Functional atlases for analysis of motor and neuropsychological outcomes after medial globus pallidus and subthalamic stimulation

Anatomical atlases have been developed to improve the targeting of basal ganglia in deep brain stimulation. However, the sole anatomy cannot predict the functional outcome of this surgery. Deep brain stimulation is often a compromise between several functional outcomes: motor, fluency and neuropsychological outcomes in particular. In this study, we have developed anatomo-clinical atlases for the targeting of subthalamic and medial globus pallidus deep brain stimulation. The activated electrode coordinates of 42 patients implanted in the subthalamic nucleus and 29 patients in the medial globus pallidus were studied. The atlas was built using the representation of the volume of tissue theoretically activated by the stimulation. The UPDRS score was used to represent the motor outcome. The Stroop test was represented as well as semantic and phonemic fluencies. For the subthalamic nucleus, best motor outcomes were obtained when the supero-lateral part of the nucleus was stimulated whereas the semantic fluency was impaired in this same region. For the medial globus pallidus, best outcomes were obtained when the postero ventral part of the nucleus was stimulated whereas the phonemic fluency was impaired in this same region. There was no significant neuropsychological impairment. We have proposed new anatomo-clinical atlases to visualize the motor and neuropsychological consequences at 6 months of subthalamic nucleus and pallidal stimulation in patients with Parkinson's disease.

Toward a standard ontology of surgical process models

PURPOSE

The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative-OntoSPM Collaborative Action-with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative.

METHODS

A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM.

RESULTS

The workshop led to the OntoSPM Collaborative Action-launched in mid-2016-with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use.

CONCLUSION

The OntoSPM Collaborative Action has been in operation for 24 months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts' suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.

Knowledge transfer for surgical activity prediction

PURPOSE

Lack of annotated training data hinders automatic recognition and prediction of surgical activities necessary for situation-aware operating rooms. We propose using knowledge transfer to compensate for data deficit and improve prediction.

METHODS

We used two approaches to extract and transfer surgical process knowledge. First, we encoded semantic information about surgical terms using word embedding. Secondly, we passed knowledge between different clinical datasets of neurosurgical procedures using transfer learning.

RESULTS

The combination of two methods provided 22% improvement of activity prediction. We also made several pertinent observations about surgical practices based on the results of the performed transfer.

CONCLUSION

Word embedding boosts learning process. Transfer learning was shown to be more effective than a simple combination of data, especially for less similar procedures.

Surgical skills: Can learning curves be computed from recordings of surgical activities?

PURPOSE

Surgery is one of the riskiest and most important medical acts that are performed today. The need to improve patient outcomes and surgeon training, and to reduce the costs of surgery, has motivated the equipment of operating rooms with sensors that record surgical interventions. The richness and complexity of the data that are collected call for new methods to support computer-assisted surgery. The aim of this paper is to support the monitoring of junior surgeons learning their surgical skill sets.

METHODS

Our method is fully automatic and takes as input a series of surgical interventions each represented by a low-level recording of all activities performed by the surgeon during the intervention (e.g., cut the skin with a scalpel). Our method produces a curve describing the process of standardization of the behavior of junior surgeons. Given the fact that junior surgeons receive constant feedback from senior surgeons during surgery, these curves can be directly interpreted as learning curves.

RESULTS

Our method is assessed using the behavior of a junior surgeon in anterior cervical discectomy and fusion surgery over his first three years after residency. They revealed the ability of the method to accurately represent the surgical skill evolution. We also showed that the learning curves can be computed by phases allowing a finer evaluation of the skill progression.

CONCLUSION

Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring.

Finding discriminative and interpretable patterns in sequences of surgical activities

OBJECTIVE

Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest to understand and analyze surgical behaviors. This article addresses the issue of identifying discriminative patterns of surgical practice from recordings of surgeries. These recordings are sequences of low-level surgical activities representing the actions performed by surgeons during surgeries.

MATERIALS AND METHOD

To discover patterns that are specific to a group of surgeries, we use the vector space model (VSM) which is originally an algebraic model for representing text documents. We split long sequences of surgical activities into subsequences of consecutive activities. We then compute the relative frequencies of these subsequences using the tf*idf framework and we use the Cosine similarity to classify the sequences. This process makes it possible to discover which patterns discriminate one set of surgeries recordings from another set.

RESULTS

Experiments were performed on 40 neurosurgeries of anterior cervical discectomy (ACD). The results demonstrate that our method accurately identifies patterns that can discriminate between (1) locations where the surgery took place, (2) levels of expertise of surgeons (i.e., expert vs. intermediate) and even (3) individual surgeons who performed the intervention. We also show how the tf*idf weight vector can be used to both visualize the most interesting patterns and to highlight the parts of a given surgery that are the most interesting.

CONCLUSIONS

Identifying patterns that discriminate groups of surgeon is a very important step in improving the understanding of surgical processes. The proposed method finds discriminative and interpretable patterns in sequences of surgical activities. Our approach provides intuitive results, as it identifies automatically the set of patterns explaining the differences between the groups.

Automatic matching of surgeries to predict surgeons' next actions

OBJECTIVE

More than half a million surgeries are performed every day worldwide, which makes surgery one of the most important component of global health care. In this context, the objective of this paper is to introduce a new method for the prediction of the possible next task that a surgeon is going to perform during surgery.

MATERIAL AND METHOD

We formulate the problem as finding the optimal registration of a partial sequence to a complete reference sequence of surgical activities. We propose an efficient algorithm to find the optimal partial alignment and a prediction system using maximum a posteriori probability estimation and filtering. We also introduce a weighting scheme allowing to improve the predictions by taking into account the relative similarity between the current surgery and a set of pre-recorded surgeries.

RESULTS

Our method is evaluated on two types of neurosurgical procedures: lumbar disc herniation removal and anterior cervical discectomy. Results show that our method outperformed the state of the art by predicting the next task that the surgeon will perform with 95% accuracy.

