This document discusses using network and semantic analysis to map disciplinary structures in cognitive neuroscience. It provides examples of contemporary meta-analyses tools like Neurosynth and the Cognitive Atlas that synthesize knowledge in the field using semantic terminology and brain locations. The document outlines applying network analysis techniques like text network analysis to represent relations between anatomy and concept terms found in cognitive neuroscience literature. It describes generating networks from a corpus of cognitive neuroscience articles and analyzing the conceptual, anatomical, and functional network structures that emerge. Limitations and future directions are also discussed.
EEG Based Classification of Emotions with CNN and RNNijtsrd
Emotions are biological states associated with the nervous system, especially the brain brought on by neurophysiological changes. They variously cognate with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure and it exists everywhere in daily life. It is a significant research topic in the development of artificial intelligence to evaluate human behaviour that are primarily based on emotions. In this paper, Deep Learning Classifiers will be applied to SJTU Emotion EEG Dataset SEED to classify human emotions from EEG using Python. Then the accuracy of respective classifiers that is, the performance of emotion classification using Convolutional Neural Network CNN and Recurrent Neural Networks are compared. The experimental results show that RNN is better than CNN in solving sequence prediction problems. S. Harshitha | Mrs. A. Selvarani "EEG Based Classification of Emotions with CNN and RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30374.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30374/eeg-based-classification-of-emotions-with-cnn-and-rnn/s-harshitha
EEG Based Classification of Emotions with CNN and RNNijtsrd
Emotions are biological states associated with the nervous system, especially the brain brought on by neurophysiological changes. They variously cognate with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure and it exists everywhere in daily life. It is a significant research topic in the development of artificial intelligence to evaluate human behaviour that are primarily based on emotions. In this paper, Deep Learning Classifiers will be applied to SJTU Emotion EEG Dataset SEED to classify human emotions from EEG using Python. Then the accuracy of respective classifiers that is, the performance of emotion classification using Convolutional Neural Network CNN and Recurrent Neural Networks are compared. The experimental results show that RNN is better than CNN in solving sequence prediction problems. S. Harshitha | Mrs. A. Selvarani "EEG Based Classification of Emotions with CNN and RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30374.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30374/eeg-based-classification-of-emotions-with-cnn-and-rnn/s-harshitha
Toward Tractable AGI: Challenges for System Identification in Neural CircuitryRandal Koene
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon.
Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
Naturalized Epistemology North American Computing and Philosophy 2007 Gordana Dodig-Crnkovic
Gordana Dodig-Crnkovic
Knowledge Generation as Natural Computation,
Journal of Systemics, Cybernetics and Informatics, Vol 6, No 2, 2008
http://www.iiisci.org/Journal/CV$/sci/pdfs/G774PI.pdf
Use Your Mind to Change Your Brain: Tools for Cultivating Happiness, Love an...Rick Hanson
Tools for well-being, grounded in cutting-edge science and the wisdom of the world’s contemplative traditions.
More resources, freely offered at http://www.rickhanson.net
Toward Tractable AGI: Challenges for System Identification in Neural CircuitryRandal Koene
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon.
Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
Naturalized Epistemology North American Computing and Philosophy 2007 Gordana Dodig-Crnkovic
Gordana Dodig-Crnkovic
Knowledge Generation as Natural Computation,
Journal of Systemics, Cybernetics and Informatics, Vol 6, No 2, 2008
http://www.iiisci.org/Journal/CV$/sci/pdfs/G774PI.pdf
Use Your Mind to Change Your Brain: Tools for Cultivating Happiness, Love an...Rick Hanson
Tools for well-being, grounded in cutting-edge science and the wisdom of the world’s contemplative traditions.
More resources, freely offered at http://www.rickhanson.net
The cognitive advantage insights from early adopters on driving business va...Diego Alberto Tamayo
The cognitive advantage
Cognitive computing is quickly emerging as a transformative technology that enables
organizations to gain business advantage. Also referred to as artificial intelligence (AI), cognitive
technology augments human expertise to unlock new intelligence from vast quantities of data and
to develop deep, predictive insights at scale. This shift to systems that can reason and learn is
especially germane to the bottom line; the cognitive era is here in large part because it makes
practical business sense.
Jeff Hawkins NAISys 2020: How the Brain Uses Reference Frames, Why AI Needs t...Numenta
Jeff Hawkins presents a talk on "How the Brain Uses Reference Frames to Model the World, Why AI Needs to do the Same." In this talk, he gives an overview of The Thousand Brains Theory and discusses how machine intelligence can benefit from working on the same principles as the neocortex.
