1. fMRI guided Microarray analysis Imaging-Guided Microarray: Isolating Molecular Profiles That Dissociate Alzheimer’s Disease from Normal Aging  A.C. Pereira, W. Wu & S.A. Small  Ann NY Acad. Sci. 1097, Feb 2007 Combining Brain Imaging with Microarray: Isolating Molecules Underlying the Physiologic Disorders of the Brain  A. Pierce & S.A. Small  Neurochemical Research, Vol. 29, No. 6, June 2004

2. Crash course: The CELL and microarrays in 3 slides Cells internal processes and inter-cell communication based on proteins Goal: Figure out which proteins exist in a cell under some condition  Condition – e.g. disease  Many times – detect proteins differentially expressed – e.g. disease vs. control Basic: staining a specific protein and follow it under a microscope Next: The CELL

3. From DNA to Protein (Final) product – Protein Intermediate product mRNA Idea: measure mRNA to get protein measurements Simultaneous measurements by hybridization

4. DNA Microarrays mRNA – concatenation of nucleotides 4 types ATGC – pegs/holes Process  Crush cell  Wash all but mRNA  Glue lamps Spill on chip Shake well!

5. Sorry, 4 slides... Chip design – probes for genes Light on --> Protein exists Light off --> No protein at the moment

6. Problem setting Given two sets of DNA microarrays:  Disease  Control Extract a set of differentially expressed genes  Feature selection for classification  Biological significant features for downstream research

7. Problem setting revisited Given two sets of DNA microarrays:  Disease  Control + fMRI measurements of the two populations Extract a small set of differentially expressed pathogenic-behaving genes  Feature selection for classification  Biological significant features for downstream research

8. Nervous System Diseases Multiple categorizations:  Organic vs. Functional  Anatomic vs. Physiologic  Structural vs. Metabolic Physiologic = molecular pathway  Invisible to (non functional) imaging  Not evident under microscope, no histological markers Anatomic = loss/gain of tissue

9. A Needle in a Haystack Target: Find the one(?) molecule that malfunctions:  Multiple molecular pathways within a neuron  Neuronal interconnection  Cascade/ripple throughout the system Molecule -> Neuron (population) Neuron -> Other neuron Other neuron -> Other molecules Molecules might be in the same neuron population (feedback)  infeasible for standard statistical analysis

10. Aging and AD Cognitive decrease (AD and aging)  Differential – vulnerable vs. resistant  Memory Encoding  Hippocampus Entorhinal Cortex Dentate Gyrus CA subfields Subiculum Common process:  Synaptic Failure leads to:  Cell loss / tangles / plaques Function, not structure!

11. AD Aging Known from postmortem,in- vitro, and fMRI Interconn. Asses all regions together Hippocampus

12. Microarray analysis Differential expression analysis “Blind” analysis Thousands of parameters simultaneously High false positives rate (multiple comparisons, recall FDR) Poor signal-to-noise ratio Usually produce a “list” of differentially expressed genes “list” can be very long (up to hundreds)

13. Statistical Modeling Temporal model  2 nd stage for fMRI Double subtraction With sickness - basal metabolic rate changes as well

14. Multiple Studies Why fMRI and not postmortem?  p.m. biased against earliest (and most discriminatory) stages  Only fMRI can image the cell-sickness stage  EC found to be the primary source of dysfunction in AD What about normal aging?  Age-related changes in the EC matched pathological decline  Age-related changes in the dentate gyrus (DG), and subiculum (SUB), matched normal aging

15. Spatio-Temporal Model How a pathogenic molecule should behave?  Differentially expressed in the EC (vs. no differentially expression in the DG)  Differences between AD and controls should be age independent once EC dysfunction begins it does not worsen across age groups or over time

16. Results 5 Molecules matched the pattern Much less than 100s! Best molecule: VPS35 Part of a complex that connects-to and transports substances within a cell A-beta – a known “smoking gun” for AD Experiments validated:  Low VPS35 --> High A-beta Required neuronal molecules in end-to-end transportation are not transported --> brain dysfunction

17. Conclusion Microarrays – noisy, unfocused results fMRI – imaging in-vivo, not post-mortem Create statistical model (criteria) using fMRI, for microarray differentiation  Lack of specific methods  Not a parametric model, like a thumb rule  Nice example for research advance My personal research is on PD Lots of imaging data Any suggestions? Thanks!