Therapeutic Implications of
Computer Models of Brain Activity
for Alzheimer Disease.

UMK - logo

      Włodzisław Duch  

wduch

Computational Intelligence Laboratory,
Department of Informatics,
Nicolaus Copernicus University,
Grudziądzka 5, 87-100 Toruń, Poland.

WWW: http://www.fizyka.umk.pl/~duch




Recent Projects of the
Department of Informatics



Computational Intelligence (CI)

Cognitive Science

Applications

Some papers are in our on-line archive:

WWW: http://www.fizyka.umk.pl/kmk/publications.html


Plan

  1. Computational psychiatry

  2. Methods and applications

  3. Alzheimer disease

  4. Competing hypothesis

  5. Therapeutic implications

  6. Future plans


Computational psychiatry


  1. Medicine and psychiatry - experimental, phenomenological.
  2. Understanding of causal interactions is difficult using the stimulus-response paradigm.
  3. Neurochemistry/psychopharmacology: answers only to what happens if, not how.
  4. Different levels of organization.
  5. Different questions at different levels.

History.

“Neural network models offer a better chance of rescuing the study of human psychological responses to drugs than anything else currently available”
(Callaway, Halliday, Naylor, Yano, Herzig, Neuropsychopharmacology 1994).


Methods and applications

Psychiatric models must first perform basic cognitive functions, than pathology is introduced.

Lesioning connections disrupts dynamic mechanisms.

Similar - neurological disorders, such as epilepsy, cortical reorganization after stroke, frontal lobe syndromes ... Neuropsychological and cognitive disturbances.
Review: E. Ruppin, Network 6 (1995) 635-656
Some psychiatric and neurological problems that has been modeled so far.

Hopfield model (1982)

Active nodes (neurons)
(V=+1, dark)
Non-active (V=-1, light)

All nodes are connected.
Symmetric synaptic connections Wij=Wji

Stable patterns of excitations = memory traces.

Simplest threshold dynamics:

Point attractors correspond to the minima of:

More sophisticated Hebbian networks have cyclic attractors.
Associative memory:


Alzheimer disease

Global dysfunction at the behavioral level:

Etiology unknown; some forms due to genetic risk.
10% of people over 70 have it.
Biochemical level - responsible for degeneration, but neural network level for behavior.
Drugs slowing down the memory impairment - increase acetylocholine realease.

At the neuron level:

Neural mechanisms.

Synaptic deletion - reduction of the number of synapses;
Synaptic compensation - increase in the size of the remaining ones.

Types of questions:
How deletion/compensation influences memory deterioration?
What strategies of compensation could maintain memory capacity?

Model of associative memory based on Hopfield-like attractor network (D. Horn et/al. 1993).
Assume that:

Estimation of associative memory capacity - methods developed in statistical physics.
Each pattern has a basin of attraction, patterns similar to those memorized should be correctly recognized.
Memory deterioration is delayed if the remaining connections grow by:

d - level of random synaptic deletion,
k=k(d) is a compensation-strategy parameter, fitted to experimental data.
Different k=k(d) functions lead to a different time course of the disease.
Horn and Ruppin, new hypothesis for the appearence of parkinsonian symptoms in AD patients.

Problem with Hopfield networks - non-local learning, not plausible from neurobiological point of view.

Local Hebbian synaptic storage, attractor network model, Willshaw model (Ruppin & Reggia 1995):
Simulations with 1500 neurons, 75 memory patterns, activity 0.05.
Local compansation, different for each neural module: CiWij
In reality: changes of firing thresholds.

Left to right: no compensation, global and local compension.

Compensation normalizes sizes of the basins of attractors.

Hebbian synaptic modification - learning, memory.
Neuronal activity-dependent compensation mechanism - maintaining.
This model explains:

Local compensation is history dependent
⇒ broad variability in magnitude of degradation for the same cognitive competencies.

AD: failure of normal regulatory (compensatory) mechanism.
Neural tangles result from disruption of cellural transportantion system, hence deficient synaptic compensation.
AD patients without tangles - probably due to excessive synaptic loss.
Coupling between metabolic cellular degeneration and system level effects.

Alternative hypothesis: Hasselmo (1992-1995).
Storing new patterns guided by interference with many old patterns, too many explicit combinations to store.
If external strength is large enough or if internal inhibition strong enough it may be prevented, but beyond critical storage capacity it is unavoidable.

Alzheimer Disease - due to the synaptic runaway?
Decrease of the cortical inhibition
⇒ excess memory storage requirements
⇒ reduced synaptic decay
⇒ pathological growth of synaptic connections
⇒ excessive metabolic demands
⇒ neuronal degradation.

Synaptic loss, lowering synapse/neuron ratio: large cognitive deficits with little structural damage.
Neural loss - large structural damage, faster degradation.

This model explains:

Some evidence of neuro-regulatory processes: pyramidal neurons scale the overall strength of their synaptic connections as a function of their activity.
Search for more experimental evidence in favour of these models.
More detailed models needed but ...


Therapeutic implications

Some suggestions resulting from computational models:

Other attempts to make some therapeutic suggestions: neurological damage.
For example ischemic tissue damage represented by Cortical Spreading Depression Waves with high concentration of K+ ions, during acute stroke.


What needs to be done

This model explains many forms of amnesia.

Computational simulations of more sophisticated models - only quite recently (J. Murre, private information).


Włodzisław Duch