Identification of Diagnostic Evoked Response Potential Segments in Alzheimer’s Disease

1. Identification of Diagnostic Evoked Response Potential Segments in Alzheimer’s Disease Benvenuto, Jin, Casale, Lynch, Granger, Experimental Neurology 176, 269-276 (2002)

2. Background • EEG coherence is thought to be a measure of regional cortical synchronization and possibly the functional status of intracortical communication. • Since the early, and especially in the late 90s, a number of quantitative EEG (mostly coherence) measures have been used in attempt to identify physiological correlates of the cognitive changes found on early stages Alzheimer’s disease: • Slowing of spectral EEG predicts the rate of subsequent cognitive and functional decline in patients with AD (multiple linear regression analysis). • Patients with AD had significantly lower intra- and interhemispheric coherence than controls in the alpha and beta frequency bands. AD patients, particularly those with severe cognitive impairments, have reduced alpha band coherence in temporo-parieto-occipital areas. • Further evidence linking coherence to the evolution of AD comes from results suggesting that patients homozygous for the Apo-E epsilon4 allele, a predisposing condition for sporadic Alzheimer’s, have particularly reduced bilateral coherence in select cortical fields. • Other EEG indices (such as the EEG complexity) are reported to discriminate AD from normal controls or other types of dementia, as well as to differentiate subgroups among AD patients.

3. The novelty The evolved method uses projection pursuit algorithms to search for differentially diagnostic segments within the time locked signals, with correlated co-occurrences of segments used as composite features in classification. Because time-locked signals are required, evoked response potentials (ERPs) to photic driving were used in the studies instead of free running EEG. The results indicate that application of iterative projection pursuit to ERPs can be used to recognize AD with a high degree of accuracy.

4. Methods • Patient Population • 15 AD patients: unhospitalized, unmedicated, aged 76.2 ± 5.7 years. The severity of illness was limited to mild or moderate, based on a screening tests. • Normal Controls • 17 normal subjects matched for age, gender, and education level were recruited from the community. Each subject was interviewed by a research psychiatrist rule out AD and other psychiatric diagnoses. • Exclusion Criteria • Severe or unstable disease other than AD; • Medical or psychiatric disorders that might complicate the assessment of dementia; • A disability that may prevent the subject from completing all study requirements (e.g., blindness, deafness, language difficulty); • Recent intake of an investigational drug, drug known to cause major organ system toxicity, any CNS-active medication, or any recreational drug.

5. Methods Apparatus Electrode cap, amplifier system, PC, bright light source. ERPs were collected from 19 sites on the skull through scalp electrodes embedded in a tight-fitting meshwork cap. Procedures Subjects were acclimated to the apparatus for 5 min during which time the quality of each of the 19leads was checked. Once normal voltage EEG was recorded from all sites, stimuli (visual light flashes) were presented at 60 per minute (1 Hz) for a session of 5 min in duration, and continuous ERPs were collected. Illustration: Anonymous ERP subject.

6. Methods • “Vectorizing” subjects: • For each subject, for each electrode channel: • Split the 300 sec of measurements into segments of 1 sec each: • Average the 1 sec segments: Average • Bin the resulting average into 5-ms segments (resulting in 200 values): Binning (v1, v2, …, v200) The values from the 19 channels conflated into 1 vector (of 3800 elements): (u1, u2, …, u200) (u1, …, u200, v1, …, v200, w1, …, w200) = (e1, e2, …, e3800) Conflating (v1, v2, …, v200) (w1, w2, …, w200)

7. Methods • Analyses • After a single vector was constructed for each subject, jackknife analyses were run, in which all the subject records but one were used as matching data, and the remaining subject was tested to see which category (Alzheimer’s or control) that subject would be placed in. Three classification algorithms with three parameter settings (k=1,3,5) were used: • k-nearest-neighbors analysis. • Projection pursuit analysis. • Extended projection pursuit analysis.