This talk was first presented at the NAISys conference on November 10, 2020. You can find a re-recording of the talk here: https://youtu.be/mGSG7I9VKDU
Apical-amplification, apical-isolation, apical-drive. two-compartment spiking model. ThetaPlanes piecewise linear approximation of mutlicompartment neuron activity. Sleep passed the evolutionary siege in all studied animal species, notwithstanding its apparent unproductivity (lower reactivity to external dangers, no feeding, no mating). In humans, the time spent in sleep is higher in younger individuals, precisely when learning is faster. Another element to be considered is that, thanks to an evolutionary history that spanned hundreds of millions of years and selected among countless individuals, the inter-areal and local connectome captures the priors necessary to optimize the flow and combination of internal hypotheses and sensorial evidence.
At the cellular level, optimal combination of contextual information and local computation is provided by the apical amplification principle, active during wakefulness. Deep-sleep (NREM) and REM sleep are characterized in mammals by pyramidal neurons changing to a different management of apical signals, namely apical-isolation and apical-drive.
The cognitive and energetic functions of sleep and its relations with awake performance have beeninvestigated by INFN in spiking models, engaged in learning and sleep cycles, that will be presented in this seminar. Also, preliminar information about a next generation of neural models supporting apical mechanisms will be presented.
Fundamentals of Human Neuropsychology 7th Edition Kolb Test BankPerkinser
Full download : http://alibabadownload.com/product/fundamentals-of-human-neuropsychology-7th-edition-kolb-test-bank/ Fundamentals of Human Neuropsychology 7th Edition Kolb Test Bank
AI&BigData Lab 2016. Дмитрий Новицкий: cпайковые и бионические нейронные сети...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
В мире машинного обучения многие годы доминируют нейронные сети прямого распространения (feed-forward), которые почти ничего общего не имеют с нейронами и сетями нашего мозга. В этом докладе мы познакомимся с бионическими (biologically plausible) нейронными сетями. В большинстве из них нейроны испускают и принимают импульсы (спайки). Какие возникают проблемы и сложности обучения таких сетей? В каких традиционно нерешаемых (или плохо решаемых) задачах они могут быть эффективны, как эффективен в них мозг человека и животных? Как можно реализовать такие сети аппаратно, и что такое нейроморфный компьютинг? -- Вот вопросы, которым посвящена данная презентация.
Functional specificity in the human brain A windowinto the DustiBuckner14
Functional specificity in the human brain: A window
into the functional architecture of the mind
Nancy Kanwisher1
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139
This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2005.
Contributed by Nancy Kanwisher, April 16, 2010 (sent for review February 22, 2010)
Is the human mind/brain composed of a set of highly specialized
components, each carrying out a specific aspect of human cognition,
or is it more of a general-purpose device, in which each component
participates in a wide variety of cognitive processes? For nearly two
centuries, proponents of specialized organs or modules of the
mind and brain—from the phrenologists to Broca to Chomsky and
Fodor—have jousted with the proponents of distributed cognitive
and neural processing—from Flourens to Lashley to McClelland and
Rumelhart. I argue here that research using functional MRI is begin-
ning to answer this long-standing question with new clarity and
precision by indicating that at least a few specific aspects of cogni-
tion are implemented in brain regions that are highly specialized for
that process alone. Cortical regions have been identified that are
specialized not only for basic sensory and motor processes but also
for the high-level perceptual analysis of faces, places, bodies, visu-
ally presented words, and even for the very abstract cognitive func-
tion of thinking about another person’s thoughts. I further consider
the as-yet unanswered questions of how much of the mind and
brain are made up of these functionally specialized components
and how they arise developmentally.
brain imaging | modularity | functional MRI | fusiform face area
Understanding the nature of the human mind is arguably thegreatest intellectual quest of all time. It is also one of the most
challenging, requiring the combined insights not only of psychol-
ogists, computer scientists, and neuroscientists but of thinkers in
nearly every intellectual pursuit, from biology and mathematics to
art and anthropology. Here, I discuss one currently fruitful com-
ponent of this grand enterprise: the effort to infer the architecture
of the human mind from the functional organization of the
human brain.