CONCLUSIONS

This work shows that, even from the low-level description of surgeries and without other sources of information, it is often possible to predict the next surgical task when the conditions are consistent with the previously recorded surgeries. We also showed that our method is able to assess when there is actually a large divergence between the predictions and decide that it is not reasonable to make a prediction.

In vivo Exploration of the Connectivity between the Subthalamic Nucleus and the Globus Pallidus in the Human Brain Using Multi-Fiber Tractography

The basal ganglia is part of a complex system of neuronal circuits that play a key role in the integration and execution of motor, cognitive and emotional function in the human brain. Parkinson’s disease is a progressive neurological disorder of the motor circuit characterized by tremor, rigidity, and slowness of movement. Deep brain stimulation (DBS) of the subthalamic nucleus and the globus pallidus pars interna provides an efficient treatment to reduce symptoms and levodopa-induced side effects in Parkinson’s disease patients. While the underlying mechanism of action of DBS is still unknown, the potential modulation of white matter tracts connecting the surgical targets has become an active area of research. With the introduction of advanced diffusion MRI acquisition sequences and sophisticated post-processing techniques, the architecture of the human brain white matter can be explored in vivo. The goal of this study is to investigate the white matter connectivity between the subthalamic nucleus and the globus pallidus. Two multi-fiber tractography methods were used to reconstruct pallido-subthalamic, subthalamo-pallidal and pyramidal fibers in five healthy subjects datasets of the Human Connectome Project. The anatomical accuracy of the tracts was assessed by four judges with expertise in neuroanatomy, functional neurosurgery, and diffusion MRI. The variability among subjects was evaluated based on the fractional anisotropy and mean diffusivity of the tracts. Both multi-fiber approaches enabled the detection of complex fiber architecture in the basal ganglia. The qualitative evaluation by experts showed that the identified tracts were in agreement with the expected anatomy. Tract-derived measurements demonstrated relatively low variability among subjects. False-negative tracts demonstrated the current limitations of both methods for clinical decision-making. Multi-fiber tractography methods combined with state-of-the-art diffusion MRI data have the potential to help identify white matter tracts connecting DBS targets in functional neurosurgery intervention.

Improvement of Pyramidal Tract Side Effect Prediction Using a Data-Driven Method in Subthalamic Stimulation

OBJECTIVE

subthalamic nucleus deep brain stimulation (STN DBS) is limited by the occurrence of a pyramidal tract side effect (PTSE) induced by electrical activation of the pyramidal tract. Predictive models are needed to assist the surgeon during the electrode trajectory preplanning. The objective of the study was to compare two methods of PTSE prediction based on clinical assessment of PTSE induced by STN DBS in patients with Parkinson's disease.

METHODS

two clinicians assessed PTSE postoperatively in 20 patients implanted for at least three months in the STN. The resulting dataset of electroclinical tests was used to evaluate two methods of PTSE prediction. The first method was based on the volume of tissue activated (VTA) modeling and the second one was a data-driven-based method named Pyramidal tract side effect Model based on Artificial Neural network (PyMAN) developed in our laboratory. This method was based on the nonlinear correlation between the PTSE current threshold and the 3-D electrode coordinates. PTSE prediction from both methods was compared using Mann-Whitney U test.

RESULTS

1696 electroclinical tests were used to design and compare the two methods. Sensitivity, specificity, positive- and negative-predictive values were significantly higher with the PyMAN method than with the VTA-based method (P < 0.05).

CONCLUSION

the PyMAN method was more effective than the VTA-based method to predict PTSE.

SIGNIFICANCE

this data-driven tool could help the neurosurgeon in predicting adverse side effects induced by DBS during the electrode trajectory preplanning.

Acquisition models in intraoperative positron surface imaging

PURPOSE

Intraoperative imaging aims at identifying residual tumor during surgery. Positron Surface Imaging (PSI) is one of the solutions to help surgeons in a better detection of resection margins of brain tumor, leading to an improved patient outcome. This system relies on a tracked freehand beta probe, using [Formula: see text]F-based radiotracer. Some acquisition models have been proposed in the literature in order to enhance image quality, but no comparative validation study has been performed for PSI.

METHODS

In this study, we investigated the performance of different acquisition models by considering validation criteria and normalized metrics. We proposed a reference-based validation framework to perform the comparative study between acquisition models and a basic method. We estimated the performance of several acquisition models in light of four validation criteria: efficiency, computational speed, spatial accuracy and tumor contrast.

RESULTS

Selected acquisition models outperformed the basic method, albeit with the real-time aspect compromised. One acquisition model yielded the best performance among all according to the validation criteria: efficiency (1-Spe: 0.1, Se: 0.94), spatial accuracy (max Dice: 0.77) and tumor contrast (max T/B: 5.2). We also found out that above a minimum threshold value of the sampling rate, the reconstruction quality does not vary significantly.

CONCLUSION

Our method allowed the comparison of different acquisition models and highlighted one of them according to our validation criteria. This novel approach can be extended to 3D datasets, for validation of future acquisition models dedicated to intraoperative guidance of brain surgery.

Influence of subthalamic deep-brain stimulation on cognitive action control in incentive context

Subthalamic nucleus deep-brain stimulation (STN-DBS) is an effective treatment in Parkinson's disease (PD), but can have cognitive side effects, such as increasing the difficulty of producing appropriate responses when a habitual but inappropriate responses represent strong alternatives. STN-DBS also appears to modulate representations of incentives such as monetary rewards. Furthermore, conflict resolution can be modulated by incentive context. We therefore used a rewarded Simon Task to assess the influence of promised rewards on cognitive action control in 50 patients with PD, half of whom were being treated with STN-DBS. Results were analyzed according to the activation-suppression model. We showed that STN-DBS (i) favored the expression of motor impulsivity, as measured with the Barratt Impulsiveness Scale, (ii) facilitated the expression of incentive actions as observed with a greater increase in speed according to promised reward in patients with versus without DBS and (iii) may increase impulsive action selection in an incentive context. In addition, analysis of subgroups of implanted patients suggested that those who exhibited the most impulsive action selection had the least severe disease. This may indicate that patients with less marked disease are more at risk of developing impulsivity postoperatively. Finally, in these patients, incentive context increased the difficulty of resolving conflict situations. As a whole, the current study revealed that in patients with PD, STN-DBS affects the cognitive processes involved in conflict resolution, reward processing and the influence of promised rewards on conflict resolution.

