HCS 7372.501— Spring
2007 — Graduate Seminar:
Advanced
Quantitative Methods in the Neurosciences
Green Hall GR4.402 Tuesdays and
Thursdays
Instructor
Contact Information
Larry Cauller Ph.D. Marco
Atzori, Ph.D.
JO4.214 972-883-2436 JO4.212 972-883-4311
lcauller@utdallas.edu marco.atzori@utdalla.edu
Office hours: Tuesdays and Thursdays 1:00PM
________________________________________________________________________________________________________
Prerequisites: HCS
6312, Research Methods I
(may
be waived with instructor permission)
Course Description:
This course will provide instruction in the fundamentals of
advanced statistical analysis as the basis for selection and application of
appropriate tests widely employed for neuroscience research. The course will
review specific examples of published studies that illustrate the proper
application of these tests and their particular advantages for neuroscience
research. The course will also review other quantitative methods that are
frequently employed in neuroscience research and must be understood in detail
to fully appreciate the power of such methods and the significance of such
research.
Contemporary research in the neurosciences employs a wide
range of mathematical tools in addition to the statistical methods that
quantify the reliability of experimental results. Every neuroscientist needs a
basic understanding of the mathematical underpinnings of these tools to
appreciate the particular insights they may provide, as well as their
limitations and proper uses in neuroscience research.
In addition to the topics listed in the following tentative
outline, particular attention will be given to quantitative methods that appear
prominently in published research identified by students enrolled in the
course:
1st half Instructor Dr. Larry Cauller
1.
The Practice of Statistics in the Neurosciences –
Overcoming the inferiority complex most researcher’s suffer when it comes to
statistics.
a.
‘Descriptive’ Statistics – taking a good look at
your sample distribution as a reflection of the population.
b.
‘Inferential’ Statistics and the power of the
‘Central Limit Theorem’.
c.
Assumptions and Robustness
d.
The weakness of Statistical ‘Power’ – why science
is only built upon positive result.
e.
Decision-based versus experiment-wise error rates –
Bonnferonni’s correction
f.
The a priori
demands of Spartan Statisticians versus
the post-hoc realities of research in
the real world.
g.
The limitations of multi-level ANOVA tests and the
dangers of multi-factor interactions – Dr. Cauller’s biggest pet peeve about
statistics.
h.
The necessity of specific contrasts to overcome the
limitations of ANOVA tests.
i.
Types of data in the neurosciences: When it’s best
to use non-parametric statistics – those pesty outliers.
2.
Specific contrast (multiple comparison) methods
a.
Including: Tukey’s, Newmann-Keuls and good ol’
Student’s t-test (Dr. Cauller’s favorite – non-conservative and high power)
b.
Choosing a good comparison test – conservative
versus powerful
c.
Non-parametric comparison tests – Rank-sums,
Mann-Whitney U, Sign-test
d.
Why t-tests can handle just about anything without
assuming equal variance
e.
Pitfalls and rules.
3.
Specific applications of statistical methods in
published neuroscience research
a.
Review of statistical methods commonly found in
published neuroscience research.
b.
Special attention to statistical methods the
students find in their own readings
4.
Electrophysiological data analysis: Looking through
the neuro-electric keyhole?
a.
Bio-electric signal generation: current-source
density
b.
Neural electrodes: where the metal meets the mush:
Electro-chemical Impedance
c.
Electrometers: Amplification and the effects of
filtering
d.
Levels of neuro-electric analysis – from inside
neurons to the surface of scalp
5.
Digital Signal Analysis - From wet neurons to
computers
a.
Discrete sampling of continuous neural signals –
Sampling rate and bit precision
b.
Time-locked averaging and evoked potentials – detecting faint messages amidst the roar of a
noisy brain
c.
Spike signals – the least ambiguous measure of
neuron activity
i. Introduction
to NeuroExplorer – state-of-the-art software for spike signal analysis
ii. peri-stimulus
time histograms (PSTH)
iii. inter-spike-intervals
(ISI)
iv. auto- and
cross-correlograms
v. Information
theory and the analysis of confusion
6.