The idea that the human mind/brain is made up of highly spe-
cialized components began with the Viennese physician Franz
Joseph Gall (1758–1828). Gall proposed that the brain is the seat
of the mind, that the mind is composed of distinct mental faculties,
and that each mental faculty resides in a specific brain organ. A
heated debate on localization of function in the brain raged over
the next century (SI Text), with many of the major figures in the
history of neuroscience weighing in (Broca, Brodmann, and Fer-
rier in favor, and Flourens, Golgi, and Lashley opposed). By the
early 20th century, a consensus emerged that at least basic sensory
and motor fu ...
Presented during the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'12). Part of the workshop 'New Models and Modes for Data Sharing: Experiences from Neuroscience'. Presented by Jeffrey S. Grethe, Ph.D. from the Center for Research in Biological Systems at the University of California, San Diego.
This workshop featured several large scale efforts to establish data sharing platforms, standards and tools to promote data intensive analysis in the neurosciences. As we head into the second decade of the 21st century, many scientists realize that current methods for publishing and accessing data are outmoded and inefficient. Neuroscience, with its large diverse and highly competitive community, has been slow to adopt more open sharing of data and has lacked effective tools to do so. There has been a significant investment in databases and tools for biological science, and frequent calls for more of them, but few calls to the biological community to adopt practices and frameworks for making their resources more easily discoverable and data more accessible. Data are contained within diverse sources, from web pages, databases, literature to personal lab systems, making for a haphazard mechanism for data and tool discovery. Although these mechanisms are effective for small communities, they are parochial for the totality of resources available, leading to fragmentation in the resource ecosystem. Neuroscience, with its diverse subdisciplines, complex data types and broad domain, presents the perfect exemplar of the current practices, bottlenecks and issues surrounding open access to data. This situation is changing, however, as groups have started to work together to define new models and tools for sharing and analyzing neuroscience data on an international scale. In this workshop, we bring together experts from national and international projects to discuss issues of data access and progress towards establishing platforms and best practices for effective sharing of neuroscience data in support of basic and clinical neuroscience.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
1. Mapping Disciplinary Structures
Using Network & Semantic Analysis
Greg Appelbaum
&
Elizabeth Beam
Duke University Library
Digital Scholarship Series
November 1, 2012
3. Cognitive Neuroscience Timeline
“Cognitive Revolution”
1950s - Broadbent, Chomsky, Miller…
PET TMS
EEG is developed
Reivich (1979) Barker et al (1985)
(Berger 1929)
1925 1950 1975 2000
Action potentials discovered MRI developed
Hodgkin & Huxley (1938) Lauterbur (1973)
fMRI
BOLD response first measured
Ogawa et al (1990)
Gazzaniga and Miller coined the name
“cognitive neuroscience” over martinis
at the Rockefeller Faculty Club (1976)
Adapted from: The Student's Guide to Cognitive Neuroscience by Jamie Ward, 2006
4. Information Curve
Number of studies with fMRI of Published entries by indexing
functional MRI in their title/abstract service per year
Blowup shows new influx of sources (Wikipedia)
Literature review is now somewhat of an intractable problem…
There is a profound need for tools that can synthesize.
7. Scientometrics
• The science of measuring and analyzing science.
• Is a robust field, that incorporates numerous statistical and mathematical
methods to reveal quantitative features of science.
• The lion’s share of these involve citation-based methods and clustering
algorithms to arrive at ‘knowledge-bearing units’
8. Scientometrics
2007 ”UCSD Map of Science”, Boyack and Klavans
• The largest maps of science to date (ISI and Scopus databases).
• 7.2 million papers published in more than 16,000 journals between 2001-2005.
9. Content-Based Mapping
• As a result of the informatics revolution (particularly digital archiving) it is possible
to scrutinize the content of science on large scales with relative ease.
• In turn new meta-analytic techniques can provide powerful tools for the critical
scrutiny of what is known…. Metaknowledge!
Evans and Foster (2011) Metaknowledge, Science.
10. Network Analysis
• A network consists of nodes connected by links.
• Formal network analysis was developed by sociologists, who have
studied links between friends, terrorist cells, and disease carriers.
• You may be familiar with other popular applications of network concepts.
11. Why Apply Network Analysis
to Cognitive Neuroscience
• Cognitive neuroscience aims to understand relations between brain
anatomy and behavioral function.
• These relations are linked through experimental findings to create a
network consisting of the core empirical support of the discipline.
• The rhetoric used to express these relations should be a suitable target
for meta-analysis.
