Examining the multifactorial nature of a cognitive process using Bayesian brain-behavior modeling

Chen, Rong; Herskovits, Edward H.

doi:10.1016/j.compmedimag.2014.05.001

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…An analysis becomes anatomically multivariate where the statistical model incorporates many anatomical variables, indexing the presence or absence of damage to different parts of the brain together ( Chen et al, 2008 ; Chen and Herskovits, 2015 ; Keinan et al, 2004 ; Mah et al, 2014 ; Rondina et al, 2016 ; Smith et al, 2013 ; Toba et al, 2017 ; Yourganov et al, 2016 ; Zhang et al, 2014 ). The dimensionality of such models depends on the number of such variables and their properties.…”

Section: The Dimensionality Of Anatomical Inference In the Brainmentioning

confidence: 99%

The dimensionalities of lesion-deficit mapping

Xu¹,

Jha

Nachev

2018

Neuropsychologia

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Lesion-deficit mapping remains the most powerful method for localising function in the human brain. As the highest court of appeal where competing theories of cerebral function conflict, it ought to be held to the most stringent inferential standards. Though at first sight elegantly transferable, the mass-univariate statistical framework popularized by functional imaging is demonstrably ill-suited to the task, both theoretically and empirically. The critical difficulty lies with the handling of the data's intrinsically high dimensionality. Conceptual opacity and computational complexity lead lesion-deficit mappers to neglect two distinct sets of anatomical interactions: those between areas unified by function, and those between areas unified by the natural pattern of pathological damage. Though both are soluble through high-dimensional multivariate analysis, the consequences of ignoring them are radically different. The former will bleach and coarsen a picture of the functional anatomy that is nonetheless broadly faithful to reality; the latter may alter it beyond all recognition. That the field continues to cling to mass-univariate methods suggests the latter problem is misidentified with the former, and that their distinction is in need of elaboration. We further argue that the vicious effects of lesion-driven interactions are not limited to anatomical localisation but will inevitably degrade purely predictive models of function such as those conceived for clinical prognostic use. Finally, we suggest there is a great deal to be learnt about lesion-mapping by simulation-based modelling of lesion data, for the fundamental problems lie upstream of the experimental data themselves.

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Section: The Dimensionality Of Anatomical Inference In the Brainmentioning

confidence: 99%

The dimensionalities of lesion-deficit mapping

Xu¹,

Jha

Nachev

2018

Neuropsychologia

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Machine learning in medical imaging

Shen

Zhang

et al. 2015

Computerized Medical Imaging and Graphics

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Ten problems and solutions when predicting individual outcome from lesion site after stroke

2017

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In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients.

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Examining the multifactorial nature of a cognitive process using Bayesian brain-behavior modeling

Cited by 3 publications

References 39 publications

The dimensionalities of lesion-deficit mapping

The dimensionalities of lesion-deficit mapping

Machine learning in medical imaging

Ten problems and solutions when predicting individual outcome from lesion site after stroke

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