Unravelling the Neural Basis of Spatial Delusions After Stroke

Abstract

Objective Knowing explicitly where we are is an interpretation of our spatial representations. Reduplicative paramnesia is a disrupting syndrome in which patients present a firm belief of spatial mislocation. Here, we studied the largest sample of patients with delusional misidentifications of space (ie, reduplicative paramnesia) after stroke to shed light on their neurobiology. Methods In a prospective, cumulative, case‐control study, we screened 400 patients with acute right‐hemispheric stroke. We included 64 cases and 233 controls. First, lesions were delimited and normalized. Then, we computed structural and functional disconnection maps using methods of lesion‐track and network‐mapping. The maps were compared, controlling for confounders. Second, we built a multivariate logistic model, including clinical, behavioral, and neuroimaging data. Finally, we performed a nested cross‐validation of the model with a support‐vector machine analysis. Results The most frequent misidentification subtype was confabulatory mislocation (56%), followed by place reduplication (19%), and chimeric assimilation (13%). Our results indicate that structural disconnection is the strongest predictor of the syndrome and included 2 distinct streams, connecting right fronto‐thalamic and right occipitotemporal structures. In the multivariate model, the independent predictors of reduplicative paramnesia were the structural disconnection map, lesion sparing of right dorsal fronto‐parietal regions, age, and anosognosia. Good discrimination accuracy was demonstrated (area under the curve = 0.80 [0.75–0.85]). Interpretation Our results localize the anatomic circuits that may have a role in the abnormal spatial‐emotional binding and in the defective updating of spatial representations underlying reduplicative paramnesia. This novel data may contribute to better understand the pathophysiology of delusional syndromes after stroke. ANN NEUROL 2021;89:1181–1194

Voxelwise structural disconnection mapping: Methodological validation and recommendations

“…Thresholding these disconnectome maps (i.e. keeping values ≥ 50 % and discarding values < 50 %) as performed in several prior studies ( Alves et al, 2021 , Foulon et al, 2018 , Wiesen et al, 2020 ) and enabled by default in BCBtoolkit leads to significantly reduced variance for disconnection probabilities < 50 %. This leads to heteroscedasticity in the modelling errors in general linear models and is associated with inflated FWE rates in permutation testing ( Huang et al, 2006 ).…”

“…This approach is available within the BCBtoolkit ( https://www.toolkit.bcblab.com ) and has been increasingly used in the recent years (e.g. Alves et al, 2021 , Pacella et al, 2019 , Salvalaggio et al, 2020 , Wiesen et al, 2020 ). It relies on deterministic fiber tracking seeding from individual lesions ( Thiebaut de Schotten et al, 2011 ).…”

“…In some studies, this map is again thresholded at ≥ 50 % (e.g. Alves et al, 2021 , Foulon et al, 2018 , Wiesen et al, 2020 ). The result is one disconnectome map per patient.…”

“…The result is one disconnectome map per patient. Second level statistics in terms of an association with a symptom of interest are performed either using an algorithm called AnaCOM ( Foulon et al, 2018 ), within the framework of the general linear model using permutation tests and threshold-free cluster enhancement ( Alves et al, 2021 , Monai et al, 2020 , Pacella et al, 2019 ) or using multivariate methods ( Salvalaggio et al, 2020 , Weaver et al, 2021 , Wiesen et al, 2020 ). An alternative to the voxelwise approach is to examine disconnection on the level of macroscopic tracts from white matter atlases ( Foulon et al, 2018 ).…”

White matter variability, cognition, and disorders: a systematic review

“…Consequently, white matter networks are subject to variations over the lifespan and can change with training (Scholz et al 2009 ; Thiebaut de Schotten et al 2012 ; Lebel et al 2019 ; Vanderauwera et al 2018 ). Tractography has been shown to be highly sensitive in capturing these variations, which can be associated with inter-individual differences in neuropsychological measures in the healthy population (e.g., Catani et al 2007 ; Thiebaut de Schotten et al 2011a , b ; Howells et al 2018 ) and clinical groups (e.g., Forkel et al 2014a , b ; Forkel et al 2020 ; Thompson et al 2017 ; Pacella et al 2019 ; Alves et al 2021 ). Therefore, tractography can be used to study variability in the human brain and map functional white matter correlates.…”