1 01-eScience

1.1 Audio-recording

1.2 Uses of statistics


1.3 Meta-science

1.4 Most important theme the whole semester

Dr. Taylor’s Tao of data analysis:
Follow the data, and abstract as little as possible!

Occasionally, thoughtful abstraction and summary statistics will be needed and helpful, but much more rarely, and usually only in end-stage analysis or automation, not in initial exploration (initial bushwhacking science).

1.5 Admin notes

1.6 Improving the scientific process

1.6.1 How have functions of brain regions been studied?

Early studies
* Lesions (accidental and otherwise)
* Neurosurgery - lesion and direct stimulation
* PET studies
* fMEG, fMRI

Do regions perform modular functions?

What is “representation”?

Function/Representation in cortex (task fMRI)

Classic fMRI

Define: Activation

1.7 Problem

Estimating the reproducibility of psychological science = 35%
(OpenScience-Collaboration, 2015; Science)

Single study fMRI: What stinks?
Does anyone know what kind of animal this is with “significant” activations in its brain?

1.7.1 Single study fMRI: What stinks?


“There is increasing concern that most current published research findings are false. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser pre-selection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias.” e.g. irradiating infant thymuses, statins, social psychology, nutrition research, Alzheimer’s drugs, etc.

1.7.2 Academic fashion

Academics (people) are not so rational about distributing research efforts or money to problems. What is the most interesting area of the brain?

Red = popularly studied (Behrens, 2012)
01-eScience/bias1.png What is the most interesting area of the brain?

Impact factor correlations (Behrens, 2012)
Picking the wrong brain region is a bad career move…

1.8 Solution 1: Meta-analysis


Manual meta-analysis of function performs ok…

What enabled the industrial revolution?
Craftsperson to Assembly line.
01-eScience/shoemaker.png => 01-eScience/indust.jpg
Large scale cooperation requires standardization.


Can standardization speed scientific progress as well?

Craftsperson scientist: Single study versus ?
01-eScience/shoemaker.png => 01-eScience/indust.jpg

01-eScience/fMRI1.png => ??

1.9 Solution 2: Systematic scale

1.9.1 Human Connectome Project

Solution 2: Very large projects
WU-Minn-Oxford group (the good one of the pair of schools doing this project) Solution 2: Systematic data collection

* Structural data were used for connectivity (above)
* Functional data used for meta-optimization (upcoming)

1.9.2 Blue Brain Project

Blue Brain Project Digital reconstruction of the brain by reverse-engineering mammalian brain circuitry

1.9.3 Blue Brain Project

Blue Brain Project

1.9.4 Allen Brain Atlas

01-eScience/aba0.png 01-eScience/aba1.png
01-eScience/aba2.png 01-eScience/aba3.png
Maps the expression of EVERY gene in the entire brain

1.10 Solution 3: Databasing existing data

Requires data sharing, centralized repositories

1.11 Solution 4: Ontologies and computability

Formal ontology for neuroscience studies


1.11.1 Computability and modeling the brain

1.11.2 BrainMap

BrainMap: 20 years of formally coded fMRI studies in one database

Reminder: activations

Representation in human cortex BrainMap hierarchical clustering of behavioral labels by activation locations alone:


Functional networks in human cortex Functional activations (1000s of studies) versus functional connectivity (1 study)


Side note: diseases show increased prevalence at cortical network hubs, including Alzheimers dementia, Aspergers syndrome, schizophrenia, frontotemporal dementia, juvenile myoclonic epilepsy, progressive supranuclear palsy, left and right temporal lobe epilepsy, and post-traumatic stress disorder.

1.11.3 Neurosynth

Neurosynth: automated fMRI databasing Neurosynth platform (backend in Python) auto-extracts tabular fMRI activation coordinates and word frequencies from published studies:


Representation in human cortex
* Forward inference maps show the probability of activation given the presence of the term, P(act.|term)
* Reverse inference maps show the probability of the term given observed activation, P(term|act.)


Data analysis task:
Which word-activation associations in the neurosynth database best spatially match your current brain state?
a.k.a. Mind-reading

Neurosynth: Mind reading task


General method we will cover in class – many very cool types!
This one is just a naive Bayes model.

Above chance (diagonals) for every category (i.e., success!)

Validated by manual meta-analysis

  1. Manual meta-analysis (b,c) Neurosynth

How has the function of regions been studied?

Early studies
* Lesions (accidental and otherwise)
* Neurosurgery - lesion and direct stimulation
* PET studies
* fMEG, fMRI

Modern neuroinformatics and computational neuroscience
* Large databases of studies:
* fMRI, MEG, DTI, rfMRI, gene expression, neuronal stucture, cellular connectivity
* Increases in power (n)
* More robust to bias (not entirely)
* Computable ontologies
* Functional models as hypotheses and publications/literature

1.12 Grand goal: Scientific standardization

Goal is to make this model-building process much more systematic:


Not 1 hypothesis, but at least 2, or better yet, systematically refining a computational model (e.g., bi-weekly model refinement based on empirical data).

The model is the knowledgebase, and should be the unit of publication, at least in many domains.