Vision-based and marker-less surgical tool detection and tracking: a review of the literature

In recent years, tremendous progress has been made in surgical practice for example with Minimally Invasive Surgery (MIS). To overcome challenges coming from deported eye-to-hand manipulation, robotic and computer-assisted systems have been developed. Having real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy is a key ingredient for such systems. In this paper, we present a review of the literature dealing with vision-based and marker-less surgical tool detection. This paper includes three primary contributions: (1) identification and analysis of data-sets used for developing and testing detection algorithms, (2) in-depth comparison of surgical tool detection methods from the feature extraction process to the model learning strategy and highlight existing shortcomings, and (3) analysis of validation techniques employed to obtain detection performance results and establish comparison between surgical tool detectors. The papers included in the review were selected through PubMed and Google Scholar searches using the keywords: "surgical tool detection", "surgical tool tracking", "surgical instrument detection" and "surgical instrument tracking" limiting results to the year range 2000 2015. Our study shows that despite significant progress over the years, the lack of established surgical tool data-sets, and reference format for performance assessment and method ranking is preventing faster improvement.

Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation

Deep brain stimulation of the medial globus pallidus (GPm) is a surgical procedure for treating patients suffering from Parkinson's disease. Its therapeutic effect may be limited by the presence of pyramidal tract side effect (PTSE). PTSE is a contraction time-locked to the stimulation when the current spreading reaches the motor fibers of the pyramidal tract within the internal capsule. The objective of the study was to propose a preoperative predictive model of PTSE. A machine learning-based method called PyMAN (PTSE model based on artificial neural network) accounting for the current used in stimulation, the three-dimensional electrode coordinates and the angle of the trajectory, was designed to predict the occurrence of PTSE. Ten patients implanted in the GPm have been tested by a clinician to create a labeled dataset of the stimulation parameters that trigger PTSE. The kappa index value between the data predicted by PyMAN and the labeled data was 0.78. Further evaluation studies are desirable to confirm whether PyMAN could be a reliable tool for assisting the surgeon to prevent PTSE during the preoperative planning.

Weight Gain following Pallidal Deep Brain Stimulation: A PET Study

The mechanisms behind weight gain following deep brain stimulation (DBS) surgery seem to be multifactorial and suspected depending on the target, either the subthalamic nucleus (STN) or the globus pallidus internus (GPi). Decreased energy expenditure following motor improvement and behavioral and/or metabolic changes are possible explanations. Focusing on GPi target, our objective was to analyze correlations between changes in brain metabolism (measured with PET) and weight gain following GPi-DBS in patients with Parkinson's disease (PD). Body mass index was calculated and brain activity prospectively measured using 2-deoxy-2[18F]fluoro-D-glucose PET four months before and four months after the start of GPi-DBS in 19 PD patients. Dopaminergic medication was included in the analysis to control for its possible influence on brain metabolism. Body mass index increased significantly by 0.66 ± 1.3 kg/m2 (p = 0.040). There were correlations between weight gain and changes in brain metabolism in premotor areas, including the left and right superior gyri (Brodmann area, BA 6), left superior gyrus (BA 8), the dorsolateral prefrontal cortex (right middle gyrus, BAs 9 and 46), and the left and right somatosensory association cortices (BA 7). However, we found no correlation between weight gain and metabolic changes in limbic and associative areas. Additionally, there was a trend toward a correlation between reduced dyskinesia and weight gain (r = 0.428, p = 0.067). These findings suggest that, unlike STN-DBS, motor improvement is the major contributing factor for weight gain following GPi-DBS PD, confirming the motor selectivity of this target.

Automatic data-driven real-time segmentation and recognition of surgical workflow

PURPOSE

With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection.

METHODS

The segmentation and recognition are based on a four-stage process. Firstly, during the learning time, a Surgical Process Model is automatically constructed from data annotations to guide the following process. Secondly, data samples are described using a combination of low-level visual cues and instrument information. Then, in the third stage, these descriptions are employed to train a set of AdaBoost classifiers capable of distinguishing one surgical phase from others. Finally, AdaBoost responses are used as input to a Hidden semi-Markov Model in order to obtain a final decision.

RESULTS

On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases.

CONCLUSION

Compared to the analysis based on one data type only, a combination of visual features and instrument signals allows better segmentation, reduction of the detection delay and discovery of the correct phase order.

Synthesis and Simulation of Surgical Process Models

Virtual Reality for surgical training is mainly focused on technical surgical skills. We work on providing a novel approach to the use of Virtual Reality focusing on the procedural aspects. Our system relies on a specific work-flow generating a model of the procedure from real case surgery observation in the operating room. This article presents the different technologies created in the context of our project and their relations as other components of our workflow.

Pre-frontal-insular-cerebellar modifications correlate with disgust feeling blunting after subthalamic stimulation: A positron emission tomography study in Parkinson's disease

Subthalamic nucleus (STN) deep brain stimulation (DBS) has recently advanced our understanding of the major role played by this basal ganglion in human emotion. Research indicates that STN DBS can induce modifications in all components of emotion, and neuroimaging studies have shown that the metabolic modifications correlated with these emotional disturbances following surgery are both task- and sensory input-dependent. Nevertheless, to date, these modifications have not been confirmed for all emotional components, notably subjective emotional experience, or feelings. To identify the neural network underlying the modification of feelings following STN DBS, we assessed 16 patients with Parkinson's disease before and after surgery, using both subjective assessments of emotional experience and ¹⁸ [F]fluorodeoxyglucose positron emission tomography (¹⁸ FDG-PET). The patients viewed six film excerpts intended to elicit happy, angry, fearful, sad, disgusted, and neutral feelings, and they self-rated the intensity of these feelings. After DBS, there was a significant reduction in the intensity of the disgust feeling. Correlations were observed between decreased disgust experience and cerebral glucose metabolism (FDG uptake) in the bilateral pre-frontal cortices (orbitofrontal, dorsolateral, and inferior frontal gyri), bilateral insula, and right cerebellum. We suggest that the STN contributes to the synchronization process underlying the emergence of feelings.

