On The Entropy of
Brain Anatomic Regions for
Complex Problem Solving
Gönül Günal Değirmendereli - METU
Sharlene D. Newman - Indiana University
Fatoş T. Yarman Vural - METU
ISBCS’2020
Major Goal
To understand how complex problem-solving processes lead
to changes in information content of anatomic regions.
✓ Measure the information content of anatomic regions
using fMRI data during a complex problem-solving.
✓ Analyze the brain regions activated in two main phases of
the problem-solving process:
✓ Planning: problem representation & solution formulation
✓ Execution: implementation of solution plan
Method for Analysing the Complex Problem Solving
• We propose an information theoretic method based on
Shannon information theory.
✓ The information amount conveyed by a random
signal is inversely proportional to the entropy
measures.
➢ High entropy indicates a low information content.
➢ A low entropy region has high information
content.
Entropy
• Measures the randomness/uncertainty vs. regularity/predictability
of neural operations.
• Greater randomness implies higher entropy,
• Greater predictability implies lower entropy.
✓ A voxel whose BOLD signal varies irregular
over time, gives a high entropy value,
✓ A voxel whose signal varies in a highly
regular pattern, produces a low entropy
value.
Major Assumption
• Our major assumption:
✓ The low entropy brain regions are more intimately
involved in complex problem-solving processes
compared to the high entropy regions.
• We measure the information content by Shannon entropy
✓ At each anatomic region
✓ For each problem-solving phase (Planning & Execution)
✓ For expert and novice players
Tower Of London (TOL) Experiment
•
•
•
•
fMRI data was recorded during the computerized TOL game.
For each puzzle the initial and goal positions were presented
A five or six moves to reach the goal configuration.
Subjects were directed to generate a solution plan prior to
making their first move.
Dynamic Entropy for Low Entropy Region
• The dynamic entropy in a low entropy region
✓ always low during the TOL game, and
✓ always high during the resting state.
❖ The region with high information content requires more organized signal
Right Precuneus
Dynamic Entropy for Low Entropy Region
• The dynamic entropy in another minimum entropy region
Left Superior Parietal
Dynamic Entropy for High Entropy Region
• The dynamic entropy variations in a maximum entropy region
are rather arbitrary. Highly contaminated with noise.
Right Insula
Dynamic Entropy for Planning-Execution-Resting States
2nd set of experiments:
• The behavior of dynamic entropy in the most informative
(low entropy) anatomic regions of expert (successful) and
novice (unsuccessful) players are analyzed.
Expert Players
• The dynamic entropy in the resting state and planning-execution phases
are highly correlated with the experimental setup.
✓ The lowest information content is observed during the resting state in a
low entropy region.
✓ The information content is increased in the region activated by the
problem-solving tasks.
Low entropy region: Right Precuneus
Novice Players
• The dynamic entropy and task relations are more random compared to
the expert players.
✓ The dynamic entropy fluctuations are rather arbitrary during the resting
state and while playing the TOL game, in a low entropy anatomic region.
Low entropy region: Right Lingual
Static Entropy for Planning and Execution Tasks
The entropy for planning phase is lower than the execution phase.
• Since planning requires the participation of numerous cognitive processes, it
would lead to more coherent neural processing than the execution task.
Static Entropy for Experts and Novices
The expert players have lower static entropy values compared to the
novice players
➢ The active regions carry more information for the expert players compared to
the novice players.
Durations of Planning and Execution Phases
• Time spending for planning and
execution phases are differing.
• For the successful runs, the planning
time is longer than the execution time.
4500
➢ If the planning is successful, the execution
task will be completed in a relatively short
time.
➢ If the planning is not complete or accurate,
re-planning is required during execution to
make corrections on the preliminary plan.
2000
4000
3500
3000
2500
1500
1000
500
0
Successful
Runs
PLANNING
Unsuccessful
Runs
EXECUTION
Conclusion
✓ There is high overlap between the low entropy regions and the
active anatomic regions observed by experimental neuroscience,
for the problem-solving task.
✓ Low entropy successfully identifies the active brain regions
which participate the problem-solving task.
✓ Therefore, we conclude that the static and dynamic entropy
measures yield promising information for the identification of
activated regions and the detection of brain states related with
the complex problem-solving process.
✓ This capability would be useful in revealing the hidden cognitive
states of subjects performing a specific cognitive task.
gonul.gunal@metu.edu.tr
sdnewman@indiana.edu
vural@ceng.metu.edu.tr
For more information:
Degirmendereli G., Newman S.D., Yarman Vural F.T. ”On the
Entropy of Brain Anatomic Regions for Complex Problem Solving,”
2019 IEEE 19th Int. Conf. on Bioinformatics and Bioengineering