8. Methods k-nearest-neighbors k “nearest” neighbors of the subject under investigation are being chosen. The diagnosis is set according to the neighbors’ majority type. The distance metric is based on a “Mahalanobis distance” (norm): C C C C A C C C A ? A A A C (Σ – the covariance matrix.) A A The analysis was performed for k=1,3,5. Illustration: The k here is 3. The subject under investigation will be classified as “Control”. In reality, the number of dimensions is 3800, not 2.

9. Methods Projection pursuit Subsets of the subject-vectors were randomly generated, focusing on a “few” voltage values out of 3800. The subsets are equivalent to a “projection” on a lower-dimension space. In each subspace, k nearest neighbor was performed as before. Votes for Alzheimer’s versus Control were tallied across all subspaces, and the resulting majority classification was used as a diagnosis. The analysis was performed for k=1,3,5. Illustration: Two projections of the same 3D data set.

10. Methods Extended projection pursuit The previous “projection pursuit” procedure was performed. Based on its findings, the most predictive subspaces were selected and the process performed again; this iterative compilation of subspaces continued until all subspaces chosen were more predictive than a preselected threshold amount. The resulting majority classification was used as a diagnosis. The analysis was performed for k=1,3,5. subject vector PP+KNN Selection PP+KNN Selection Illustration: Iterative PP, KNN and sub-space selections.

11. Results Discrimination of AD from control The sensitivity statistic gives attention to the rate of the AD patients diagnosed: • For k nearest neighbors (k=1,3,5), the sensitivity does not exceed 25%. The maximum false positive rate is 12%. • For projection pursuit methods, the best sensitivity is 75%, with corresponding 29% false positive rate. • For all extended projection pursuit methods, the sensitivity is 100%, with a false positive rate of 6.1% (one subject).

12. Results Plot of Sensitivity vs. False Positive Rate

13. Results Temporal Location of Predictive Features in the ERP The predictiveness is defined as the percentage of times the segment is used in correct prediction. The following “typical” table represents the predictiveness of 100-ms segments originating from C4 electrode.

14. Results Spatial and Frequency Location of Predictive Features The following figure shows the relative power in each of four frequency bands (delta, theta, alpha, beta) for averages of Alzheimer’s and matched controls, plotted across the 19 electrodes of the headset apparatus.

15. Authors' Summary • Extended pursuit projection identifies correlates of AD in ERPs elicited by simple visual stimuli (sensitivity of 100%, false positive rate of 6%). • The most distinctive features occurred within two temporal segments (200 and 400 ms and from 800 to 1000 ms) and arose from fronto-parietal recording sites. • There is prior evidence indicating that correlates of mild AD are found within these spatio-temporal coordinates. Although there was evidence that simple learning contributed to the observed differentiation of the AD group, the unstructured stimuli used in the study have the disadvantage of not activating cognitive activities thought to be impaired by AD. • Between-group differences could be enhanced, and probably markedly so, with paradigms that engage attention to novelty or working memory. On the other hand, unstructured cues have the important advantages of test simplicity and applicability across patient populations.

16. Conclusions and criticism • The number of subjects is small, the number of methods tried is at least 9. • The “projection pursuit” procedure is unclear. What does it mean “randomly generated subspaces”, “few voltage values”? • The presented “projection pursuit” method resembles deduction based on “bagging” (or “majority voting”) rule. There is no attempt at “projection pursuit” optimization via index/objection functions (such as the ones suggested by Friedman-Tukey; Jones-Sibson; Intrator-Cooper, Hebb-Oja…). The only article quoted on the subject of projection pursuit is from 1985. • It is implied that the same “significant spaces” are shared by and calculated across the different jackknifed subjects, which means that thetest subjects are influencing the results! If that is the case, this involves a risk of circular reasoning. • And yet… the potential seems to be there (the results may be ok, even if their derivation was problematic.). A more elaborate projection pursuit, or other clustering methods carefully applied might yield more founded results.

17. Time for Questions?!

18. Thank You.