12. N e t w o r k Te x t A n a l y s i s
In a source memory study, we used two novel approaches to data
analysis that allowed item memory strength and source memory
strength to be assessed independently. First, we identified regions in both
hippocampus and perirhinal_cortex in which activity varied as a
function of subsequent item memory strength while source memory
strength was held constant at chance levels. Second, we identified regions
in prefrontal_cortex in which activity varied as a function of
subsequent source memory strength while item memory strength
was held constant. These findings suggest that activity in the
medial_temporal_lobe is predictive of subsequent memory
strength, whereas activity in prefrontal_cortex is predictive of
subsequent recollection.
• Network text analysis takes words as nodes.
• Two nodes are linked if they co-occur in the same window of
adjacent words in the text.
• To represent the field of cognitive neuroscience, we visualized
links between anatomy and concepts terms.
13. The Corpus
• Source:
• Nature Neuroscience, Neuron, Neuroimage, Journal of Neuroscience, Journal
of Cognitive Neuroscience
• January 1, 2008 through June 30, 2010
• 7,675 articles
• Criteria for inclusion:
• Use of functional magnetic resonance imaging (fMRI) for primary data collection.
• Stated goals of understanding links between the human brain and some function.
• A report of empirical data collected for the current article.
• Final Corpus: Abstracts and Titles from 1,127 fMRI studies
• Versus: EEG (346), PET (120), and TMS (109)
14. Term Classification
• Two semantic categories were defined for Anatomy and
Concept terms.
– Anatomy terms referred to one of the following:
1. A brain structure (e.g., “hippocampus”)
2. A functionally defined region (e.g., “fusiform face area”)
– Concept terms belonged to one of the following categories:
1. A domain of cognitive neuroscience (e.g., “memory”)
2. A process within a domain (e.g., “working memory”)
3. A stimulus property (e.g., “face” or “risk”)
15. Text Preprocessing
• Normalized for grammatical variants of terms
• Because standard thesauri do not cover neuroanatomical
terms, nor the jargon of cognitive neuroscience, we authored
custom thesauri.
– BIGRAM THESAURUS:
• prefrontal cortex prefrontal_cortex
– GENERALIZATION THESAURUS:
• pfc, prefrontal, prefrontal cortices prefrontal_cortex
16. T h e To p Te r m s
Frequency Frequency
1. Vision 637 1. PFC 356
2. Memory 556 2. Amygdala 329
3. Behavior 497 3. ACC 272
4. Information 490 4. Hippocampus 269
5. Attention 488 5. Parietal Cortex 227
6. Representation 450 6. Visual Cortex 177
7. Control 449 7. Intraparietal Sulcus 152
8. Object 442 8. mPFC 138
9. Perception 439 9. Insula 131
10. Cognition 434 10. Cerebellum 129
17. Network Generation
6 word sliding window
These results support
the hypothesis that
specific subregions in
the MTL are associated
with item memory and
memory for context.
~50 highest weighted links
18. 3 Networks
Conceptual Structure:
-- concept x concept
Anatomical Structure:
-- anatomy x anatomy
Functional Structure:
-- (anatomy x concept + concept x anatomy)
21. Centrality Measures
• DEGREE: The number of direct connections that a node has.
– Nodes with high degree are “connectors” or “hubs.”
– is highly correlated (r~.8) with frequency
• BETWEENNESS: It is equal to the number of shortest paths
from all vertices to all other paths that pass through that node.
– Nodes with high betweenness are “brokers.”
22. Conceptual Structure
Betweenness vs. Frequency
0.06
Emotion
Cognition
Selection Learning
0.05
Control
Betweenness Centrality
Recognition Spatial
0.04 Word
Movement
0.03
Inhibition
Representation
Priming
Encoding Prediction
Sensory Action
Auditory Face Observation Vision
0.02 Error Memory
Suppression Perception
Working Memory
Reward
Verbal Category Speech Attention
0.01 Semantic
Load Motion Retrieval Motor Object
Social
Executive
Future Risk
0
0 100 200 300 400 500 600
Frequency
28. Structural Synonymity
Conceptual Network Anatomical Network
• Second-level Positional Analyses
– Second-order projections link terms that occupy similar positions in
the network and therefore represent semantic synonyms.
– Computed as the correlation coefficient between each row in the
adjacency matrix of link weights
29. Observations
• Positive structure. Whereas concepts terms organize
around hubs for perception/attention, representation, and
control, a few highly central anatomy terms lead into
branches representing processing streams.
