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 –

AQMdesStatsI.xls

AQMdesStatsII_moments.xls

AQMdesStatsIII_flat.xls

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

Student's t-test

Correlation and Trend analysis

non-parametric vs t-tests

FFTdigitalFiltering

Discrete Sampling and Sampling Errors

Percentage Transform

ANOVA power

Advanced methods

Correlogram

Effect Magnitude and Data Transformation

Link to Statistical Power Analysis Java Applets (sophisticated)

Link to ANOVA Power Analysis (simple)

ImmunoHistoChemistry Methods

Quantitative Double Labeling

Selecting Fluorochromes

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, 8:30 a.m. to 5:30 p.m.

The contact information for the Office of Disability Services is:

The University of Texas at Dallas, SU 22

PO Box 830688

Richardson, Texas 75083-0688

(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.

Date

Topic

Assignments and Due Dates