Detecting Surgical Tools by Modelling Local Appearance and Global Shape

Detecting tools in surgical videos is an important ingredient for context-aware computer-assisted surgical systems. To this end, we present a new surgical tool detection dataset and a method for joint tool detection and pose estimation in 2d images. Our two-stage pipeline is data-driven and relaxes strong assumptions made by previous works regarding the geometry, number, and position of tools in the image. The first stage classifies each pixel based on local appearance only, while the second stage evaluates a tool-specific shape template to enforce global shape. Both local appearance and global shape are learned from training data. Our method is validated on a new surgical tool dataset of 2 476 images from neurosurgical microscopes, which is made freely available. It improves over existing datasets in size, diversity and detail of annotation. We show that our method significantly improves over competitive baselines from the computer vision field. We achieve 15% detection miss-rate at 10(-1) false positives per image (for the suction tube) over our surgical tool dataset. Results indicate that performing semantic labelling as an intermediate task is key for high quality detection.

Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training

Dexterity and procedural knowledge are two critical skills that surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow us to provide an in-depth analysis of surgical gestures to precisely assess these skills. Our objective is to develop a method for the automatic and quantitative assessment of surgical gestures. To reach this goal, we propose a new unsupervised algorithm that can automatically segment kinematic data from robotic training sessions. Without relying on any prior information or model, this algorithm detects critical points in the kinematic data that define relevant spatio-temporal segments. Based on the association of these segments, we obtain an accurate recognition of the gestures involved in the surgical training task. We, then, perform an advanced analysis and assess our algorithm using datasets recorded during real expert training sessions. After comparing our approach with the manual annotations of the surgical gestures, we observe 97.4% accuracy for the learning purpose and an average matching score of 81.9% for the fully automated gesture recognition process. Our results show that trainees workflow can be followed and surgical gestures may be automatically evaluated according to an expert database. This approach tends toward improving training efficiency by minimizing the learning curve.

Reduced Verbal Fluency following Subthalamic Deep Brain Stimulation: A Frontal-Related Cognitive Deficit?

Objective

The decrease in verbal fluency in patients with Parkinson’s disease (PD) undergoing subthalamic nucleus deep brain stimulation (STN-DBS) is usually assumed to reflect a frontal lobe-related cognitive dysfunction, although evidence for this is lacking.

Methods

To explore its underlying mechanisms, we combined neuropsychological, psychiatric and motor assessments with an examination of brain metabolism using F-18 fluorodeoxyglucose positron emission tomography, in 26 patients with PD, 3 months before and after surgery. We divided these patients into two groups, depending on whether or not they exhibited a postoperative deterioration in either phonemic (10 patients) or semantic (8 patients) fluency. We then compared the STN-DBS groups with and without verbal deterioration on changes in clinical measures and brain metabolism.

Results

We did not find any neuropsychological change supporting the presence of an executive dysfunction in patients with a deficit in either phonemic or semantic fluency. Similarly, a comparison of patients with or without impaired fluency on brain metabolism failed to highlight any frontal areas involved in cognitive functions. However, greater changes in cognitive slowdown and apathy were observed in patients with a postoperative decrease in verbal fluency.

Conclusions

These results suggest that frontal lobe-related cognitive dysfunction could play only a minor role in the postoperative impairment of phonemic or semantic fluency, and that cognitive slowdown and apathy could have a more decisive influence. Furthermore, the phonemic and semantic impairments appeared to result from the disturbance of distinct mechanisms.

Procedural surgical skill assessment in laparoscopic training environments

PURPOSE

This study aimed to identify detailed differences in laparoscopic surgical processes between expert and novice surgeons in a training environment and demonstrate that surgical process modeling can be used for such detailed analysis.

METHODS

Eleven expert surgeons each of whom had performed [Formula: see text] laparoscopic procedures were compared with 10 young surgeons each of whom had performed [Formula: see text] laparoscopic procedures, and five medical students. Each examinee performed a specific skill assessment task. During tasks, instrument motion was monitored using a video capture system. From the video, the corresponding workflow was recorded by labeling the surgeons' activities according to a predefined terminology. Activities represented manual work steps performed during the task, described by a combination of a verb (representing the action), a tool, and the involved structure. The results were described as the number of occurrences (times), average duration (seconds), total duration (seconds), minimal duration (seconds), maximal duration (seconds), and occupancy percentage (%).

RESULTS

The terminology for describing the processes of this task included 10 actions, six tools, four structures, and three events for each hand. There were 63 combinations of different possible activities; significant differences in 12 activities were observed between the expert and novice groups (young surgeons and medical students). The expert group performed the task with fewer occurrences and shorter duration than did the novice group in the left hand.

CONCLUSIONS

We identified differences in surgical process between experts and novices in laparoscopic surgical simulation. Our proposed method would be useful for education and training in laparoscopic surgery.

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition

PURPOSE

The rise of intraoperative information threatens to outpace our abilities to process it. Context-aware systems, filtering information to automatically adapt to the current needs of the surgeon, are necessary to fully profit from computerized surgery. To attain context awareness, representation of medical knowledge is crucial. However, most existing systems do not represent knowledge in a reusable way, hindering also reuse of data. Our purpose is therefore to make our computational models of medical knowledge sharable, extensible and interoperational with established knowledge representations in the form of the LapOntoSPM ontology. To show its usefulness, we apply it to situation interpretation, i.e., the recognition of surgical phases based on surgical activities.