• Negative structure. Islands appear as collections of
isolated terms on the networks, while gaps are revealed
by network measures as terms with high betweenness
centrality relative to frequency.
30. Limitations #1
• A not B problem
– The co-occurrence of two terms in text could reflect a positive association, a negative
association, or even the speculation about an unknown association.
– Without further (difficult) coding, we cannot resolve this problem.
– See however, typical Google search queries…
• Curration
– How to move this to an autonomous process?
32. Future Directions
• Larger longitudinal corpus to
map relationships over time.
• Extend these tool beyond
cognitive neuroscience.
• Develop web-based tools
…that interface with other existing
meta-analysis tools.
Contour density maps show a birds eye
view of the landscape.
33. Conclusions
• Using this approach we are able to endogenously map the
knowledge space of cognitive neuroscience.
• We are able to identify terms that are understudied
compared to their importance.
• These results can provide prescriptive recommendations
for topics whose further study will most efficiently build new
links between structure and function.
36. An Integrated Approach to the Collection and Analysis of Network Data*
Kathleen M. Carley, Jana Diesner, Jeffrey Reminga, Maksim Tsvetovat
37. Cognitive Neuroscience:
Journal Citation and Topic Maps
• All articles from authors who contributed to the Summer Institute Cognitive
Neuroscience. (5 year intervals)
Journal Citation Map
1988 2007
CNS Topic Maps
Bruer (2004) Mapping Cognitive Neuroscience: Two‐dimensional perspectives on twenty years of cognitive neuroscience research
38. Prospective Trace of a Sub-Discipline (Bruer, 2010)
• The author set consists of 28
authors highly active in attentional
research in the mid-1980s.
• “By 1990 a distinct cognitive
neuroscience specialty cluster
emerges, dominated by authors
engaged in brain imaging research”
Bruer, J.T. (2010) Can we talk? How the cognitive neuroscience of attention emerged from
neurobiology and psychology, 1980–2005. Scientometrics.
39. Neuroeconomics
the neural basis of decision making
Levallois et al (under review) Translating Upwards: Linking the Neural and Social
Sciences via Neuroeconomics,
41. Scientometric Interpretations of
Network Structure
Future Directions
① Intrinsic structure
– Connectivity and position of nodes
– Clusters within the network
② Global structure
– Network density and topology
– Changes in networks over time
The local neighborhood of our 5
Contour density maps show a birds eye
journals (around here)
view of the landscape.
42. Scientometrics
2002 Map of Science: Boyack and Klavans
Bibliographic Coupling of SCI & SSCI to arrive at „Neighborhoods‟ and „Disciplines‟.
– 730,000 papers, 7,300 journals, 671 “disciplines”
43. References
Carley, K. M. and Reminga, J. (2004) ORA: Organization Risk Analyzer. CASOS Technical
Report CMU-ISRI-04-106, 1-45.
Carley, K. M. (2006) A dynamic network approach to the assessment of terrorist
groups and the impact of alternative courses of action. Visualizing Network
Information KN1, 1-10.
Moody, J. and Light, R. (2006) A view from above: the evolving sociological landscape.
The American Sociologist 37.2, 67-86.
Moody, J. (2011) Introduction to Social Network Analysis. Social Science Research
Institute Workshop.
Moreno, J. L. (1953) Who shall survive? New York: Beacon House.
Newman, M. E. J. (2004) Co-authorship networks and patterns of collaboration.
Proceedings of the National Academy of Sciences 101.s1, 5200-5205.
I show people stuff, record their brain activity, and try to figure out how the stuff in between works…The techniques measure changes in electrical voltage potentials or regional blood oxygenation when the brain is subjected to various stimuli
And things have really taken off at this point for CNS… this plot just goes to 2007 but the growth is exponentialWhat is more, there is evidence that historically fMRI studies are cited ~3 times as often as other CNS methods (Fellows et al JOCN 2005)These information curves are reflective of the larger literature.. Need meta analysis tools
http://neurosynth.org/Activation coordinates are extracted from published neuroimaging articles using an automated parser. The full text of all articles is parsed, and each article is 'tagged' with a set of terms that occur at a high frequency in that article. A list of several thousand terms that occur at high frequency in 20 or more studies is generated. For each term of interest (e.g., 'emotion', 'language', etc.), the entire database of coordinates is divided into two sets: those that occur in articles containing the term, and those that don't. A giant meta-analysis is performed comparing the coordinates reported for studies with and without the term of interest. In addition to producing statistical inference maps (i.e., z and p value maps), we also compute posterior probability maps, which display the likelihood of a given term being used in a study if activation is observed at a particular voxel.Cognitiveatlas.orgThe Cognitive Atlas is a collaborative knowledge building project that aims to develop a knowledge base (or ontology) that characterizes the state of current thought in cognitive science. The project is led by Russell Poldrack, Professor of Psychology and Neurobiology at the University of Texas at Austin in collaboration with the UCLA Center for Computational Biology (A. Toga, PI) and UCLA Consortium for Neuropsychiatric Phenomics (R. Bilder, PI). It is supported by grant RO1MH082795 from the National Institute of Mental Health.
