Latent disconnectome prediction of long-term cognitive symptoms in stroke

Abstract

Stroke significantly impacts quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need for a better prediction of long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnections-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace to predict neuropsychological scores one year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights available as the first comprehensive atlas of disconnectome-deficit relations across 86 neuropsychological scores. Out-of-sample prediction derived from this atlas achieved average accuracy over 80%, which is higher than any other framework. Our novel predictive framework is available as an interactive web application, the disconnectome symptoms discoverer (http://disconnectomestudio.bcblab.com), to provide the foundations for a new and practical approach to modelling cognition in stroke. Our atlas and web application will reduce the burden of cognitive deficits on patients, their families, and wider society while also helping to tailor personalized treatment programs and discover new targets for treatments. We expect the range of assessments and the predictive power of our framework to increase even further through future crowdsourcing.

Preserved Corticospinal Tract Revealed by Acute Perfusion Imaging Relates to Better Outcome After Thrombectomy in Stroke

“…Finally, there is growing evidence supporting the assumption, that the individual disruption of white matter tracts by the subacute and chronic stroke lesion, that is, disconnectivity, highly determines the outcome in the motor 39 or cognitive domain. 40,41 Furthermore, the affection and preservation of white matter pathways following intravenous thrombolysis are associated with the recovery beyond the effect of lesion growth alone 42 ; thus, the measurement of salvaged structural connectivity contains information on treatment effect in addition to lesion volume.…”

Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain

“…Associating functions to RSNs is usually done by indirect inference, using their spatial maps and contrasting them with fMRI-derived activation maps of specific cognitive functions 2 . As lesion studies have historically been a major tool in determining functions of grey matter area 57 , and more recently of white matter pathways 58 , WhiteRest provides a new tool to understand the link between cognition and resting-state networks. Reversely, the WhiteRest integrated functional and structural connectivity can shed light on the functional mechanisms of the brain and the origins of cognitive disorders.…”

“…While promising results link stroke symptoms and RSNs in our study, further investigations will be required to fully disentangle the relationship between cognition (or cognitive deficits) and RSNs, using more advanced models than the relatively simple linear approach from the present study. Recent works have been undertaking the prediction of symptoms and recovery from stroke based on functional and structural data 58,59 , a very important and interesting goal for which WhiteRest may eventually be of use, adding interpretable data to these multimodal methods.…”

Unravelling the fabric of the human mind: the brain-cognition space

“…The recent blossoming of large dataset studies and the introduction of the meta-analytic approach allow for scrutinising the consistency of neuroimaging results [13][14][15][16][17] and reducing the likelihood of false positive errors 18 . The advent of new techniques such as dimensionality embedding allows the visualisation of the complexity inherent in large data by reducing its dimension, allowing probing of the neural cognitive systems as a whole 19,20 . Dimensionality reduction applied to neuroimaging data indicates that a few embedding components can describe the underlying pattern of functional activation in the brain 21 .…”