The Research Methods of Biopsychology: Part I

1. The Research Methods of Biopsychology: Part I Methods of Studying the Nervous System

2. Hebb, D.O. (1949). The organization of behavior: A neuropsychological theory. New York: John Wiley & Sons Inc. “when an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” (p. 62) In other words: “cells that fire together, wire together.” Wikipedia states - …although this is an oversimplification of the nervous system not to be taken literally, as well as not accurately representing Hebb's original statement on cell connectivity strength changes. …simultaneous activation of cells leads to pronounced increases in synaptic strength. Such learning is known as Hebbian learning. If Wikipedia suggests caution, well…

3. Hebbian Learning “What has become known as ‘Hebb’s synapse’ or ‘learning postulate’ is one of the few aspects of the theory he did not consider completely original. Something like it had been proposed by many psychologists, including Freud in his early years as a neurobiologist.” (Milner, 1993)

4. Lack of novelty aside, the theory had an even bigger problems because current technology rendered it untestable. Then, in 1973… A hundred or so rapid high frequency stimulations of the PP resulted in LTP at the recording site. LTP is the increased synaptic strength that lasted for months or even longer, theoretically as long as a memory trace may exist.

5. Here is a modern electrophysiological recording apparatus that can both stimulate and record electrical signals from a 400 micron thick slice of the rat hippocampus. Just what you need to measure LTP. • Biopsychological research often dependends on advances in technology • Brain imaging • recording neural activity • lesion technology

6. Standard X-ray techniques are good for visualizing bone in flesh (or metal in flesh given unfortunate circumstances) Such techniques are not very useful for visualizing brain structures because X-rays primarily pass through soft tissue. So this is a fake pic from someone trying to belittle Homer Simpson

7. Contrast X ray techniques Use of X ray technology with other features that enhance contrast to visualize aspects of brain anatomy Pneumoencephalography Angiography

8. Methods of visualizing the human brain Computerized Axial Tomography (CAT) CAT is a 3-D X-ray constructed of a series of photographs representing horizontal sections through the brain.

9. Methods of visualizing the human brain Computerized Axial Tomography (CAT) The X-ray tube and detector rotate in opposition around the brain at one level taking a series of measurements from which an image of one section is constructed.

10. Methods of visualizing the human brain Magnetic Resonance Imaging (MRI) Provides high resolution 3-D images of the brain.

11. Methods of visualizing the human brain Magnetic Resonance Imaging (MRI) Measures the waves emitted by hydrogen atoms when they are activated by radio-frequency waves in a magnetic field.

12. II. Methods of visualizing the human brain D. Positron Emission Tomography (PET) Provides information about the metabolic activity of the brain.

13. Methods of visualizing the human brain Positron Emission Tomography (PET) The patient is injected with radioactive 2-deoxyglucose (2-DG) which is taken up rapidly by active neurons.

14. Methods of visualizing the human brain Positron Emission Tomography (PET) Because 2-DG cannot be metabolized, it temporarily accumulates in active (energy consuming) neurons and will indicate the brain regions that are active during performance of some test.

15. Methods of visualizing the human brain Given the following images, what is your diagnosis of the patient? CAT scan MRI scan PET scan No metabolic activity

16. Methods of visualizing the human brain Functional MRI* Provides information about increases in oxygen (blood) flow to brain regions that are active during performance of a task. The BOLD signal - magnetic resonance of blood changes when oxygenated.

17. Methods of visualizing the human brain Functional MRI* • Advantages over PET: • Noninvasive • Shows both structure and function • Spatial resolution is better • Can produce 3D images of activity over the entire brain

18. magnetoencephalogram (MEG) - the magnetic fields produced by electrical brain activity. electroencephalogram (EEG) - the associated scalp potentials. Clusters of thousands of synchronously activated pyramidal cortical neurons are believed to be the main generators of MEG and EEG signals. provide unique insights into the dynamic behavior of the human brain as they are able to follow changes in neural activity on a millisecond time-scale

19. Transcranial Magnetic Stimulation • Disrupts activity in an area of cortex by creating a magnetic field under a coil positioned next to the skull.

20. The God helmet

21. Parallel and Interactive Memory Systems in the Human Brain and the limitations of fMRI studies

22. In this learning game you are the weather forecaster. You will learn how to predict rain or shine using a deck of four cards: Probabilistic Classification Task Knowlton, B.J., Mangels, J.A., & Squire, L.R. (1996) Science, 273, 1399-1402.

23. (A) Performance on the probabilistic classification tasks by controls (CON, n = 15), amnesic patients (AMN, n = 12), patients with Parkinson’s disease (PD, n = 20), and a subgroup of PD patients with the most severe symptoms (PD*, n = 10). (B) Performance on the declarative memory task. Both PD and PD* groups exhibited entirely normal declarative memory for facts about the testing episode, despite their poor performance on the task itself. In contrast, amnesic patients exhibited a severe impairment in declarative memory for the testing episode but normal performance on the classification test.

24. Probabilistic Classification Tasks for fMRI Studies

25. a. Activation for FB compared to baseline (yellow = increase, blue = decrease); b. Activation for PA compared to baseline; c. Regions exhibiting significant differences between FB and PA tasks; d. Plot of task related signal change from the MTL region exhibiting maximal task-dependent differences against a region in the right caudate that exhibited significant negative correlation with the MTL in functional connectivity analysis. Each data point represents a single subject. Poldrack, R.A., Clark, J., Paré-Blagoev, E.J., Shohamy, D., Moyano, J.C., Myers, C., & Gluck, M.A. (2001). Nature, 414, 546-550.

26. Results from event-related FMRI study of FB category learning (experiment 2). a, Regions exhibiting significant evoked activation (yellow) or deactivation (blue) for classification trials. Yellow arrow highlights region of caudate activation, white arrowhighlights region of MTL deactivation. b, c, Depiction of parametric change in modelled evoked haemodynamic response across the initial 450-s scanning run (averaged across subjects) in b, left body of caudate nucleus (-12, 3, 21), and c, left MTL (-24, -3, -24). Red indicates positive, event-related response, blue indicates negative event-related response. Left body of caudate nucleus Left medial temporal lobe Poldrack, et al. (2001). Interactive memory systems in the human brain. Nature, 414, 546-550.

27. Put on the critical thinking caps (save the God helmet for later) What can/should we conclude from this study? The authors state that the results: “…provide the first substantive evidence, to our knowledge, for competition between memory systems in the human brain …the present study provides direct evidence for competition at the neural level by demonstrating three essential features of the MTL-striatum interaction. First, it shows that engagement of MTL and striatum is modulated by whether the task encourages the use of declarative versus nondeclarative memory processes or strategies. Second, it demonstrates that engagement of these regions is negatively correlated across subjects. Third, it demonstrates rapid reciprocal changes in the engagement of these regions. These data are concordant with animal lesion studies demonstrating that the memory systems based on the MTL and striatum can compete with one another during learning.” Poldrack, R.A., Clark, J., Paré-Blagoev, E.J., Shohamy, D., Moyano, J.C., Myers, C., & Gluck, M.A. (2001). Nature, 414, 546-550.

28. However, the authors also state that their computational theory: “…interprets both the earlier animal data and the present human imaging data as implying an interaction between the hippocampus and other brain structures, in which the hippocampus has a modulatory role in learning by developing new stimulus representations during early phases of training which are used by the striatum to develop complex stimulus-response associations.” Poldrack, R.A., Clark, J., Paré-Blagoev, E.J., Shohamy, D., Moyano, J.C., Myers, C., & Gluck, M.A. (2001). Nature, 414, 546-550.