Quantitative analysis of microscopic neural
structure
a.
Introduction to ImagePro – state-of-the-art
software for tissue image analysis
b.
Matching the spectra of fluorochromes and filters –
contemporary microscope methods
c.
Effective sampling methods for counting and
measuring neurons with statistical confidence
d.
Quantitative analysis of relative label intensity
7.
The Euler constant and nature’s pivotal difference
equation – its pervasive role in neuroscience theory and methods
a.
Length and time constants
b.
Kinetics and active bio-physics
8.
Numerical simulation versus analytical solutions
a.
The limits of simplification
b.
Dynamical systems and intractability
2nd half instructor Dr. Marco Atzori
1)
Elements of calculus
2) Fourier
Transforms: formant analysis
3) Auto- and
cross-correlation function: NMR decay time, synchronization in neuronal
ensembles
4)
Distributions / Dirac-d function / random number generation/ binomial and
Poisson distributions
5) Quantal
analysis in synaptic transmission (Salgado)
6)
Coefficient of variation analysis in synaptic transmission
7) Noise
analysis in single-channel conductance measurement
8) Wavelet
analysis
9)
Single-cell, single-compartment Hodgkin and Huxley model (Lu Dinh)
10)
Multiple compartment HH, interneuronal network (Tram Nguyen)
Student Learning Objectives:
After completing the
course, students should be able to:
1.1 Identify and select statistical tests
appropriate for the majority of uses in neuroscience research.
1.2 Explain the importance of descriptive
statistics and employ such statistics to characterize the sample distributions
of neuroscience data..
1.3. Describe the
limitations and pitfalls of ANOVA tests with respect to multi-factor and
multi-level experimental designs.
1.4. Explain the
importance of multiple comparison tests and employ such tests to overcome the
limitations of ANOVA tests.
2.1 Describe the
mathematical underpinnings of a wide range of quantitative methods, in addition
to statistics, commonly employed in the neurosciences.
2.2 Characterize major types of neuroscience data
from neuro-electric signals to microscopic images of neural structures.
2.3 Identify the particular quantitative methods
employed in contemporary neuroscience research for the analysis of these major
types of data.
3. Interpret and criticize published
applications of advanced statistical and other quantitative methods.
4. Identify and describe the capabilities of
state-of-the-art software applications widely available for statistical and
other quantitative analyses useful for neuroscience research.
Textbooks and materials:
No
textbooks are required for this course.
Students
are encouraged to find a friendly statistics textbook with an emphasis upon
practical applications and step-by-step calculations. (Students are invited to
study excerpts from Dr. Cauller’s out-of-date text.)
The
course will require students to make extensive use of PubMed search tools and
access electronic neuroscience journals on-line.
Exams and student assignments
Exams:
Students will be required to take two exams, a mid-term exam related to
topics presented by Dr. Cauller, and a final exam related to the topics
presented by Dr. Atzori Each exam will
be administered during a single class period. Student will be expected to
describe and interpret analytical methods related to figures taken from
published neuroscience studies.
Assignments: Students will be asked to find
specific examples of published applications of both statistical and other
quantitative methods from the contemporary neuroscience literature and discuss
these examples in class.
Attendance:
Student attendance and participation in each class will be directly
noted by the instructors.
Grading Policy
20%
of the overall grade will be based upon student attendance and participation.
40% of the overall grade will be
based upon student performance on each of the two exams.
Course policy
Professionalism: In
appreciation of the multidisciplinary nature of this course, students will be
expected to respect the importance of every topic by participating in each
class with as much enthusiasm as they have for topics specifically related to
their personal interests.
Downloads
16 Jan –
AQMdesStatsIII_normalized_flat.xls
AQMinfStatsV_CentralLimitTheorem.xls
18 Jan –
AQM_PracticalStatistics_Para.pdf
AdvQuantMeth_SquaredDeviationDerivation.doc
23 Jan –
AQM_PracticalStatistics_nonPara.pdf
Link to Wikipedia_on_Statistics – beware, Mann-Whitney U-table link is simply wrong.
