Application of a Computerized Language Lateralization Index from fMRI by a Group of Clinical Neuroradiologists

Jones, Stephen E.; Mahmoud, Safaa Rashad; González-Martínez, Jorge; Lockwood, Daniel; Moon, Doksu; Smith, Alison S.; Stultz, Todd W.; Tievsky, Andrew L.; Phillips, Micheal D.

doi:10.3174/ajnr.a3271

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Studies comparing quantitative with qualitative assessment of language lateralization have shown that visual inspection by an experienced rater is reliable for presurgical assessment of language lateralization. [29][30][31] This qualitative approach provided a clinically applicable assessment of the cerebrocellebellar relationship in patients with tumor.…”

Section: 23mentioning

confidence: 99%

Crossed Cerebrocerebellar Language Lateralization: An Additional Diagnostic Feature for Assessing Atypical Language Representation in Presurgical Functional MR Imaging

Orellana¹,

Visch-Brink

Vernooij³

et al. 2014

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE:Determining language dominance with fMRI is challenging in patients with brain tumor, particularly in cases of suspected atypical language representation. Supratentorial activation patterns must be interpreted with great care when the tumor is in or near the presumed language areas, where tumor tissue or mass effect can lead to false-negative fMRI results. In this study, we assessed cerebrocerebellar language fMRI lateralization in healthy participants and in patients with brain tumors with a focus on atypical language representation.

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Section: 23mentioning

confidence: 99%

Crossed Cerebrocerebellar Language Lateralization: An Additional Diagnostic Feature for Assessing Atypical Language Representation in Presurgical Functional MR Imaging

Orellana¹,

Visch-Brink

Vernooij³

et al. 2014

AJNR Am J Neuroradiol

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“…Our approach rather aims at standardizing visual interpretation, making it less dependent on rater experience but still applicable in the clinical context, where inferences must be drawn from individual patient's dataset, even at the cost of suboptimal data quality [51]. This is in accordance with studies showing that visual inspection by an experienced rater is a reliable and valuable method to validate and interpret fMRI in a clinical routine setting [26,[52][53][54][55][56][57][58][59]. Gaillard et al [54] compared visual assessment of fMRI with quantitative methods such as the LI and found comparable results.…”

Section: Discussionmentioning

confidence: 85%

Clinical Speech fMRI in Children and Adolescents

Staudt

2021

Clin Neuroradiol

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Purpose In patients with drug-resistant focal epilepsy, surgical resection is often the only treatment option to achieve long-term seizure control. Prior to brain surgery involving potential language areas, identification of hemispheric language dominance is crucial. Our group developed and validated a functional magnetic resonance imaging (fMRI) battery of four pediatric language tasks. The present study aimed at optimizing fMRI data acquisition and analysis using these tasks. Methods We retrospectively analyzed speech fMRI examinations of 114 neuropediatric patients (age range 5.8–17.8 years) who were examined prior to possible epilepsy surgery. In order to evaluate hemispheric language dominance, 1–4 language tasks (vowel identification task VIT, word-chain task WCT, beep-story task BST, synonym task SYT) were measured. Results Language dominance was classified using fMRI activation in the 13 validly lateralizing ROIs (VLR) in frontal, temporal and parietal lobes and cerebellum of the recent validation study from our group: 47/114 patients were classified as left-dominant, 34/114 as bilateral and 6/114 as right-dominant. In an attempt to enlarge the set of VLR, we then compared for each task agreement of these ROI activations with the classified language dominance. We found four additional task-specific ROIs showing concordant activation and activation in ≥ 10 sessions, which we termed validly lateralizing (VLRnew). The new VLRs were: for VIT the temporal language area and for SYT the middle frontal gyrus, the intraparietal sulcus and cerebellum. Finally, in order to find the optimal sequence of measuring the different tasks, we analyzed the success rates of single tasks and all possible task combinations. The sequence 1) VIT 2) WCT 3) BST 4) SYT was identified as the optimal sequence, yielding the highest chance to obtain reliable results even when the fMRI examination has to be stopped, e.g., due to lack of cooperation. Conclusion Our suggested task order together with the enlarged set of VLRnew may contribute to optimize pediatric speech fMRI in a clinical setting.

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“…These might include information about the patient's compliance with the task, movement-associated signal changes (24) and whether a sufficient amount of data was collected (39). Of note, subjective ratings of data quality have been shown to vary strongly between different evaluators (40). Therefore, it seems imperative to advance the development of automated data quality assessments and the understanding of how data quality and measures of lateralisation interact.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the Confidence in fMRI-Based Language Lateralisation Through Laterality Index Deconstruction

et al. 2019

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In epilepsy patients, language lateralisation is an important part of the presurgical diagnostic process. Using task-based fMRI, language lateralisation can be determined by visual inspection of activity patterns or by quantifying the difference in left- and right-hemisphere activity using variations of a basic formula [(L–R)/(L+R)]. However, the values of this laterality index (LI) depend on the choice of activity thresholds and regions of interest. The diagnostic utility of the LI also depends on how its continuous values are translated into categorical decisions about a patient's language lateralisation. Here, we analysed fMRI data from 712 epilepsy patients who performed a verbal fluency task. Each fMRI data set was evaluated by a trained human rater as depicting left-sided, right-sided, or bilateral lateralisation or as being inconclusive. We used data-driven methods to define the activity thresholds and regions of interest used for LI computation and to define a classification scheme that allowed us to translate the LI values into categorical decisions. By deconstructing the LI into measures of laterality (L–R) and strength (L+R), we also modelled the relationship between activation strength and conclusiveness of a data set. In a held-out data set, predictions reached 91% correct when using only conclusive data and 82% when inconclusive data were included. Although only trained on human evaluations of fMRIs, the approach generalised to the prediction of language Wada test results, allowing for significant above-chance accuracies. Compared against different existing methods of LI-computation, our approach improved the identification and exclusion of inconclusive cases and ensured that decisions for the remaining data could be made with consistently high accuracies. We discuss how this approach can support clinicians in assessing fMRI data on a single-case level, deciding whether lateralisation can be determined with sufficient certainty or whether additional information is needed.

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Application of a Computerized Language Lateralization Index from fMRI by a Group of Clinical Neuroradiologists

Cited by 5 publications

References 13 publications

Crossed Cerebrocerebellar Language Lateralization: An Additional Diagnostic Feature for Assessing Atypical Language Representation in Presurgical Functional MR Imaging

Crossed Cerebrocerebellar Language Lateralization: An Additional Diagnostic Feature for Assessing Atypical Language Representation in Presurgical Functional MR Imaging

Clinical Speech fMRI in Children and Adolescents

Quantifying the Confidence in fMRI-Based Language Lateralisation Through Laterality Index Deconstruction

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