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High-dimensional therapeutic inference in the focally damaged human brain.

ABSTRACT: See Thiebaut de Schotten and Foulon (doi:10.1093/brain/awx332) for a scientific commentary on this article.Though consistency across the population renders the extraordinarily complex functional anatomy of the human brain surveyable, the inverse inference-from common functional maps to individual behaviour-is constrained by marked individual deviation from the population mean. Such inference is fundamental to the evaluation of therapeutic interventions in focal brain injury, where the impact of an induced structural change in the brain is quantified by its behavioural consequences, inevitably refracted through the lens of lesion-outcome relations. Current therapeutic evaluations do not incorporate inferences to the individual outcome derived from a detailed specification of the lesion anatomy, relying only on reductive parameters such as lesion volume and crudely discretised location. Examining 1172 patients with anatomically registered focal brain lesions, here we show that such low-dimensional models are highly insensitive to therapeutic effects. In contrast, high-dimensional models supported by machine learning dramatically improve sensitivity by leveraging complex individuating patterns in the functional architecture of the brain. The failure to replicate in humans positive interventional effects in experimental animals is thus revealed to have a remediable inferential cause, forcing a radical re-evaluation of therapeutic inference in the human brain.

SUBMITTER: Xu T

PROVIDER: S-EPMC5837627 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant. Positively, we show that novel machine learning techniques employing high-dimensional inference can nonetheless accurately converge on the true locus. We conclude that current inferences about human brain function and deficits based on lesion mapping must be re-evaluated with methodology that adequately captures the high-dimensional structure of lesion data.

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High-dimensional therapeutic inference in the focally damaged human brain.

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