METHODS

Considering best practices in ontology engineering and building on our ontology for laparoscopy, we formalized the workflow of laparoscopic adrenalectomies, cholecystectomies and pancreatic resections in the framework of OntoSPM, a new standard for surgical process models. Furthermore, we provide a rule-based situation interpretation algorithm based on SQWRL to recognize surgical phases using the ontology.

RESULTS

The system was evaluated on ground-truth data from 19 manually annotated surgeries. The aim was to show that the phase recognition capabilities are equal to a specialized solution. The recognition rates of the new system were equal to the specialized one. However, the time needed to interpret a situation rose from 0.5 to 1.8 s on average which is still viable for practical application.

CONCLUSION

We successfully integrated medical knowledge for laparoscopic surgeries into OntoSPM, facilitating knowledge and data sharing. This is especially important for reproducibility of results and unbiased comparison of recognition algorithms. The associated recognition algorithm was adapted to the new representation without any loss of classification power. The work is an important step to standardized knowledge and data representation in the field on context awareness and thus toward unified benchmark data sets.

Work domain constraints for modelling surgical performance

PURPOSE

Three main approaches can be identified for modelling surgical performance: a competency-based approach, a task-based approach, both largely explored in the literature, and a less known work domain-based approach. The work domain-based approach first describes the work domain properties that constrain the agent's actions and shape the performance. This paper presents a work domain-based approach for modelling performance during cervical spine surgery, based on the idea that anatomical structures delineate the surgical performance. This model was evaluated through an analysis of junior and senior surgeons' actions.

METHOD

Twenty-four cervical spine surgeries performed by two junior and two senior surgeons were recorded in real time by an expert surgeon. According to a work domain-based model describing an optimal progression through anatomical structures, the degree of adjustment of each surgical procedure to a statistical polynomial function was assessed.

RESULTS

Each surgical procedure showed a significant suitability with the model and regression coefficient values around 0.9. However, the surgeries performed by senior surgeons fitted this model significantly better than those performed by junior surgeons. Analysis of the relative frequencies of actions on anatomical structures showed that some specific anatomical structures discriminate senior from junior performances.

CONCLUSION

The work domain-based modelling approach can provide an overall statistical indicator of surgical performance, but in particular, it can highlight specific points of interest among anatomical structures that the surgeons dwelled on according to their level of expertise.

Preoperative brain metabolism and quality of life after subthalamic nucleus stimulation in Parkinson's disease

Subthalamic nucleus deep brain stimulation (STN-DBS) has been proven to improve health-related quality of life (HRQoL) in patients with Parkinson's disease (PD) presenting medically refractory motor complications and dyskinesia. However, some patients fail to benefit from STN-DBS despite rigorous preoperative selection. We postulated that they have a particular, clinically ineloquent, brain metabolism before surgery. We divided 40 stimulated PD patients into two groups (responders vs. nonresponders) depending on whether they reported or not a clinically significant improvement in their quality of life 1 year after surgery. We retrospectively compared their preoperative brain metabolism on the basis of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans. We also analyzed their neuropsychological and psychiatric profiles before and after surgery. All 40 patients met the STN-DBS selection criteria, but only 50% of them had significantly improved 1 year after surgery. Preoperative PET scans showed that metabolism was higher in the left insula, both inferior frontal gyri and left precentral gyrus in nonresponders than in responders. Clinically, postoperative motor scores were similar in both groups, but a worsening of the depression score was observed among nonresponders. PET imaging revealed that nonresponders were characterized by distinctive brain functioning pre-surgery, in regions involved in associative and limbic circuits, as a result of PD-related degeneration. STN-DBS may have interfered with this already abnormal circuitry, leading to the occurrence of complex nonmotor symptoms reducing quality of life. Preoperative brain metabolism could be a useful biomarker for anticipating STN-DBS efficacy in terms of HRQoL in the context of advanced PD.

Assessing neurosurgical non-technical skills: an exploratory study of a new behavioural marker system

RATIONALE, AIMS AND OBJECTIVES

The management of non-technical skills is a major factor affecting teamwork quality and patient safety. This article presents a behavioural marker system for assessing neurosurgical non-technical skills (BMS-NNTS). We tested the BMS during deep brain stimulation surgery.

METHOD

We developed the BMS in three stages. First, we drew up a provisional assessment tool based on the literature and observation tools developed for other surgical specialties. We then analysed videos made in an operating room (OR) during deep brain stimulation operations in order to ensure there were no significant omissions from the skills list. Finally, we used five videos of operations to identify the behavioural markers of non-technical skills in verbal communications.

RESULTS

Analyses of more than six hours of observations revealed 3515 behaviours from which we determined the neurosurgeon's non-technical skills behaviour pattern. The neurosurgeon frequently engaged in explicit coordination, situation awareness and leadership behaviours. In addition, the neurosurgeon's behaviours differed according to the stage of the operation and the OR staff members with whom she was communicating.

CONCLUSIONS

Our behavioural marker system provides a structured approach to assessing non-technical skills in the field of neurosurgery. It can also be transferred to other surgical specialties and used in surgeon training curricula.

Comparative validation of single-shot optical techniques for laparoscopic 3-D surface reconstruction

Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at www.open-CAS.com upon publication of the paper.

Investigation of morphometric variability of subthalamic nucleus, red nucleus, and substantia nigra in advanced Parkinson's disease patients using automatic segmentation and PCA-based analysis

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective surgical therapy to treat Parkinson's disease (PD). Conventional methods employ standard atlas coordinates to target the STN, which, along with the adjacent red nucleus (RN) and substantia nigra (SN), are not well visualized on conventional T1w MRIs. However, the positions and sizes of the nuclei may be more variable than the standard atlas, thus making the pre-surgical plans inaccurate. We investigated the morphometric variability of the STN, RN and SN by using label-fusion segmentation results from 3T high resolution T2w MRIs of 33 advanced PD patients. In addition to comparing the size and position measurements of the cohort to the Talairach atlas, principal component analysis (PCA) was performed to acquire more intuitive and detailed perspectives of the measured variability. Lastly, the potential correlation between the variability shown by PCA results and the clinical scores was explored.

