RISK ANALYSIS AND DECISION MAKING IN NEUROSURGERY: BRAIN MAPPING STRUCTURAL INVARIANT OF COGNITIVE FUNCTIONS FOR SURGICAL PLANNING

Monday, October 24, 2011
Grand Ballroom AB (Hyatt Regency Chicago)
Poster Board # 18
(ESP) Applied Health Economics, Services, and Policy Research

Candidate for the Lee B. Lusted Student Prize Competition


Olena Nikolenko, M.D., Ph.D., Tufts Medical Center, Boston, MA, Lusiena Klaupik, M.D., Ph.D., Odessa National Medical University, Odessa, Ukraine and Oleg Nikolenko, PhD, Odessa National University, Odessa, Ukraine

Purpose: to increase cost-effectiveness of neurosurgical decision making and incorporation of patient preferences for surgeries in eloquent cortex. Planning of neurologic surgery is based on the current characteristics of the patient’s state, the functional significance of the involved brain areas and the form of surgical intervention. Risk – is an inherent property of neurosurgery. It is associated with decrease numbers of successful surgeries and probably will not get the expected outcome. Traditional selection and interpretation of cognitive task don’t have a “capacity” to predict all possible neurocognitive outcomes of surgeries in eloquent cortex. Effective risk analysis and decision making in neurosurgery can be achieved by brain mapping of the structural invariant of cognitive functions.

Method: The methodology of risk analysis in neurosurgery involves statistical analysis, probability concepts, analysis of sensitivity and scenario analysis of neurological surgeries. The result of risk analysis is expressed as a probability distribution of possible values of main variables of neurosurgical planning. Functional magnetic resonance imaging (fMRI), intraoperative electrophysiologic testing (ECS), intracranial electroencephalography (iEEG) and magnetoencephalography (MEG) for surgical planning have been directed at identifying eloquent brain areas associated with cognitive functions such as language, attention, memory, executive functions. This serves as methodological bases for classification system of cognitive tasks for surgical brain mapping and relevant neurosurgical decision making. Language reflexes structure of cognitive functions by categories such as objects, properties and relations. They organize the structural invariants of cognition and can be tested with cognitive tasks. So brain mapping could be associated with neurocognitive competence to operate separately with objects, properties or relations during cognitive tasks.

Result: Neurocognitive dysfunctions and relevant neurosurgical risks can be divided into three groups: substrate (objects), attributive (properties) and relational (relations). This approach discovers new opportunities for more effective decision making strategies in neurosurgery by brain mapping structural invariants of cognitive function for surgical planning.

Conclusion: Brain mapping and cognitive tasks can discover the conflict between processes of operation with objects, properties or relations that could be an evidence of neurocognitive dysfunction. Resolution of conflicts between objects, properties, relations could decrease neurosurgical risks, increase the cost-effectiveness of neurosurgery and improve incorporation of patient preferences into neurosurgical decision making.