Tag: Brain

The NIST Lab has collaborated with the Utrecht group headed by Dr. H. Hulshoff Pol for the past number of years [1, 2, 3]. Her group has used my ANIMAL non-linear registration software with voxel based morphometry (VBM) for morphometric analysis of cerebral MRI of patients with schizophrenia. Recently they have provided evidence which suggests that brain structures reflect overlapping and segregating genetic liabilities for schizophrenia and BD. The overlapping smaller white matter volume and common areas of thinner and/or thicker cortex (thinner right orbitofrontal cortex, thinner right (and left) parahippocampus, and thicker temporoparietal and left superior motor cortices) suggest that both disorders share neurodevelopmental roots. They have also demonstrated excessive thinning of the cortex over time, most pronounced in the temporal and frontal areas, with progression across the span of the illness. This excessive thinning appears related to medication and outcome. Their findings were extended to reveal that cannabis use in patients with schizophrenia resulted in additional thinning in the left dorsolateral prefrontal cortex (DLPFC), left anterior cingulate cortex (ACC) and left occipital lobe as compared to those patients with schizophrenia that did not use cannabis after a 5-year follow-up.

In later papers, they have identified focal grey-matter differences in a number of regions when compared to age-matched controls [4]. They have also found focal white matter changes that suggest abnormal inter-hemispheric connectivity of anterior cortical and sub-cortical brain regions, reflecting decreased hemispheric specialisation in schizophrenia [5]. These results have been partially replicated in a twin study [6,7] and a long term follow-up study [8].

References

[1] Hulshoff Pol HE, van Baal GC, Schnack HG, Brans RG, van der Schot AC, Brouwer RM, van Haren NE, Lepage C, Collins DL, Evans AC, Boomsma DI, Nolen W, Kahn RS. Overlapping and segregating structural brain abnormalities in twins with schizophrenia or bipolar disorder. Arch Gen Psychiatry. 2012 Apr;69(4):349–59

[2] van Haren, Neeltje EM, Hugo G. Schnack, Wiepke Cahn, Martijn P. van den Heuvel, Claude Lepage, Louis Collins, Alan C. Evans, Hilleke E. Hulshoff Pol, and René S. Kahn. “Changes in cortical thickness during the course of illness in schizophrenia.” Archives of general psychiatry. 2011; 68(9): 871–880.

[3] M. Rais, N.E.M. van Haren, W Cahn, H.G. Schnack, C. Lepage, D.L. Collins, A. Evans, H. E. Hulshoff Poll and R.S. Kahn, “Cannabis use and progressive cortical thickness loss in areas rich in CB1 receptors during the first five years of schizophrenia,” European Neuropsychopharmacology, 2010 Dec; 20(12): 855–65.

[4] H. E. Hulshoff Pol, H. G. Schnack, R. C. Mandl, N. E. van Haren, H. Koning, D. L. Collins, A. C. Evans, and R. S. Kahn, “Focal gray matter density changes in schizophrenia,” Arch Gen Psychiatry. 2001; 58: 1118–25.

[5] H. E. Hulshoff Pol, H. G. Schnack, R. C. Mandl, W. Cahn, D. L. Collins, A. C. Evans, and R. S. Kahn, “Focal white matter density changes in schizophrenia: reduced inter-hemispheric connectivity,” Neuroimage. 2004; 21: 27–35.

[6] H. Hulshoff Pol, H. G. Schnack, R. C. Mandl, R. G. H. Brans, N. E. van Haren, W. F. C. Baare, C. J. van Oel, D. L. Collins, A. C. Evans, and R. S. Kahn, “Gray and white matter density changes in monozygotic and same-sex dizygotic twins discordant for schizophrenia: a voxel-based morphometry study,” presented at 10th Annual Meeting of the Organization for Human Brain Mapping, Budapest, Hungary, 2004.

[7] H. E. Hulshoff Pol, H. G. Schnack, R. C. Mandl, R. G. Brans, N. E. van Haren, W. F. Baare, C. J. van Oel, D. L. Collins, A. C. Evans, and R. S. Kahn, Gray and white matter density changes in monozygotic and same-sex dizygotic twins discordant for schizophrenia using voxel-based morphometry, Neuroimage. 2006; 31(2): 482–8.

[8] N. E. van Haren, H. E. Hulshoff Pol, H. G. Schnack, W. Cahn, R. C. Mandl, D. L. Collins, A. C. Evans, and R. S. Kahn, “Focal Gray Matter Changes in Schizophrenia across the Course of the Illness: A 5-Year Follow-Up Study,” Neuropsychopharmacology. 2007; 32: 2057–66.

