analysis tools conceived initially for animal tracing studies (Sporns, 2006; Sporns, 2007)[32][33] allow a detailed study of the network structure of human cortical connectivity (Hagmann et al., 2008).[34] The human brain network was characterized using a broad array of network analysis methods including core decomposition, modularity analysis, hub classification and centrality. Hagmann et al. presented evidence for the existence of a structural core of highly and mutually interconnected brain regions, located primarily in posterior medial and parietal cortex. The core comprises portions of the posterior cingulate cortex, the precuneus, the cuneus, the paracentral lobule, the isthmus of the cingulate, the banks of the superior temporal sulcus, and the inferior and superior parietal cortex, all located in both cerebral hemispheres.
More recently, Connectograms have been used to visualize full-brain data by placing cortical areas around a circle, organized by lobe.[35][36] Inner circles then depict cortical metrics on a color scale. White matter fiber connections in DTI data are then drawn between these cortical regions and weighted by FA and strength of the connection. Such graphs have even been used to analyze the damage done to the famous traumatic brain injury patient Phineas Gage.[37]
Primary challenge for macroscale connectomics: determining parcellations of the brain[edit]
The initial explorations in macroscale human connectomics were done using either equally sized regions or anatomical regions with unclear relationship to the underlying functional organization of the brain (e.g. gyral and sulcal-based regions). While much can be learned from these approaches, it is highly desirable to parcellate the brain into functionally distinct parcels: brain regions with distinct architectonics, connectivity, function, and/or topography (Felleman and Van Essen, 1991).[38] Accurate parcellation allows each node in the macroscale connectome to be more informative by associating it with a distinct connectivity pattern and functional profile. Parcellation of localized areas of cortex have been accomplished using diffusion tractography (Beckmann et al. 2009)[39] and functional connectivity (Nelson et al. 2010)[40] to non-invasively measure connectivity patterns and define cortical areas based on distinct connectivity patterns. Such analyses may best be done on a whole brain scale and by integrating non-invasive modalities. Accurate whole brain parcellation may lead to more accurate macroscale connectomes for the normal brain, which can then be compared to disease states.
Mapping functional connectivity to complement anatomical connectivity[edit]
Using functional MRI (fMRI) in the resting state and during tasks, functions of the connectome circuits are being studied.[41] Just as detailed road maps of the earth's surface do not tell us much about the kind of vehicles that travel those roads or what cargo they are hauling, to understand how neural structures result in specific functional behavior such as consciousness, it is necessary to build theories that relate functions to anatomical connectivity.[42]
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Michio Kaku/Photo © Andrea Brizzi
