Dedicated to the acceptance, medical treatment, & legal protection of individuals in the process of correcting the misalignment of their anatomical sex, & supporting their transition into society.
functional Magnetic Resonance Imaging (fMRI) uses radio waves and a strong magnetic field rather than X-rays to take clear and detailed pictures of internal organs and tissues.
Scientists use this technology to identify brain regions where blood vessels are expanding, chemical changes are taking place, or extra oxygen is being delivered. These are indications that a particular part of the brain is processing information and giving commands to the body.
As a patient performs a particular task, the metabolism will increase in the brain area responsible for that task, changing the signal in the MRI image. So by performing specific tasks that correspond to different functions, scientists can locate the part of the brain that governs that function.
Symbols & Ideas. Speech and visual communication are parallel and usually interdependent means by which humans exchange information. Vision provides particularly rich information about objects and events in the outside world.
The visual language we use to record these phenomena is adaptable to the needs of communication because of the simplicity of line, shape and colour, Creating an image to communicate an idea presupposes that the qualities of line and shape, proportion and color can convey meaning directly without the use of words.
Ideograms & Icons. An ideogram, or ideograph (Gr. idea "idea" + grapho "to write"), represents an idea within a graphic symbol. It replaces a group of letters arranged according to the rules of an alphabetic language (based on phonemes of a spoken language). People can visualize their thinking and minimize verbalization. The elements in an image represent concepts in a spatial context, rather than the linear form used for words.
However, an ideogram may be too abstract for some cases of everyday use, so icons (a special case of an ideogram) can provide a more nearly representational picture of a subject. Icons can be a convenient graphical shorthand for what would be otherwise lengthy text descriptions.
Icons are very common in online applications ranging from websites to music downloads, as well as more tradional settings such as supermarket and road signage. More recently, icons have found increased use as a short form notation in medical delivery systems.
As simple examples of how icons can be used, those at the top of this sidebar can each represent a single subject. While the assignment of content may be arbitrary and influenced by cultural convention, consistent use can convey meaning.
Syntax & Grammar. The picture of the light switch denotes what it is, but also invokes a procedure for its use. Some degree of preexistent knowledge is necessary to ensure that people who view the icon can understand what it means.
Moreover, the icon stands alone. Another icon that sits beside it may have meaning, but any connections between them are accidental and subject to imposition by a person viewing the two icons. They are not a set. The only context is brought to the viewing by the viewer.
It takes a set of rules and practices, understood by all users, to indicate how one icon can can act upon another one. In this way, a grammatical structure can be applied to images and used to communicate more complex concepts, particularly those that act over time.
Cultural Influences. We could say, using the icons shown above, that we better turn off the lights and lock the door. We could use the first two icons, but it would make more sense if we reversed the order shown above. That is, it would make more sense to an individual accustomed to apprehending text and symbols in a left-to-right manner. The reverse would be true in some other cultures.
Neither practice is inherently "better" in this case, but conflicts will occur if the rules are not fully understood by everyone who might seek meaning in the string of icons.
Another area influenced by the divergence of cultures is in the construction of icons in the first place. The Geneva Convention specified the sole use of a Red Cross as the symbol for humanitarian and medical vehicles and buildings to protect them from military attack.
However, the Muslim nations (primarily the Ottoman Empire and, later, Turkey) objected and preferred the Red Crescent. Persia (now Iran) opted for a red lion and sun. Even though Iran no longer uses the Persian symbol and chose the Red Crescent, it has reserved the right to take it up again at any time. The Israel Red Cross uses a Red Star of David, arguing for parity with the Christian and Muslim symbols, but it is not recognized under international humanitarian law.
In response to all this, the Red Cross movement has deliberated over a new symbol without any religious connontations but easily recognizable on the battlefield. This move may be subject to other objections and, in any case, would require further international agreement.
Harry Benjamin Syndrome (HBS) is a medical condition: the misalignment between an individual’s innate brain sex and physical anatomy.