In some patients with refractory epilepsy that are candidates for surgery, intracranial EEG is recorded to precisely localize the epileptic focus. To record intracranial EEG, multiple depth electrodes are surgically implanted through holes in the skull, each with 8–15 equally spaced contacts. Current implantation planning consists of visual inspection of the patient’s MRI & CTA, visually searching for paths to the targets while avoiding vessels. This procedure is sub-optimal since estimation of the number of electrodes needed to sample a region and the precise location of each contact, which is paramount to accurately identify the focus and its extent, cannot be considered.

The goal of this project, conducted by Dr. Zelmann, one of my postdoctoral fellows, is to evaluate the clinical use of optimized depth electrode planning. We hypothesize that the use of our computed aided procedure will increase the accuracy and amount of information obtained during EEG intracranial investigation while constraining the trajectories to safe paths.

We have developed a procedure to optimize electrode location that would enable us to obtain complete coverage of a lesion or region of interest and surrounding neocortical grey matter, while minimizing the risk of approaching vessels and other critical structures. To this end, we automatically segment MRI, CT and CTA data; we model each electrode as a cylinder to assess risk; we estimate the contribution of individual contacts to record EEG; we compute an aggregated score for each electrode and a global score, obtaining the best cohort as the combined set of electrodes that remain at a safe distance.

To allow its clinical use during planning and surgical implantation, the procedure is integrated into an image-guided neuronavigation system developed in our research laboratory over the past decade, which is called IBIS (Interactive Brain Imaging System; Mercier et al., Int J CARS. 2011;6(4):507–522). IBIS allows planning and navigation based on preoperative medical image data. The best cohort and a list of possible electrodes per target (ordered in terms of risk and recorded EEG) is generated and displayed in IBIS and will be available during planning and in the OR. The surgeon can visually review trajectories, decide between one of the automatic trajectories and the manual one and, if necessary, re-plan a trajectory in almost real time.

Preliminary results [1] presented at an international workshop on clinical image-based procedures, suggest that automatic planning allows recording from a larger volume than manually planned trajectories (p<0.01) and from more temporal grey matter (p<0.001), while remaining further away from segmented vessels (p<0.01). We are currently evaluating the procedure in retrospective clinically acquired imaging data from 20 patients with electrodes implanted in MTL regions. We are comparing visual and automatic trajectories by estimating volume recording from the target volumes (in this case: amygdala and hippocampus) and neocortical temporal grey matter as well as distance to vessels and other critical structures.

Reference

[1] Zelmann, R., Beriault, S., Mok, K., Haegelen, C., Hall, J., Pike, G. B., Olivier, A & Collins, D. L. (2014). Automatic Optimization of Depth Electrode Trajectory Planning. In Clinical Image-Based Procedures. Translational Research in Medical Imaging (pp. 99–107). Springer International Publishing.

[2] R. Zelmann, S. Beriault, K. Mok, C. Haegelen, J. Hall, G.B. Pike, A. Olivier & D.L. Collins, “Improving Recorded Volume in Mesial Temporal Lobe by Optimizing Stereotactic Intracranial Electrode Implantation Planning” submitted to International Journal of Computer Assisted Radiology and Surgery (CARS-D-14–00251), Oct 9, 2014.

[3] Zelmann, R., Beriault, S., Mok, K., Haegelen, C., Hall, J., Pike, G. B., Olivier, A & Collins, D. L. (2014). Automatic Optimization of Depth Electrode Trajectory Planning. In Clinical Image-Based Procedures. Translational Research in Medical Imaging (pp. 99–107). Springer International Publishing.

[4] R. Zelman, D.L. Collins, “Automatic Optimization of Depth Electrode Trajectory Planning”, MICCAI 2013 Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging

[5] C. Haegelen, P. Perucca, C.-E. Châtillon, L. Andrade-Valença, R. Zelmann, J. Jacobs, D. L. Collins, F. Dubeau, A. Olivier and J. Gotman. “High-frequency oscillations, extent of surgical resection and surgical outcome in drug-resistant focal epilepsy”, Epilepsia 2013 May;54(5):848–57.

[6] Zelmann, R., S. Beriault, M. M. Marinho, K. Mok, J. A. Hall, N. Guizard, C. Haegelen, A. Olivier, G. B. Pike, and D. L. Collins. “Improving recorded volume in mesial temporal lobe by optimizing stereotactic intracranial electrode implantation planning.” International journal of computer assisted radiology and surgery (2015): 1–17.

Autism Spectrum Disorder (ASD) is a set of neurological disorders characterized by impaired social communication and interaction, as well as restricted, repetitive patterns of behaviour, interests and activities. It is commonly diagnosed at around the ages of 3 to 4 years, developing tools that could aid in obtaining a diagnosis as early as 6 or 12 months of age would allow an early therapy that could potentially increase the adaptation to society of children with ASD.

At the NIST, we have utilized voxel-wise image processing methods to investigate the brain development of children at high-risk of ASD during the first 2 years of life. This work has led to observations of significant differences in the growth trajectories of several regions in the brain between children who are diagnosed with ASD at 2 years of age and normal controls.