Advanced methods demonstrated with Excel
Correlation and Trend analysis
Discrete Sampling and Sampling Errors
Advanced methods
Effect Magnitude and Data Transformation
Link to Statistical Power Analysis Java Applets (sophisticated)
Link to ANOVA Power Analysis (simple)
These descriptions and timelines are subject to change at the discretion
of the Professor.
Date
|
Topic
|
Assignments
and Due Dates
|
|
Week 1 Jan. 9 |
Dr. Cauller – Introduction and Orientation
to the Course Introduction
to using MS Excel for class demonstrations |
Find
a published application of statistics in your research area |
|
Jan. 11 |
Practical
Statistics in the Neurosciences Descriptive Statistics and
Population Distributions The Limitations of ANOVA |
|
|
Week 2 Jan. 16 |
Application
of Statistics in Neuroscience Research Inferential Statistics and The Power of the
Central Limit Theorem
Assumptions, Robustness and Pitfalls The
Impractical Notion of Statistical Power –
Why science is only built upon positive results.
Types of Neuroscience Data – Non-parametric statistics. |
|
|
Jan. 18 |
Using Specific Contrast Statistical Tests –
Beyond ANOVA |
|
|
Week 3 Jan. 23 |
Applications of Statistics in
Published Neuroscience Research Review
of examples of published applications of statistics |
|
|
Jan. 25 |
Student-led
discussion of specific examples of published statistical applications related
to their individual neuroscience interests
|
Presentation
of published applications of statistics in neuroscience research |
|
Week 4 Jan. 30 |
Quantitative Methods for the Analysis of Neuro-Electric Signals Principles
of Neuro-Electric signal generation Levels
of Neuro-Electric analysis – intracellular to EEG |
Find
a published study that employs quantitative tools for the analysis of
neuro-electric signals |
|
Feb. 1 |
Digital
Signal Analysis – Analog-to-Digital Conversion The
Power of Time-locked Signal Averaging Quantitative
Analysis of Spike Signals – Intro to NeuroExplorer |
|
|
Week 5 Feb. 6 |
Student-led
discussion of published examples of quantitative neuro-electric signal
analysis |
Presentation
of published examples of neuro-electric signal analysis |
|
Feb. 8 |
Quantitative Analysis of
Microscopic Neural Structure Fluorescence
microscopy – experimental design and controls Sampling methods for counting
and measuring labeled neurons |
Find
example of quantitative micro-structure analysis in published neuroscience
research |
|
Week 6 Feb. 13 |
Introduction
to ImagePro – state-of-the-art software from the NIH Quantifying
relative label intensity |
|
|
Feb. 15 |
Student-led
discussion of published applications of quantitative micro-structure analysis
in neuroscience research |
Presentation
of published neuroscience research that employed micro-structure analysis |
|
Week 7 Feb. 20 |
Numerical Methods for Simulation of Neural Dynamics Computational
Neuroscience for explanation and validation |
|
|
Feb. 22 |
Fourier Analysis and
Digital Filtering |
|
|
Week 8 Feb. 27 |
Review and
preparations for test |
|
|
Mar. 1 |
MID-TERM EXAMINATION on Dr. Cauller’s topics |
|
|
Week 9 Mar 6 & 8 |
SPRING BREAK |
Have
fun – you’ve earned it. |
|
Week 10 Mar. 13 |
Dr. Atzori INTRODUCTION and ORIENTATION to further topics Essential
Elements of Calculus |
|
|
Mar. 15 |
Auto- and cross-correlation analysis – decay and synchronization |
|
|
Week 11 Mar. 20 |
Signal Decomposition Analysis Fourier Transforms – speech formants and spectral
complexity |
|
|
Mar. 22 |
Wavelet Analysis – more appropriate for transient
signals |
|
|
Week 12 Mar. 27 |
Random neural events - Binomial
and Poisson Distributions The
Dirac-d function: constructing
the real world…
… out of imaginary concepts |
|
|
Mar. 29 |
Noise analysis of single-channel nano-signals |
|
|
Week 13 Apr. 3 |
The
Analysis of Synaptic Transmission – Beyond the reach of direct observation Coefficient
of Variation analysis |
|
|
Apr. 5 |
Quantal
analysis of synaptic release (Salgado) |
|
|
Week 14 Apr. 10 |
Computational Models have played a Central Role… …
in History of Neuroscience Breakthroughs The single-compartment
Hodgkin and Huxley (HH) model (Lu Dinh) |
|
|
Apr. 12 |
Synaptic Network
models constructed of multi-compartment HH neurons (Tram Nguyen) |
|
|
Week 15 Apr. 17 |
Review and Discussion of Dr. Atzori’s topics |
|
|
Apr. 19 |
FINAL
EXAMINATION on Dr. Atzori’s topics |
|
|
Week 16 Apr. 23 |
FINALS WEEK |
|
Student Conduct & Discipline
The University of Texas System
and The University of Texas at Dallas have rules and regulations for the
orderly and efficient conduct of their business. It is the responsibility of each student and
each student organization to be knowledgeable about the rules and regulations
which govern student conduct and activities.