Surgical process modelling: a review

PURPOSE

Surgery is continuously subject to technological and medical innovations that are transforming daily surgical routines. In order to gain a better understanding and description of surgeries, the field of surgical process modelling (SPM) has recently emerged. The challenge is to support surgery through the quantitative analysis and understanding of operating room activities. Related surgical process models can then be introduced into a new generation of computer-assisted surgery systems.

METHODS

In this paper, we present a review of the literature dealing with SPM. This methodological review was obtained from a search using Google Scholar on the specific keywords: "surgical process analysis", "surgical process model" and "surgical workflow analysis".

RESULTS

This paper gives an overview of current approaches in the field that study the procedural aspects of surgery. We propose a classification of the domain that helps to summarise and describe the most important components of each paper we have reviewed, i.e., acquisition, modelling, analysis, application and validation/evaluation. These five aspects are presented independently along with an exhaustive list of their possible instantiations taken from the studied publications.

CONCLUSION

This review allows a greater understanding of the SPM field to be gained and introduces future related prospects.

Development of workflow task analysis during cerebral diagnostic angiographies: time-based comparison of junior and senior tasks

OBJECTIVE

Assessing neuroradiologists' skills in the operating room (OR) is difficult and often subjective. This study used a workflow time-based task analysis approach while performing cerebral angiography.

METHODS

Eight angiographies performed by a senior neuroradiologist and eight performed by a junior neuroradiologist were compared. Dedicated software with specific terminology was used to record the tasks. Procedures were subdivided into phases, each comprising multiple tasks. Each task was defined as a triplet, associating an action, an instrument and an anatomical structure. The duration of each task was the metric. Total duration of the procedure, task duration and the number of times a task was repeated were identified. The focus was on tasks using fluoroscopy and for moving the X-ray table/tube.

RESULTS

The total duration of tasks to complete the entire procedure was longer for the junior operators than for the seniors (P=0.012). The mean duration per task during the navigation phase was 86s for the juniors and 43s for the seniors (P=0.002). The total and mean durations of tasks involving the use of fluoroscopy were also longer for the juniors (P=0.002 and P=0.033, respectively). For tasks involving the table/tube, the total and mean durations were again longer for the juniors (P=0.019 and P=0.082, respectively).

CONCLUSION

This approach allows reliable skill assessment in the radiology OR and comparison of junior and senior competencies during cerebral diagnostic angiography. This new tool can improve the quality and safety of procedures, and facilitate the learning process for neuroradiologists.

Multi-site study of surgical practice in neurosurgery based on surgical process models

Surgical Process Modelling (SPM) was introduced to improve understanding the different parameters that influence the performance of a Surgical Process (SP). Data acquired from SPM methodology is enormous and complex. Several analysis methods based on comparison or classification of Surgical Process Models (SPMs) have previously been proposed. Such methods compare a set of SPMs to highlight specific parameters explaining differences between populations of patients, surgeons or systems. In this study, procedures performed at three different international University hospitals were compared using SPM methodology based on a similarity metric focusing on the sequence of activities occurring during surgery. The proposed approach is based on Dynamic Time Warping (DTW) algorithm combined with a clustering algorithm. SPMs of 41 Anterior Cervical Discectomy (ACD) surgeries were acquired at three Neurosurgical departments; in France, Germany, and Canada. The proposed approach distinguished the different surgical behaviors according to the location where surgery was performed as well as between the categorized surgical experience of individual surgeons. We also propose the use of Multidimensional Scaling to induce a new space of representation of the sequences of activities. The approach was compared to a time-based approach (e.g. duration of surgeries) and has been shown to be more precise. We also discuss the integration of other criteria in order to better understand what influences the way the surgeries are performed. This first multi-site study represents an important step towards the creation of robust analysis tools for processing SPMs. It opens new perspectives for the assessment of surgical approaches, tools or systems as well as objective assessment and comparison of surgeon's expertise.

Analysis of electrode deformations in deep brain stimulation surgery

PURPOSE

Deep brain stimulation (DBS) surgery is used to reduce motor symptoms when movement disorders are refractory to medical treatment. Post-operative brain morphology can induce electrode deformations as the brain recovers from an intervention. The inverse brain shift has a direct impact on accuracy of the targeting stage, so analysis of electrode deformations is needed to predict final positions.

METHODS

DBS electrode curvature was evaluated in 76 adults with movement disorders who underwent bilateral stimulation, and the key variables that affect electrode deformations were identified. Non-linear modelling of the electrode axis was performed using post-operative computed tomography (CT) images. A mean curvature index was estimated for each patient electrode. Multivariate analysis was performed using a regression decision tree to create a hierarchy of predictive variables. The identification and classification of key variables that determine electrode curvature were validated with statistical analysis.

RESULTS

The principal variables affecting electrode deformations were found to be the date of the post-operative CT scan and the stimulation target location. The main pathology, patient's gender, and disease duration had a smaller although important impact on brain shift.

CONCLUSIONS

The principal determinants of electrode location accuracy during DBS procedures were identified and validated. These results may be useful for improved electrode targeting with the help of mathematical models.

The state of the art of visualization in mixed reality image guided surgery

This paper presents a review of the state of the art of visualization in mixed reality image guided surgery (IGS). We used the DVV (data, visualization processing, view) taxonomy to classify a large unbiased selection of publications in the field. The goal of this work was not only to give an overview of current visualization methods and techniques in IGS but more importantly to analyze the current trends and solutions used in the domain. In surveying the current landscape of mixed reality IGS systems, we identified a strong need to assess which of the many possible data sets should be visualized at particular surgical steps, to focus on novel visualization processing techniques and interface solutions, and to evaluate new systems.