HBS develops before birth and, for example, results in a person who is neurobiologically female, but exhibits male sexual anatomy. The same is true for neurobiological males with female externals.
HBS is a prior condition for those who have successfully corrected their physical anatomy and brought it into alignment with their brain sex. Following transition, such individuals are no longer HBS.
The designation HBS progressively supersedes the traditional and less precise terminology (e.g., transsexual or transsexuality).
Pittsburgh, PA, USA. Sharing our experiences depends on our ability to visualize and describe the contents of our memories. Prediction of future actions by ourselves and others is founded on our here-and-now processing of images and language. The recognition of words and their combination into new patterns is fundamental to communication. Scientists now have a computer model that reveals how the brain represents meaning and predicts brain activation patterns for thousands of concrete nouns.
The computational model developed by Carnegie Mellon University scientists represents an important step toward understanding how the human brain codes the meanings of words and the unique brain activation patterns associated with names for things that you can see, hear, feel, taste or smell. The findings appear in the journal Science.
Predicting Human Brain Activity Associated with the Meanings of Nouns. Tom M. Mitchell, Svetlana V. Shinkareva, Andrew Carlson, Kai-Min Chang, Vicente L. Malave, Robert A. Mason, and Marcel Adam Just. Science 2008; 320(5880): 1191-1195 doi: 10.1126 / science.1152876
The work could eventually lead to the use of brain scans to identify thoughts and could have applications in the study of memory, brain-related birth conditions, autism, disorders of thought (such as paranoid schizophrenia), and semantic dementias such as Pick's disease.
Carnegie Mellon researchers previously have shown that they can use functional magnetic resonance imaging (fMRI) to detect and locate which areas of the brain are activated when a person thinks about a specific word. Using this data, the researchers developed a computational model to correctly determine what word a research subject was thinking about by analyzing brain scan data. The model predicts these activation patterns for concrete nouns — things that are experienced through the senses — for which fMRI data does not yet exist.
The research team constructed the computational model by using fMRI activation patterns for 60 concrete nouns broken down into 12 categories including animals, body parts, buildings, clothing, insects, vehicles and vegetables.
The model also performed a statistical analysis of a text corpus, or a set of texts that contained more than a trillion words, noting how each noun was used in relation to a set of 25 verbs associated with sensory or motor functions. Combining the brain scan information with the analysis of the text corpus, the computer then predicted the brain activity pattern of thousands of other concrete nouns with accuracies significantly greater than chance.
The computer can effectively predict what each participant's brain activation patterns would look like when each thought about these words, even without having seen the patterns associated with those words in advance.
"We believe we have identified a number of the basic building blocks that the brain uses to represent meaning," said Mitchell, who heads the School of Computer Science's Machine Learning Department. "Coupled with computational methods that capture the meaning of a word by how it is used in text files, these building blocks can be assembled to predict neural activation patterns for any concrete noun. And we have found that these predictions are quite accurate for words where fMRI data is available to test them."
In the study, nine subjects underwent fMRI scans while concentrating on 60 stimulus nouns — five words in each of 12 semantic categories including animals, body parts, buildings, clothing, insects, vehicles and vegetables.
The Carnegie Mellon model predicted fMRI images for celery and airplane that show significant similarities with the observed images for each of those words.
Activity areas:
• Red = high
• Blue = low
Illustration courtesy of Science magazine.
Just, a professor of psychology who directs the Center for Cognitive Brain Imaging, said the computational model provides insight into the nature of human thought. "We are fundamentally perceivers and actors," he said. "So the brain represents the meaning of a concrete noun in areas of the brain associated with how people sense it or manipulate it. The meaning of an apple, for instance, is represented in brain areas responsible for tasting, for smelling, for chewing. An apple is what you do with it. Our work is a small but important step in breaking the brain's code."
In addition to representations in these sensory-motor areas of the brain, the Carnegie Mellon researchers found significant activation in other areas, including frontal areas associated with planning functions and long-term memory. When someone thinks of an apple, for instance, this might trigger memories of the last time the person ate an apple, or initiate thoughts about how to obtain an apple.