General information on student conduct and discipline is contained in
the UTD publication, A to Z Guide,
which is provided to all registered students each academic year.
The University of Texas at Dallas
administers student discipline within the procedures of recognized and
established due process. Procedures are
defined and described in the Rules and
Regulations, Board of Regents, The University of Texas System, Part 1, Chapter
VI, Section 3, and in Title V, Rules on Student Services and Activities of
the university’s Handbook of Operating
Procedures. Copies of these rules
and regulations are available to students in the Office of the Dean of
Students, where staff members are available to assist students in interpreting
the rules and regulations (SU 1.602, 972/883-6391).
A student at the university
neither loses the rights nor escapes the responsibilities of citizenship. He or she is expected to obey federal, state,
and local laws as well as the Regents’ Rules, university regulations, and
administrative rules. Students are
subject to discipline for violating the standards of conduct whether such
conduct takes place on or off campus, or whether civil or criminal penalties
are also imposed for such conduct.
Academic Integrity
The faculty expects from its
students a high level of responsibility and academic honesty. Because the value of an academic degree
depends upon the absolute integrity of the work done by the student for that
degree, it is imperative that a student demonstrate a high standard of
individual honor in his or her scholastic work.
Scholastic dishonesty
includes, but is not limited to, statements, acts or omissions related to
applications for enrollment or the award of a degree, and/or the submission as
one’s own work or material that is not one’s own. As a general rule, scholastic dishonesty
involves one of the following acts:
cheating, plagiarism, collusion and/or falsifying academic records. Students suspected of academic dishonesty are
subject to disciplinary proceedings.
Plagiarism, especially from
the web, from portions of papers for other classes, and from any other source
is unacceptable and will be dealt with under the university’s policy on
plagiarism (see general catalog for details).
This course will use the resources of turnitin.com, which searches the
web for possible plagiarism and is over 90% effective.
Email Use
The University of Texas at Dallas
recognizes the value and efficiency of communication between faculty/staff and
students through electronic mail. At the same time, email raises some issues
concerning security and the identity of each individual in an email
exchange. The university encourages all
official student email correspondence be sent only to a student’s U.T. Dallas
email address and that faculty and staff consider email from students official
only if it originates from a UTD student account. This allows the university to
maintain a high degree of confidence in the identity of all individual corresponding
and the security of the transmitted information. UTD furnishes each student with a free email
account that is to be used in all communication with university personnel. The
Department of Information Resources at U.T. Dallas provides a method for students
to have their U.T. Dallas mail forwarded to other accounts.
Withdrawal from Class
The administration of this institution has set
deadlines for withdrawal of any college-level courses. These dates and times
are published in that semester's course catalog. Administration procedures must
be followed. It is the student's responsibility to handle withdrawal
requirements from any class. In other words, I cannot drop or withdraw any
student. You must do the proper paperwork to ensure that you will not receive a
final grade of "F" in a course if you choose not to attend the class
once you are enrolled.
