Probabilistic machine learning for the evaluation of presurgical language dominance

Tomer Gazit Functional Brain Center,

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 PhD
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Fani Andelman Functional Neurosurgery Unit, Department of Neurosurgery and

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Yifat Glikmann-Johnston Functional Brain Center,

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Tal Gonen Functional Brain Center,
School of Psychological Sciences and

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 MA
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Aliya Solski Functional Brain Center,

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Irit Shapira-Lichter Functional Brain Center,
Department of Neurology, Tel Aviv Souraksy Medical Center;

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Moran Ovadia Functional Brain Center,

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Svetlana Kipervasser Functional Neurosurgery Unit, Department of Neurosurgery and
Department of Neurology, Tel Aviv Souraksy Medical Center;
Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; and

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Miriam Y. Neufeld Department of Neurology, Tel Aviv Souraksy Medical Center;
Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; and

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Itzhak Fried Functional Neurosurgery Unit, Department of Neurosurgery and
Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; and
Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, California
Sagol School of Neuroscience,

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Talma Hendler Functional Brain Center,
Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; and
School of Psychological Sciences and
Sagol School of Neuroscience,

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Daniella Perry Functional Brain Center,

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OBJECTIVE

Providing a reliable assessment of language lateralization is an important task to be performed prior to neurosurgery in patients with epilepsy. Over the last decade, functional MRI (fMRI) has emerged as a useful noninvasive tool for language lateralization, supplementing or replacing traditional invasive methods. In standard practice, fMRI-based language lateralization is assessed qualitatively by visual inspection of fMRI maps at a specific chosen activation threshold. The purpose of this study was to develop and evaluate a new computational technique for providing the probability of each patient to be left, right, or bilateral dominant in language processing.

METHODS

In 76 patients with epilepsy, a language lateralization index was calculated using the verb-generation fMRI task over a wide range of activation thresholds (from a permissive threshold, analyzing all brain regions, to a harsh threshold, analyzing only the strongest activations). The data were classified using a probabilistic logistic regression method.

RESULTS

Concordant results between fMRI and Wada lateralization were observed in 89% of patients. Bilateral and right-dominant groups showed similar fMRI lateralization patterns differentiating them from the left-dominant group but still allowing classification in 82% of patients.

CONCLUSIONS

These findings present the utility of a semi-supervised probabilistic learning approach for presurgical language-dominance mapping, which may be extended to other cognitive domains such as memory and attention.

ABBREVIATIONS

EPI = echo-planar imaging; fMRI = functional MRI; LDP = language dominance probability; LI = lateralization index; SPGR = spoiled gradient; TASMC = Tel Aviv Sourasky Medical Center.
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