Transcript Seminar07_Shaus

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.
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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.
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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
i. Severe or unstable disease other than AD;
ii. Medical or psychiatric disorders that might complicate the assessment of
dementia;
iii. A disability that may prevent the subject from completing all study
requirements (e.g., blindness, deafness, language difficulty);
iv. Recent intake of an investigational drug, drug known to cause major organ
system toxicity, any CNS-active medication, or any recreational drug.
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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
19 leads 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.
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Illustration: Anonymous
ERP subject.
“Vectorizing” subjects:
For each subject, for each electrode channel:
i.
Split the 300 sec of measurements into segments of 1 sec each:
ii. Average the 1 sec segments:
Average
iii. 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)
(v1, v2, …, v200) Conflating
= (e1, e2, …, e3800)
(w1, w2, …, w200)
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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.
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The distance metric is based on a
“Mahalanobis distance” (norm):
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k “nearest” neighbors of the subject
under investigation are being chosen.
The diagnosis is set according to the
neighbors’ majority type.
(Σ – the covariance matrix.)
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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.
The analysis was performed for
k=1,3,5.
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.
Illustration: Two projections of the same 3D data set.
The analysis was performed for k=1,3,5.
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subject vector
PP+KNN
Selection
PP+KNN
Selection
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.
Illustration: Iterative PP, KNN and sub-space selections.
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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).
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Plot of Sensitivity vs. False Positive Rate
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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.
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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.
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• 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.
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• 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 the test 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.
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