Student Grievance Procedures
Procedures for
student grievances are found in Title V, Rules on Student Services and
Activities, of the university’s Handbook
of Operating Procedures.
In attempting to
resolve any student grievance regarding grades, evaluations, or other
fulfillments of academic responsibility, it is the obligation of the student
first to make a serious effort to resolve the matter with the instructor,
supervisor, administrator, or committee with whom the grievance originates
(hereafter called “the respondent”).
Individual faculty members retain primary responsibility for assigning
grades and evaluations. If the matter
cannot be resolved at that level, the grievance must be submitted in writing to
the respondent with a copy of the respondent’s School Dean. If the matter is not resolved by the written
response provided by the respondent, the student may submit a written appeal to
the School Dean. If the grievance is not
resolved by the School Dean’s decision, the student may make a written appeal
to the Dean of Graduate or Undergraduate Education, and the deal will appoint
and convene an Academic Appeals Panel.
The decision of the Academic Appeals Panel is final. The results of the academic appeals process
will be distributed to all involved parties.
Copies of these
rules and regulations are available to students in the Office of the Dean of Students,
where staff members are available to assist students in interpreting the rules
and regulations.
Incomplete Grade Policy
As per
university policy, incomplete grades will be granted only for work unavoidably
missed at the semester’s end and only if 70% of the course work has been
completed. An incomplete grade must be
resolved within eight (8) weeks from the first day of the subsequent long
semester. If the required work to
complete the course and to remove the incomplete grade is not submitted by the
specified deadline, the incomplete grade is changed automatically to a grade of
F.
Disability Services
The goal of
Disability Services is to provide students with disabilities educational
opportunities equal to those of their non-disabled peers. Disability Services is located in room 1.610
in the Student Union. Office hours are
Monday and Thursday, 8:30 a.m. to 6:30 p.m.; Tuesday and Wednesday, 8:30 a.m.
to 7:30 p.m.; and Friday,
The contact
information for the Office of Disability Services is:
The University
of Texas at Dallas, SU 22
(972) 883-2098
(voice or TTY)
Essentially, the
law requires that colleges and universities make those reasonable adjustments
necessary to eliminate discrimination on the basis of disability. For example, it may be necessary to remove
classroom prohibitions against tape recorders or animals (in the case of dog
guides) for students who are blind.
Occasionally an assignment requirement may be substituted (for example,
a research paper versus an oral presentation for a student who is hearing
impaired). Classes enrolled students
with mobility impairments may have to be rescheduled in accessible facilities. The college or university may need to provide
special services such as registration, note-taking, or mobility assistance.
It is the
student’s responsibility to notify his or her professors of the need for such
an accommodation. Disability Services
provides students with letters to present to faculty members to verify that the
student has a disability and needs accommodations. Individuals requiring special accommodation
should contact the professor after class or during office hours.
Religious Holy Days
The
University of Texas at Dallas will excuse a student from class or other
required activities for the travel to and observance of a religious holy day
for a religion whose places of worship are exempt from property tax under
Section 11.20, Tax Code, Texas Code Annotated.
The
student is encouraged to notify the instructor or activity sponsor as soon as
possible regarding the absence, preferably in advance of the assignment. The student, so excused, will be allowed to
take the exam or complete the assignment within a reasonable time after the
absence: a period equal to the length of the absence, up to a maximum of one
week. A student who notifies the instructor and completes any missed exam or
assignment may not be penalized for the absence. A student who fails to
complete the exam or assignment within the prescribed period may receive a
failing grade for that exam or assignment.
If a student or
an instructor disagrees about the nature of the absence [i.e., for the purpose
of observing a religious holy day] or if there is similar disagreement about
whether the student has been given a reasonable time to complete any missed
assignments or examinations, either the student or the instructor may request a
ruling from the chief executive officer of the institution, or his or her
designee. The chief executive officer or designee must take into account the
legislative intent of TEC 51.911(b), and the student and instructor will abide
by the decision of the chief executive officer or designee.
These descriptions and timelines are subject to
change at the discretion of the Professor.