Voxel-Based Lesion Symptom Mapping

Baldo, Juliana V.; Ivanova, Maria V.; Herron, Timothy J.; Wilson, Stephen M.; Dronkers, Nina F.

doi:10.1007/978-1-0716-2225-4_5

Cited by 7 publications

(3 citation statements)

References 114 publications

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“…When examining the relationship between the brain and behavior, researchers have relied on lesion information for over 200 years ( Baldo et al, 2022 ). Although powerful and highly informative, this approach limits the search for relationships to only the areas where lesions occur.…”

Section: Discussionmentioning

confidence: 99%

ASL MRI informs blood flow to chronic stroke lesions in patients with aphasia

et al. 2023

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Introduction: Response to post-stroke aphasia language rehabilitation is difficult to anticipate, mainly because few predictors can help identify optimal, individualized treatment options. Imaging techniques, such as Voxel-based Lesion Symptom Mapping have been useful in linking specific brain areas to language behavior; however, further development is required to optimize the use of structural and physiological information in guiding individualized treatment for persons with aphasia (PWA). In this study, we will determine if cerebral blood flow (CBF) mapped in patients with chronic strokes can be further used to understand stroke-related factors and behavior.Methods: We collected perfusion MRI data using pseudo-Continuous Arterial Spin Labeling (pCASL) using a single post-labeling delay of 2,200 ms in 14 chronic PWA, along with high-resolution structural MRI to compute maps of tissue damage using Tissue Integrity Gradation via T2w T1w Ratio (TIGR). To quantify the CBF in chronic stroke lesions, we tested at what point spatial smoothing should be applied in the ASL analysis pipeline. We then related CBF to tissue damage, time since stroke, age, sex, and their respective cross-terms to further understand the variability in lesion CBF. Finally, we assessed the feasibility of computing multivariate brain-behavior maps using CBF and compared them to brain-behavior maps extracted with TIGR MRI.Results: We found that the CBF in chronic stroke lesions is significantly reduced compared to its homologue grey and white matter regions. However, a reliable CBF signal (although smaller than expected) was detected to reveal a negative relationship between CBF and increasing tissue damage. Further, the relationship between the lesion CBF and age, sex, time since stroke, and tissue damage and cross-terms suggested an aging-by-disease interaction. This relationship was strongest when smoothing was applied in the template space. Finally, we show that whole-brain CBF relates to domain-general visuospatial functioning in PWA. The CBF-based brain-behavior maps provide unique and complementary information to structural (lesion-based) brain-behavior maps.Discussion: Therefore, CBF can be detected in chronic stroke lesions using a standard pCASL MRI acquisition and is informative at the whole-brain level in identifying stroke rehabilitation targets in PWAs due to its relationship with demographic factors, stroke-related factors, and behavior.

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Section: Discussionmentioning

confidence: 99%

ASL MRI informs blood flow to chronic stroke lesions in patients with aphasia

et al. 2023

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“…We then conducted CLSM (Gleichgerrcht, Fridriksson, Rorden, & Bonilha, 2017) using restingstate functional connectivity based on the AICHA atlas, including all left-to-left, left-to-right, and right-to-right connections in the analysis. Only voxels (or regions for RLSM) where at least 5 patients had damage were considered, based on the minimum overlap recommendation of 10% of the patient sample (Baldo, Ivanova, Herron, & et al, 2022). All tests were two-tailed, with α = 0.05, and significance was determined via permutation testing, where stability of pvalue were tested in increments of 1000 permutations, ranging from 1,000 permutation to 10,000 permutations.…”

Section: Comparison With Standard Lesion Symptom Mappingmentioning

confidence: 99%

Network-based Statistics Distinguish Anomic and Broca Aphasia

Zhao,

Riccardi,

Desai

et al. 2023

Preprint

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Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. Due to the complexity of speech-language processing, the neural mechanisms that underpin various symptoms between different types of aphasia are still not fully understood. We used the network-based statistic method to identify distinct subnetwork(s) of connections differentiating the resting-state functional networks of the anomic and Broca groups. We identified one such subnetwork that mainly involved the brain regions in the premotor, primary motor, primary auditory, and primary sensory cortices in both hemispheres. The majority of connections in the subnetwork were weaker in the Broca group than the anomic group. The network properties of the subnetwork were examined through complex network measures, which indicated that the regions in the superior temporal gyrus and auditory cortex bilaterally exhibit intensive interaction, and primary motor, premotor and primary sensory cortices in the left hemisphere play an important role in information flow and overall communication efficiency. These findings underlied articulatory difficulties and reduced repetition performance in Broca aphasia, which are rarely observed in anomic aphasia. This research provides novel findings into the restingstate brain network differences between groups of individuals with anomic and Broca aphasia. We identified a subnetwork of, rather than isolated, connections that statistically differentiate the resting-state brain networks of the two groups, in comparison with standard lesion symptom mapping results that yield isolated connections.

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“…2,3 The principle that location of brain damage can reveal causal information about where cognitive processes are implemented in the brain continues to be productive for cognitive neuroscience and there is a growing number of studies leveraging lesion-symptom mapping (LSM) methods. [4][5][6] Unlike older lesion mapping approaches that evaluated the locus of lesion overlap, modern LSM studies employ more sophisticated statistical analyses to objectively identify voxels that are consistently damaged in individuals with a specific impairment yet spared in those without the symptom. 7 Traditional LSM is conducted using a mass-uni-variate approach in which damage to each voxel (or region) of the brain is independently tested for its association with a given impairment.…”

Section: Introductionmentioning

confidence: 99%

Stable multivariate lesion symptom mapping

Teghipco,

Newman-Norlund,

Gibson

et al. 2024

Aperture Neuro

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Multivariate lesion-symptom mapping (MLSM) considers lesion information across the entire brain to predict impairments. The strength of this approach is also its weakness—considering many brain features together synergistically can uncover complex brain-behavior relationships but exposes a high-dimensional feature space that a model is expected to learn. Successfully distinguishing between features in this landscape can be difficult for models, particularly in the presence of irrelevant or redundant features. Here, we propose stable multivariate lesion-symptom mapping (sMLSM), which integrates the identification of reliable features with stability selection into conventional MLSM and describe our open-source MATLAB implementation. Usage is showcased with our publicly available dataset of chronic stroke survivors (N=167) and further validated in our independent public acute stroke dataset (N = 1106). We demonstrate that sMLSM eliminates inconsistent features highlighted by MLSM, reduces variation in feature weights, enables the model to learn more complex patterns of brain damage, and improves model accuracy for predicting aphasia severity in a way that tends to be robust regarding the choice of parameters for identifying reliable features. Critically, sMLSM more consistently outperforms predictions based on lesion size alone. This advantage is evident starting at modest sample sizes (N>75). Spatial distribution of feature importance is different in sMLSM, which highlights the features identified by univariate lesion symptom mapping while also implicating select regions emphasized by MLSM. Beyond improved prediction accuracy, sMLSM can offer deeper insight into reliable biomarkers of impairment, informing our understanding of neurobiology.

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Voxel-Based Lesion Symptom Mapping

Cited by 7 publications

References 114 publications

ASL MRI informs blood flow to chronic stroke lesions in patients with aphasia

ASL MRI informs blood flow to chronic stroke lesions in patients with aphasia

Network-based Statistics Distinguish Anomic and Broca Aphasia

Stable multivariate lesion symptom mapping

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