Contribution of White Matter Fiber Bundle Damage to Language Change After Surgery for Temporal Lobe Epilepsy

Naming fMRI-guided white matter language tract volumes influence naming decline after temporal lobe resection

OBJECTIVE

The aim of this study was to explore the relation of language functional MRI (fMRI)-guided tractography with postsurgical naming decline in people with temporal lobe epilepsy (TLE).

METHODS

Twenty patients with unilateral TLE (9 left) were studied with auditory and picture naming functional MRI tasks. Activation maxima in the left posterobasal temporal lobe were used as seed regions for whole-brain fibre tractography. Clinical naming performance was assessed preoperatively, 4 months, and 12 months following temporal lobe resection. Volumes of white matter language tracts in both hemispheres as well as tract volume laterality indices were explored as moderators of postoperative naming decline using Pearson correlations and multiple linear regression with other clinical variables.

RESULTS

Larger volumes of white matter language tracts derived from auditory and picture naming maxima in the hemisphere of subsequent surgery as well as stronger lateralization of picture naming tract volumes to the side of surgery correlated with greater language decline, which was independent of fMRI lateralization status. Multiple regression for picture naming tract volumes was associated with a significant decline of naming function with 100% sensitivity and 93% specificity at both short-term and long-term follow-up.

INTERPRETATION

Naming fMRI-guided white matter language tract volumes relate to postoperative naming decline after temporal lobe resection in people with TLE. This can assist stratification of surgical outcome and minimize risk of postoperative language deficits in TLE.

Exo- and endoscopic lateral orbital wall approach for the medial temporal lobe glioma: how I do it

BACKGROUND

The endoscopic transorbital approach provides a direct access to the medial temporal lobe (MTL). However, when excising a highly vascular tumour, a wider access route that enables the concurrent use of standard neurosurgical instruments with both hands is preferable.

METHOD

We described the concept and technique of the lateral orbital wall approach (LOWA), which comprises orbitotomy and mini-craniotomy to treat MTL lesions using an exoscope and endoscope.

CONCLUSION

The LOWA provides a safe and natural surgical corridor to the MTL and enables 2- or 3-hand surgery. Hence, LOWA can potentially improve safety and efficiency to treat MTL lesions.

Advancements in Diffusion MRI Tractography for Neurosurgery

ABSTRACT

Diffusion magnetic resonance imaging tractography is a noninvasive technique that enables the visualization and quantification of white matter tracts within the brain. It is extensively used in preoperative planning for brain tumors, epilepsy, and functional neurosurgical procedures such as deep brain stimulation. Over the past 25 years, significant advancements have been made in imaging acquisition, fiber direction estimation, and tracking methods, resulting in considerable improvements in tractography accuracy. The technique enables the mapping of functionally critical pathways around surgical sites to avoid permanent functional disability. When the limitations are adequately acknowledged and considered, tractography can serve as a valuable tool to safeguard critical white matter tracts and provides insight regarding changes in normal white matter and structural connectivity of the whole brain beyond local lesions. In functional neurosurgical procedures such as deep brain stimulation, it plays a significant role in optimizing stimulation sites and parameters to maximize therapeutic efficacy and can be used as a direct target for therapy. These insights can aid in patient risk stratification and prognosis. This article aims to discuss state-of-the-art tractography methodologies and their applications in preoperative planning and highlight the challenges and new prospects for the use of tractography in daily clinical practice.

What are we really predicting with fMRI in epilepsy surgery?

While memory and language functional magnetic resonance imaging (fMRI) paradigms are becoming evermore refined, the measures of outcome they predict following epilepsy surgery tend to remain single scores on pencil and paper tests that were developed decades ago and have been repeatedly shown to bear little relation to patients' subjective reports of memory problems in the real world. The growing imbalance between the increasing sophistication of the predictive paradigms on the one hand and the vintage measures of the outcome on the other in the fMRI epilepsy surgery literature threatens the clinical relevance of studies employing these technologies. This paper examines some of the core principles of assessing neuropsychological outcomes following epilepsy surgery and explores how these may be adapted and applied in fMRI study designs to maximize the clinical relevance of these studies.

The impact of temporal lobe epilepsy surgery on picture naming and its relationship to network metric change

BACKGROUND

Anterior temporal lobe resection (ATLR) is a successful treatment for medically-refractory temporal lobe epilepsy (TLE). In the language-dominant hemisphere, 30%- 50% of individuals experience a naming decline which can impact upon daily life. Measures of structural networks are associated with language performance pre-operatively. It is unclear if analysis of network measures may predict post-operative decline.

METHODS

White matter fibre tractography was performed on preoperative diffusion MRI of 44 left lateralised and left resection individuals with TLE to reconstruct the preoperative structural network. Resection masks, drawn on co-registered pre- and post-operative T1-weighted MRI scans, were used as exclusion regions on pre-operative tractography to estimate the post-operative network. Changes in graph theory metrics, cortical strength, betweenness centrality, and clustering coefficient were generated by comparing the estimated pre- and post-operative networks. These were thresholded based on the presence of the connection in each patient, ranging from 75% to 100% in steps of 5%. The average graph theory metric across thresholds was taken. We incorporated leave-one-out cross-validation with smoothly clipped absolute deviation (SCAD) least absolute shrinkage and selection operator (LASSO) feature selection and a support vector classifier to assess graph theory metrics on picture naming decline. Picture naming was assessed via the Graded Naming Test preoperatively and at 3 and 12 months post-operatively and the outcome was classified using the reliable change index (RCI) to identify clinically significant decline. The best feature combination and model was selected using the area under the curve (AUC). The sensitivity, specificity and F1-score were also reported. Permutation testing was performed to assess the machine learning model and selected regions difference significance.

RESULTS

A combination of clinical and graph theory metrics were able to classify outcome of picture naming at 3 months with an AUC of 0.84. At 12 months, change in strength to cortical regions was best able to correctly classify outcome with an AUC of 0.86. Longitudinal analysis revealed that betweenness centrality was the best metric to identify patients who declined at 3 months, who will then continue to experience decline from 3 to 12 months. Both models were significantly higher AUC values than a random classifier.

CONCLUSION

Our results suggest that inferred changes of network integrity were able to correctly classify picture naming decline after ATLR. These measures may be used to prospectively to identify patients who are at risk of picture naming decline after surgery and could potentially be utilised to assist tailoring the resection in order to prevent this decline.