On mixed reality environments for minimally invasive therapy guidance: systems architecture, successes and challenges in their implementation from laboratory to clinic

Mixed reality environments for medical applications have been explored and developed over the past three decades in an effort to enhance the clinician's view of anatomy and facilitate the performance of minimally invasive procedures. These environments must faithfully represent the real surgical field and require seamless integration of pre- and intra-operative imaging, surgical instrument tracking, and display technology into a common framework centered around and registered to the patient. However, in spite of their reported benefits, few mixed reality environments have been successfully translated into clinical use. Several challenges that contribute to the difficulty in integrating such environments into clinical practice are presented here and discussed in terms of both technical and clinical limitations. This article should raise awareness among both developers and end-users toward facilitating a greater application of such environments in the surgical practice of the future.

A cognitive engineering framework for the specification of information requirements in medical imaging: application in image-guided neurosurgery

PURPOSE

This study proposes a framework coming from cognitive engineering, which makes it possible to define what information content has to be displayed or emphasised from medical imaging, for assisting clinicians according to their level of expertise in the domain.

METHOD

We designed a rating scale to assess visualisation systems in image-guided neurosurgery with respect to the depiction of the neurosurgical work domain. This rating scale was based on a neurosurgical work domain analysis. This scale has been used to evaluate visualisation modes among neurosurgeons, residents and engineers. We asked five neurosurgeons, ten medical residents and ten engineers to rate two visualisation modes from the same data (2D MR image vs. 3D computerised image). With this method, the amount of abstract and concrete work domain information displayed by each visualisation mode can be measured.

RESULTS

A global difference in quantities of perceived information between both images was observed. Surgeons and medical residents perceived significantly more information than engineers for both images. Unlike surgeons, however, the amount of information perceived by residents and engineers significantly decreased as information abstraction increased.

CONCLUSIONS

We demonstrated the possibility of measuring the amount of work domain information displayed by different visualisation modes of medical imaging according to different user profiles. Engineers in charge of the design of medical image-guided surgical systems did not perceive the same set of information as surgeons or even medical residents. This framework can constitute a user-oriented approach to evaluate the amount of perceived information from image-guided surgical systems and support their design from a cognitive engineering point of view.

Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures

PURPOSE

Surgical process models (SPMs) have recently been created for situation-aware computer-assisted systems in the operating room. One important challenge in this area is the automatic acquisition of SPMs. The purpose of this study is to present a new method for the automatic detection of low-level surgical tasks, that is, the sequence of activities in a surgical procedure, from microscope video images only. The level of granularity that we addressed in this work is symbolized by activities formalized by triplets <action, surgical tool, anatomical structure> .

METHODS

Using the results of our latest work on the recognition of surgical phases in cataract surgeries, and based on the hypothesis that most activities occur in one or two phases only, we created a light-weight ontology, formalized as a hierarchical decomposition into phases and activities. Information concerning the surgical tools, the areas where tools are used and three other visual cues were detected through an image-based approach and combined with the information of the current surgical phase within a knowledge-based recognition system. Knowing the surgical phases before the activity, recognition allows supervised classification to be adapted to the phase. Multiclass Support Vector Machines were chosen as a classification algorithm.

RESULTS

Using a dataset of 20 cataract surgeries, and identifying 25 possible pairs of activities, a frame-by-frame recognition rate of 64.5 % was achieved with the proposed system.

CONCLUSIONS

The addition of human knowledge to traditional bottom-up approaches based on image analysis appears to be promising for low-level task detection. The results of this work could be used for the automatic indexation of post-operative videos.

DVV: a taxonomy for mixed reality visualization in image guided surgery

Mixed reality visualizations are increasingly studied for use in image guided surgery (IGS) systems, yet few mixed reality systems have been introduced for daily use into the operating room (OR). This may be the result of several factors: the systems are developed from a technical perspective, are rarely evaluated in the field, and/or lack consideration of the end user and the constraints of the OR. We introduce the Data, Visualization processing, View (DVV) taxonomy which defines each of the major components required to implement a mixed reality IGS system. We propose that these components be considered and used as validation criteria for introducing a mixed reality IGS system into the OR. A taxonomy of IGS visualization systems is a step toward developing a common language that will help developers and end users discuss and understand the constituents of a mixed reality visualization system, facilitating a greater presence of future systems in the OR. We evaluate the DVV taxonomy based on its goodness of fit and completeness. We demonstrate the utility of the DVV taxonomy by classifying 17 state-of-the-art research papers in the domain of mixed reality visualization IGS systems. Our classification shows that few IGS visualization systems' components have been validated and even fewer are evaluated.

Augmented environments for minimally invasive therapy: implementation barriers from technology to practice

Augmented environments for medical applications have been explored and developed in an effort to enhance the clinician's view of anatomy and facilitate the performance of minimally invasive procedures. These environments must faithfully represent the real surgical field and require seamless integration of pre- and intra-operative imaging, surgical instrument tracking and display technology into a common framework centered around the patient. However, few image guidance environments have been successfully translated into clinical use. Several challenges that contribute to the slow progress of integrating such environments into clinical practice are discussed here in terms of both technical and clinical limitations.

Surgical tools recognition and pupil segmentation for cataract surgical process modeling

In image-guided surgery, a new generation of Computer-Assisted-Surgical (CAS) systems based on information from the Operating Room (OR) has recently been developed to improve situation awareness in the OR. Our main project is to develop an application-dependant framework able to extract high-level tasks (surgical phases) using microscope videos data only. In this paper, we present two methods: one method to segment the pupil and one to extract and recognize surgical tools. We show how both methods improve the accuracy of the framework for analysis of cataract surgery videos, to detect eight surgical phases.

A framework for the recognition of high-level surgical tasks from video images for cataract surgeries

The need for a better integration of the new generation of computer-assisted-surgical systems has been recently emphasized. One necessity to achieve this objective is to retrieve data from the operating room (OR) with different sensors, then to derive models from these data. Recently, the use of videos from cameras in the OR has demonstrated its efficiency. In this paper, we propose a framework to assist in the development of systems for the automatic recognition of high-level surgical tasks using microscope videos analysis. We validated its use on cataract procedures. The idea is to combine state-of-the-art computer vision techniques with time series analysis. The first step of the framework consisted in the definition of several visual cues for extracting semantic information, therefore, characterizing each frame of the video. Five different pieces of image-based classifiers were, therefore, implemented. A step of pupil segmentation was also applied for dedicated visual cue detection. Time series classification algorithms were then applied to model time-varying data. Dynamic time warping and hidden Markov models were tested. This association combined the advantages of all methods for better understanding of the problem. The framework was finally validated through various studies. Six binary visual cues were chosen along with 12 phases to detect, obtaining accuracies of 94%.