http://neurosynth.org/Activation coordinates are extracted from published neuroimaging articles using an automated parser. The full text of all articles is parsed, and each article is 'tagged' with a set of terms that occur at a high frequency in that article. A list of several thousand terms that occur at high frequency in 20 or more studies is generated. For each term of interest (e.g., 'emotion', 'language', etc.), the entire database of coordinates is divided into two sets: those that occur in articles containing the term, and those that don't. A giant meta-analysis is performed comparing the coordinates reported for studies with and without the term of interest. In addition to producing statistical inference maps (i.e., z and p value maps), we also compute posterior probability maps, which display the likelihood of a given term being used in a study if activation is observed at a particular voxel.Cognitiveatlas.orgThe Cognitive Atlas is a collaborative knowledge building project that aims to develop a knowledge base (or ontology) that characterizes the state of current thought in cognitive science. The project is led by Russell Poldrack, Professor of Psychology and Neurobiology at the University of Texas at Austin in collaboration with the UCLA Center for Computational Biology (A. Toga, PI) and UCLA Consortium for Neuropsychiatric Phenomics (R. Bilder, PI). It is supported by grant RO1MH082795 from the National Institute of Mental Health.
Meta data about institutions, countries of origin, patents, etc… can be included
Can be used to map institutional strategies… e.g. NSF and NIMHAre used to determine which fields are most closely connected, which produce the most patents, and which are the most intellectually vitalConnectedness coefficients between fields are calculated year-by-year to measure change. Authors found that connectivity is going up in ‘distant’ fields. This indicates that science is in a state of change.
A network consists of nodes connected by edges, in the mathematical lingo, or what we call “links.” When you’re clicking through pages on Facebook, you can imagine that you are a node linked to your friend John, another node, and that John is linked to a few of your friends in addition to people that you aren’t friends with. This sort of social network is a complex, hierarchical structure, in which you can be indirectly connected to millions of other people (750 million users) around the world through your few hundred friends. The idea of social networks is not new at all to sociologists, who in the 1930’s first used what they referred to as “sociograms” to describe interpersonal relations. More recently, social network analysis has been applied to study networks of terrorist cells and scientific journal co-authors. Sociologists have also developed mathematical tools to measure network properties, as well as computer programs to visualize large networks. The concept of networks is also important for neuroscientists. We all know that behaviors are not controlled by discrete brain areas working independently of each other, but rather by complex anatomical networks of a number of distally connected regions. In learning and memory research, a more sophisticated neural network model has been proposed for an associational organization of memories.
The sources of bias: (1) Across subfields, research on a given topic may be advanced to varying degrees. The boundaries between those subfields may be more or less permeable. (2) Imprecision in terminology leads to unnecessary distinctions and unwanted conflations. What one calls “working memory” may be referred to as “cognitive control” by another. (3) Once a brain function is linked to a function, knowledge of that link can shape the direction of future research. This may lead to reification of concepts as new researchers apply old labels to their findings.
We’ve applied the idea of a social network to study the semantic structure of cognitive neuroscience. Instead of people or neurons, our nodes are words from the abstracts of fMRI articles. The idea is that, typically speaking, authors use anatomy and concepts words together when they are describing a relationship between a brain area and a behavior or cognitive process. By searching for word co-associations, we extract this relational information. Then, we integrate these relationships across the literature into a network structure.
