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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Rethinking causality and data complexity in brain lesion-behaviour inference and its implications for

Christoph Sperber¹

¹Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.

Cortex; a Journal Devoted to the Study of the Nervous System and Behavior

|February 17, 2020

Summary

Understanding brain function through lesion-behavior mapping faces challenges due to data complexity. This study highlights how causality and inter-variable dependencies impact lesion-brain inference and post-stroke outcome prediction models.

Keywords:

Machine learning Multivariate lesion behaviour mapping Outcome prediction Stroke Support vector machine

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Area of Science:

Neuroscience
Computational Neuroscience
Medical Imaging

Background:

Lesion-behavior modeling is crucial for mapping human brain functions and predicting post-stroke outcomes.
High-dimensional lesion data presents significant challenges for accurate mapping and prediction.

Purpose of the Study:

To reframe the complexity of lesion data using causal and non-causal dependencies.
To analyze the implications of this complexity for various data modeling approaches.
To challenge common strategies in lesion-behavior mapping and outcome prediction.

Main Methods:

Theoretical discussion of causal and non-causal dependencies in lesion data.
Empirical examination of common modeling strategies.
Analysis of multivariate models and causality-blind algorithms.

Main Results:

Lesion-behavior inference is susceptible to a lesion-anatomical bias, unaffected by multivariate or causality-blind models.
Multivariate lesion-brain inference is valuable when considering functional relationships between brain areas.
Data dependencies offer strategies for data reduction to improve post-stroke outcome prediction.

Conclusions:

Causality must be considered in lesion-behavior mapping to avoid invalid inferences and potential paradoxical effects.
Understanding inter-variable dependencies is key to improving lesion-behavior models.
Accurate evaluation of model quality requires accounting for non-topographical causal predictors in post-stroke behavior.