Classification of surgical processes using dynamic time warping

In the creation of new computer-assisted intervention systems, Surgical Process Models (SPMs) are an emerging concept used for analyzing and assessing surgical interventions. SPMs represent Surgical Processes (SPs) which are formalized as symbolic structured descriptions of surgical interventions using a pre-defined level of granularity and a dedicated terminology. In this context, one major challenge is the creation of new metrics for the comparison and the evaluation of SPs. Thus, correlations between these metrics and pre-operative data are used to classify surgeries and highlight specific information on the surgery itself and on the surgeon, such as his/her level of expertise. In this paper, we explore the automatic classification of a set of SPs based on the Dynamic Time Warping (DTW) algorithm. DTW is used to compute a similarity measure between two SPs that focuses on the different types of activities performed during surgery and their sequencing, by minimizing time differences. Indeed, it turns out to be a complementary approach to the classical methods that only focus on differences in the time and the number of activities. Experiments were carried out on 24 lumbar disk herniation surgeries to discriminate the surgeons level of expertise according to a prior classification of SPs. Supervised and unsupervised classification experiments have shown that this approach was able to automatically identify groups of surgeons according to their level of expertise (senior and junior), and opens many perspectives for the creation of new metrics for comparing and evaluating surgeries.

Similarity metrics for surgical process models

OBJECTIVE

The objective of this work is to introduce a set of similarity metrics for comparing surgical process models (SPMs). SPMs are progression models of surgical interventions that support quantitative analyses of surgical activities, supporting systems engineering or process optimization.

METHODS AND MATERIALS

Five different similarity metrics are presented and proven. These metrics deal with several dimensions of process compliance in surgery, including granularity, content, time, order, and frequency of surgical activities. The metrics were experimentally validated using 20 clinical data sets each for cataract interventions, craniotomy interventions, and supratentorial tumor resections. The clinical data sets were controllably modified in simulations, which were iterated ten times, resulting in a total of 600 simulated data sets. The simulated data sets were subsequently compared to the original data sets to empirically assess the predictive validity of the metrics.

RESULTS

We show that the results of the metrics for the surgical process models correlate significantly (p<0.001) with the induced modifications and that all metrics meet predictive validity. The clinical use of the metrics was exemplarily, as demonstrated by assessment of the learning curves of observers during surgical process model acquisition.

CONCLUSION

Measuring similarity between surgical processes is a complex task. However, metrics for computing the similarity between surgical process models are needed in many uses in the field of medical engineering. These metrics are essential whenever two SPMs need to be compared, such as during the evaluation of technical systems, the education of observers, or the determination of surgical strategies. These metrics are key figures that provide a solid base for medical decisions, such as during validation of sensor systems for use in operating rooms in the future.

Recording of surgical processes: a study comparing senior and junior neurosurgeons during lumbar disc herniation surgery

BACKGROUND

Evaluating surgical practice in the operating room is difficult, and its assessment is largely subjective.

OBJECTIVE

Recording of standardized spine surgery processes was conducted to ascertain whether any significant differences in surgical practice could be observed between senior and junior neurosurgeons.

METHODS

Twenty-four procedures of lumbar discectomies were consecutively recorded by a senior neurosurgeon. In 12 cases, surgery was entirely performed by a senior neurosurgeon with the aid of a resident, and in the 12 remaining cases, surgery was performed by a resident with the aid of a senior neurosurgeon. The data recorded were general parameters (operating time for the whole procedure and for each step), and general and specific parameters of the surgeon's activities (number of manual gestures, number and duration of actions performed, use of the instruments, and use of interventions on anatomic structures). The Mann-Whitney U test was used for comparison between the 2 groups of neurosurgeons.

RESULTS

The operating time was statistically lower for the group of senior surgeons. The seniors statistically demonstrated greater economy in time and in gestures during the closure step, for sewing and for the use of scissors, needle holders, and forceps. The senior surgeons statistically worked for a shorter time on the skin and used fewer manual gestures on the thoracolumbalis fascia. The number of changes in microscope position was also statistically lower for this group.

CONCLUSION

There is a relationship between surgical practice, as determined by a method of objective measurement using observation software, and surgical experience: gesture economy evolves with seniority.

Abnormal functional lateralization and activity of language brain areas in typical specific language impairment (developmental dysphasia)

Atypical functional lateralization and specialization for language have been proposed to account for developmental language disorders, yet results from functional neuroimaging studies are sparse and inconsistent. This functional magnetic resonance imaging study compared children with a specific subtype of specific language impairment affecting structural language (n = 21), to a matched group of typically developing children using a panel of four language tasks neither requiring reading nor metalinguistic skills, including two auditory lexico-semantic tasks (category fluency and responsive naming) and two visual phonological tasks based on picture naming. Data processing involved normalizing the data with respect to a matched pairs paediatric template, groups and between-groups analysis, and laterality indices assessment within regions of interest using single and combined task analysis. Children with specific language impairment exhibited a significant lack of left lateralization in all core language regions (inferior frontal gyrus-opercularis, inferior frontal gyrus-triangularis, supramarginal gyrus and superior temporal gyrus), across single or combined task analysis, but no difference of lateralization for the rest of the brain. Between-group comparisons revealed a left hypoactivation of Wernicke's area at the posterior superior temporal/supramarginal junction during the responsive naming task, and a right hyperactivation encompassing the anterior insula with adjacent inferior frontal gyrus and the head of the caudate nucleus during the first phonological task. This study thus provides evidence that this subtype of specific language impairment is associated with atypical lateralization and functioning of core language areas.