"This suggests a theory of meaning based on brain function," Just added.
To construct their computational model, the researchers used machine learning techniques to analyze the nouns in a trillion-word text corpus that reflects typical English word usage. For each noun, they calculated how frequently it co-occurs in the text with each of 25 verbs associated with sensory-motor functions, including see, hear, listen, taste, smell, eat, push, drive and lift. Computational linguists routinely do this statistical analysis as a means of characterizing the use of words.
These 25 verbs appear to be basic building blocks the brain uses for representing meaning, Mitchell said.
By using this statistical information to analyze the fMRI activation patterns gathered for each of the 60 stimulus nouns, they were able to determine how each co-occurrence with one of the 25 verbs affected the activation of each voxel, or 3-D volume element, within the fMRI brain scans.
To predict the fMRI activation pattern for any concrete noun within the text corpus, the computational model determines the noun's co-occurrences within the text with the 25 verbs and builds an activation map based on how those co-occurrences affect each voxel.
Carnegie Mellon researchers predicted the functional magnetic resonance imaging (fMRI) activation pattern for concrete nouns such as “celery” by statistically analyzing each noun’s co-occurrence with 25 verbs such as “eat,” “taste,” and “fill” in a text database.
The predicted brain activity was created by combining the fMRI signatures for each verb, weighted according to the frequency of their co-occurrences with the noun.
Illustration courtesy of Science magazine.
In tests, a separate computational model was trained for each of the nine research subjects using 58 of the 60 stimulus nouns and their associated activation patterns. The model was then used to predict the activation patterns for the remaining two nouns. For the nine participants, the model had a mean accuracy of 77 percent in matching the predicted activation patterns to the ones observed in the participants' brains.
The model proved capable of predicting activation patterns even in semantic areas for which it was untrained. In tests, the model was retrained with words from all but two of the 12 semantic categories from which the 60 words were drawn, and then tested with stimulus nouns from the omitted categories. If the categories of vehicles and vegetables were omitted, for instance, the model would be tested with words such as airplane and celery. In these cases, the mean accuracy of the model's prediction dropped to 70 percent, but was still well above chance (50 percent).
Plans for future work include studying the activation patterns for adjective-noun combinations, prepositional phrases and simple sentences. The team also plans to study how the brain represents abstract nouns and concepts. The work could eventually lead to the use of brain scans to identify thoughts and could have applications in the study of autism, disorders of thought such as paranoid schizophrenia, and semantic dementias such as Pick's disease.
Kenneth Whang, a program officer at the US National Science Foundation (NSF) says the researchers "… started with some fundamental ideas from machine learning about how to get the most out of fMRI data, and now they've not only shown the power of their computational approach, but also made headway on one of the most important problems in the understanding of language in the brain."
Whang believes that Mitchell and Just's research will stimulate further research in the field of computational neuroscience. "This opens up all sorts of new possibilities for looking into the fine structure of how patterns of brain activity relate to human thought processes."
For centuries, the concept of mind readers was strictly the domain of folklore and science fiction. But scientists have edged closer to knowing how specific thoughts activate our brains. The findings demonstrate the power of computational modeling to improve our understanding of how the brain processes information and thoughts.
Predicting Human Brain Activity Associated with the Meanings of Nouns. Tom M. Mitchell, Svetlana V. Shinkareva, Andrew Carlson, Kai-Min Chang, Vicente L. Malave, Robert A. Mason, and Marcel Adam Just. Science 2008; 320(5880): 1191-1195 doi: 10.1126 / science.1152876
Abstract
he question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.
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Crazy Sexy Cancer. This is the trailer for a documentary film by Kris Carr about her cancer adventure. It premiered on TLC and Discovery Health (Fall 2007).
Kris also authored two books, Crazy Sexy Cancer Tips and Crazy Sexy Cancer Survivor: More Rebellion and Fire for Your Healing Journey.
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