title: The value and cost of complexity in predictive modelling: role of tissue anisotropic conductivity and fibre tracts in neuromodulation.
abstract: OBJECTIVE
  Computational methods are increasingly used to optimize transcranial direct current stimulation (tDCS) dose strategies and yet complexities of existing approaches limit their clinical access. Since predictive modelling indicates the relevance of subject/pathology based data and hence the need for subject specific modelling, the incremental clinical value of increasingly complex modelling methods must be balanced against the computational and clinical time and costs. For example, the incorporation of multiple tissue layers and measured diffusion tensor (DTI) based conductivity estimates increase model precision but at the cost of clinical and computational resources. Costs related to such complexities aggregate when considering individual optimization and the myriad of potential montages. Here, rather than considering if additional details change current-flow prediction, we consider when added complexities influence clinical decisions.
  
  
  METHODS
  Towards developing quantitative and qualitative metrics of value/cost associated with computational model complexity, we considered field distributions generated by two 4 × 1 high-definition montages (m1 = 4 × 1 HD montage with anode at C3 and m2 = 4 × 1 HD montage with anode at C1) and a single conventional (m3 = C3-Fp2) tDCS electrode montage. We evaluated statistical methods, including residual error (RE) and relative difference measure (RDM), to consider the clinical impact and utility of increased complexities, namely the influence of skull, muscle and brain anisotropic conductivities in a volume conductor model.
  
  
  RESULTS
  Anisotropy modulated current-flow in a montage and region dependent manner. However, significant statistical changes, produced within montage by anisotropy, did not change qualitative peak and topographic comparisons across montages. Thus for the examples analysed, clinical decision on which dose to select would not be altered by the omission of anisotropic brain conductivity.
  
  
  CONCLUSIONS
  Results illustrate the need to rationally balance the role of model complexity, such as anisotropy in detailed current flow analysis versus value in clinical dose design. However, when extending our analysis to include axonal polarization, the results provide presumably clinically meaningful information. Hence the importance of model complexity may be more relevant with cellular level predictions of neuromodulation.
authors.affiliation: Research Center for Modeling and Simulation, National University of Sciences and Technology, Islamabad, Pakistan. School of Mechanical and Electrical Engineering, University of Southern Queensland, Toowoomba, QLD, Australia.
authors.forename: Syed Salman
authors.initials: SS
authors.lastname: Shahid
authors.forename: Marom
authors.initials: M
authors.lastname: Bikson
authors.forename: Humaira
authors.initials: H
authors.lastname: Salman
authors.forename: Peng
authors.initials: P
authors.lastname: Wen
authors.forename: Tony
authors.initials: T
authors.lastname: Ahfock
mesh_terms: D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods; D004553:Electric Conductivity; D006257:Head / Q000033:anatomy & histology / Q000502:physiology; D006801:Humans; D008953:Models, Anatomic; D008959:Models, Neurological; D009413:Nerve Fibers, Myelinated / Q000502:physiology* / Q000648:ultrastructure*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D065908:Transcranial Direct Current Stimulation / Q000379:methods*