Functional MRI was selected largely because of its popularity: it was the most widely used human neuroimaging technique in the unfiltered pool. Additionally,by restricting our analysis to studies that employed a common neuroimaging method, we minimized differences in terminology and rhetoric. Because it was byfar the most common human neuroimaging method, fMRI was a good PROXY for the complete literature.The curation of the corpus involved parsing through those 7,675articles in the selected journals and time range to identify those that used fMRI to study human behavior or cognition. This meant excluding articles that referred to fMRI for atlas generation, in studies of fMRI methods or the hemodynamic response, etc.
Next, we generated alist of all words in the corpus of abstracts. This included over 15,000 unique words.The list sorted by frequency, and the 100 most frequent Anatomy and Concept terms were identified.
First, a bigram thesaurus was created to collapse word pairs to single words by replacing spaces with underscores. This involved generating a list of words that co-occurred together, and identifying those that fit one of our semantic categories. The process was then iterated for longer phrases: for example, “primary somatosensory cortex” was first converted to “primary_somatosensory.” A new co-occurrence list was generated, and we found that “primary somatosensory” appeared with “cortex.”Second, a generalization thesaurus was created to normalize for plurals, acronyms, and hyphenated compounds. To avoid manually searching the entire list, we only normalized for variants that were more common than the 100th most frequent words on the list. Finally, these terms were converted into presentable titles for the vsualization.
The terms used to generate the networks were the 100 most frequent word forms to appear in the text after preprocessing. The final judgment of term appropriateness for the two lists was made by two expert raters (authors LGA and SAH) who evaluated every candidate term.It is interesting to note that Ccncepts words are, on the whole, used more frequently. Though not shown, even the minimum frequencies for words to make the top-50 lists were higher for concepts than anatomy (50 vs. 15). This suggests that a larger portion of each abstract is devoted to discussing the behavioral components of the experiment, results, and implications. We’ll see that these frequency shifts bear implications for the networks, resulting in a higher network density for concepts vs. anatomy terms.
Automap software was used to generate a meta-network comprised of links within and between Anatomy and Concept node classes. A link was identified as the co-occurrence of two terms within a moving window of six adjacent words that appeared in the same sentence. Links were directed from the first to the second term, as read from left to right across the text within the window. Link weights were calculated from the sum of term co-occurrences throughout the corpus and were used to construct the three networks: Conceptual (Concepts to Concepts), Anatomical (Anatomy to Anatomy), and Functional (Anatomy to Concepts and Concepts to Anatomy).In order to create an interpretable visualization, we applied a filter to link weights so that only the top fifty nodes with the most highly weighted links are shown.
The Conceptual, Anatomical, and Functional networks are substructures within the larger meta-network.
Represents the “cognitive” or psychological underpinnings of the cognitive neuroscience field. Intuitive arrangement.... “Attention,” for example,” is connected to “control,” “top-down,” “selection,” “spatial,” and “vision.There appears to be a central hub of words including “control,” “attention,” “vision,” “object,” “representation,” and “motor” that relate to different domains in cognitive neuroscience. It is notable that “memory,” the second most frequent term, is disconnected from the central hub, appearing at the center of its own cluster off to the side.This is the densest network of the three. The minimum weight threshold is 51, which is the highest threshold of all the networks. Several disconnected islands -- e.g. neuroeconomics
Before going further, it is useful to considerthe connections within the island and between the mainland had we A) picked a lower threshold and B) picked a higher threshold.In either case the same set of nodes would have been largely isolated from the rest of the network.Bootstrapping empirical approaches will be useful in quantifying this further, but here is antidotal evidence
There are a few measures that can be used to quantify the relative position of a node in the network. Degree centrality is the raw number of a node’s connections, and this is highly correlated (r~.8) with frequency in our network. Note that we compute betweenness centrality that does incorporate link weight.
The plot of frequency shows thatbetweenness and frequency are loosely correlated, as the more frequent terms tend to serve linking roles in the network. Nonetheless there are a number of interesting nodes which are more or less between than predicted by frequency. There is a cluster of highly frequent terms in the bottom right corner below the regression line, indicating that they have lower betweenness than expected by their frequency. This suggests that words like “vision,” “memory,” and “reward,” which refer to distinct domains, tend to be used in the context of only their own domains.At the top of the plot, words like “selection,” “emotion,” and “control” refer to processes that span those domains. While not as frequent, these terms create bridges throughout the network.
The network of anatomy terms is considerably less dense than the concepts network, and it lacks a central hub or self-organized clusters. Instead, it appears to be dominated by three terms: “PFC,” “amygdala,” and “ACC.” From these and other common terms, branches emerge relating to processing pathways. For example, “insula” gives rise to a sensorimotor pathway linking cortical and subcortical regions.Finally, there are several groups of terms that are entirely disconnected from the network. These correspond to visual regions and prefrontal regions.
