Neurological Disorders

Schizophrenia

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.

Epilepsy

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.

Multiple Sclerosis

Multiple sclerosis (MS) is a neurological disease that predominately affects young adults. Inflammatory mechanisms were believed to be the main contributor to the development of MS. However, more recent neuropathological and magnetic resonance imaging (MRI) studies suggest that neurodegenerative processes play an equivalent central role, that these degenerative processes commence during the early stages of the disease, and that neuronal and axonal loss may be the key substrate for the development of disability. One macroscopic hallmark of neurodegeneration is brain atrophy, which can be readily investigated non-invasively using MRI. We have recently demonstrated brain atrophy in pediatric-onset MS patients, further supporting a very early and possibly primary role for neurodegeneration in MS.

The three current hypotheses for the specific pathobiology underlying brain atrophy in MS are:

1) atrophy in focal lesions and normal-appearing brain tissue occurs secondary to neuroaxonal injury and Wallerian degeneration and loss of normal myelin or reduction in myelin density associated with inflammation-mediated tissue insult;
2) atrophy is due to a diffuse, primary degenerative process associated with neuronal cell death and subsequent loss of axons and myelinated pathways; and
3) atrophy is due to a combination of mechanisms 1 and 2. In children with MS, insult to precursors of primary myelination may further impede normal brain maturation contributing to failure of age-expected brain growth in addition to atrophic loss of established neural networks.

The onset of MS during childhood and adolescence provides a potentially enhanced capacity to distinguish the earliest aspects of MS pathobiology, as the young age of such patients inherently limits the time period available for subclinical disease. A further unique aspect of pediatric onset MS is the potential deleterious impact of MS pathobiology to the processes of primary myelination and normal brain maturation, and thus potential consequence of MS contributing to failure of age-expected brain growth. We have recently reported reduced brain volumes in pediatric-onset MS compared to pediatric normal controls in cross-sectional studies. We also noted that the reduction in brain volume was not only global, but also specifically notable in the thalamus. We now propose to delineate whether the reduced brain volumes reflect age-expected failure of normative growth or loss of previously developed brain tissue (atrophy), or both, and will further explore the selective vulnerability of specific brain regions.

MRI confirmation of progressive brain volume loss, detectable in children and adolescents with MS, will not only refute the concept of pediatric brain resiliency and enhanced repair, but will also emphasize the fundamental nature of neurodegenerative biology of MS. Such confirmation has significant import on future therapeutic strategies, as it implies that anti-inflammatory therapies alone may fail to mitigate the negative impact of MS, and that neuroprotective strategies will be required from onset. This study is supported by an operating grant (Biomedical Research) provided by the Multiple Sclerosis Society of Canada & The Multiple Sclerosis Scientific Research Foundation.

Reference

[1] R. Harmouche, N.K. Subbanna, D.L. Collins, D.L. Arnold, T. Arbel, “Probabilistic multiple sclerosis lesion classification based on modeling regional intensity variability and local neighbourhood information”, IEEE Transactions on Biomedical Engineering, 2015 May; 62(5): 1281–92.

[2] N Guizard, P Coupé, VS Fonov, JV Manjón, DL Arnold, DL Collins, “Rotation-invariant multi-contrast non-local means for MS lesion segmentation (RMNMS)”, NeuroImage: Clinical. 2015 May 8: 376–89.

[3] K. Weier, B. Banwell, A. Cerasa, D.L. Collins, A. Dogonowski, H. Lassmann, A. Quattrone, M.A. Sahraian, H.R. Siebner and T. Sprenger, “The role of the cerebellum in multiple sclerosis”, Cerebellum. 2015 Jun; 14(3); 364–74

[4] Aubert-Broche B, Fonov VS, Garcia-Lorenzo D, Mouiha A, Guizard N, Coupé P, Eskildsen SF, Collins DL. A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood. Neuroimage 2013;82C:393–402.

[5] Aubert-Broche B, Fonov V, Ghassemi R, Arnold DL, Banwell B, Sled JG, Collins DL. Regional brain atrophy in children with multiple sclerosis. Neuroimage 2011;58:409–415.

[6] Kerbrat A, Aubert-Broche B, Fonov V, Narayanan S, Sled JG, Arnold DL, Banwell B, Collins DL. Reduced head and brain size for age and disproportionately smaller thalami in child-onset MS. Neurology 2012;78:194–201.

Autism Spectrum Disorder

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.