The plot of betweennes vs. frequency shows a cluster of terms in the top left which are considerably more between than expected by frequency. Insula, for example, has the second highest betweenness despite coming in only 9th on the frequency list. This is likely due to its important role in connecting the somatosensory branch to the main network.The most notable outlier is “thalamus,” which is the most highly between term despite its relatively low position at 15th on the frequency list. Its high betweenness is likely due to its direct connections to both “amygdala” and “ACC,” two of the most frequent and central nodes in the network. .
Drilling down on “thalamus” shows that it is connected to a great many more nodes than shown on the thresholded network. Its low-level connections to “hippocampus,” “mPFC,” and “parietal cortex” likely contribute to its high betweenness. This is an important reminder that although the network visualization only displays nodes with the strongest connections, the measures are based on the connectivity of the entire network.
The most direct representation of cognitive neuroscience. The network structure appears to be driven by anatomy terms which have a higher relative betweenness centralityConversely, high-frequency concepts terms show up on the margins of the network. Several are pendants with only one other connection, such as “emotion” and “observation.” There are also quite a few dyads and triads of words disconnected from the network and with only one or two above-threshold connections. The disconnectedness and low density of this network suggest that there is room to strengthen the links between anatomy and function in the field of cognitive neuroscience.
Clearly, the anatomy and concepts terms fall along different regression lines. The anatomy terms have higher betweenness, reflecting their central positions on the network. Because the anatomical terms are less frequent, the network structure depends on how concepts are arranged around the anatomical terms.
Second-order projection networks were computed by correlating across each row in the adjancy matrix of link weights. They show links between terms that occupy similar positions in the network, and we thus call them “semantic synonyms.”Have similar meaning (e.g. Anticipation and Future) Come from the same circumscribed area of the literature (risk and reward)Suggest aspects of the literature that deserve further refinement
Retrodictive forecasting
Produces an unbiased synthesis Our approach characterizes how cognitive neuroscience presents itself to the larger scientific community, through the summaries of individual articles within their titles and abstracts.Additionalstudies of their function would have the greatest effects on the overall character of the network, so we identify them as particularly important targets for future research
Bruer Himself characterizes these as “toy maps” that may not be representative of the whole discipline.1988 and 2007 journal citation map. Asymmetric between journal co-citation (not author-wise or directional) Hub‐authority journals are black nodes, authority journals dark grey nodes, hub journals light grey nodes. Nodes are proportional to hub scores, authority scores, and hub + authority score for the black nodes.1988 & 2007 cognitive neuroscience topic map. Topic maps are generated from “stop-listed title words” of all the articles in the citation maps. Co-occurrence matrices are computed for words that occur at least 11 times in the article titles-that year and the spatial arrangement is ‘spring loaded using the Kamada-Kawai algorithm. Node size proportional to log of word occurrences.
From Bruer 2004) “On the small scale employed in this study, one might hope to see how cognitiveneuroscience emerged from its progenitor disciplines (systems neuroscience, cognitivepsychology, neuropsychology) by noting changes in co‐citation patterns among theprogenitor discipline journals and possibly through the appearance of new cognitiveneuroscience journals. These maps also allow us to visualize the citation flow amongjournals and to assess how results and ideas flowed among them. For aninterdisciplinary field emerging from a multi‐disciplinary foundation, like cognitiveneuroscience, one might be able to see how, for example, ideas and results fromneuroscience fed into psychology, from psychology into neuroscience, or both.”
The global structure includes all of the nodes within the defined boundaries of the network. It can be described by density, or the degree of interconnectedness of the nodes. It can also be described by topology, defined as “the shape, or form, of a network,” i.e., which nodes are connected to which other nodes (Moody). The global structure can be expected to change in datasets collected from different time periods. The intrinsic structure of a network arises from the position, connectivity, and centrality of individual nodes. Connectivity refers to the direct relations a node has to other nodes. Its position within the network is dependent on the connectivity of all nodes, and its centrality is the degree to which the node is located at an important position within the network.
Uses Bibliographic Coupling of SCI & SSCI to arrive at ‘Neighborhoods’ and ‘Disciplines’.Cog Neuro Lives out hereWill contact Klavas about “science Locating it”