Betty Tijms PhD

Betty Tijms


Publications:
2015
  Grey matter networks in people at increased familial risk for schizophrenia
Tijms, BM, Sprooten, E, Job, D, Johnstone, EC, Owens, DGC, Willshaw, D, Seriès, P & Lawrie, SM 2015, 'Grey matter networks in people at increased familial risk for schizophrenia' Schizophrenia Research, vol 168, no. 1-2, pp. 1-8. DOI: 10.1016/j.schres.2015.08.025

Grey matter brain networks are disrupted in schizophrenia, but it is still unclear at which point during the development of the illness these disruptions arise and whether these can be associated with behavioural predictors of schizophrenia. We investigated if single-subject grey matter networks were disrupted in a sample of people at familial risk of schizophrenia. Single-subject grey matter networks were extracted from structural MRI scans of 144 high risk subjects, 32 recent-onset patients and 36 healthy controls. The following network properties were calculated: size, connectivity density, degree, path length, clustering coefficient, betweenness centrality and small world properties. People at risk of schizophrenia showed decreased path length and clustering in mostly prefrontal and temporal areas. Within the high risk sample, the path length of the posterior cingulate cortex and the betweenness centrality of the left inferior frontal operculum explained 81% of the variance in schizotypal cognitions, which was previously shown to be the strongest behavioural predictor of schizophrenia in the study. In contrast, local grey matter volume measurements explained 48% of variance in schizotypy. The present results suggest that single-subject grey matter networks can quantify behaviourally relevant biological alterations in people at increased risk for schizophrenia before disease onset.


General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Tijms, Betty M, Sprooten, Emma, Job, Dominic, Johnstone, Eve C, Owens, David G C, Willshaw, David, Seriès, Peggy & Lawrie, Stephen M.
Number of pages: 8
Pages: 1-8
Publication Date: Oct 2015
Publication Information
Category: Article
Journal: Schizophrenia Research
Volume: 168
Issue number: 1-2
ISSN: 0920-9964
Original Language: English
DOIs: 10.1016/j.schres.2015.08.025
2012
  Similarity-Based Extraction of Individual Networks from Gray Matter MRI Scans
Tijms, BM, Series, P, Willshaw, DJ & Lawrie, SM 2012, 'Similarity-Based Extraction of Individual Networks from Gray Matter MRI Scans' Cerebral Cortex, vol 22, no. 7, pp. 1530-1541. DOI: 10.1093/cercor/bhr221
The characterization of gray matter morphology of individual brains is an important issue in neuroscience. Graph theory has been used to describe cortical morphology, with networks based on covariation of gray matter volume or thickness between cortical areas across people. Here, we extend this research by proposing a new method that describes the gray matter morphology of an individual cortex as a network. In these large-scale morphological networks, nodes represent small cortical regions, and edges connect regions that have a statistically similar structure. The method was applied to a healthy sample (n = 14, scanned at 2 different time points). For all networks, we described the spatial degree distribution, average minimum path length, average clustering coefficient, small world property, and betweenness centrality (BC). Finally, we studied the reproducibility of all these properties. The networks showed more clustering than random networks and a similar minimum path length, indicating that they were ?small world.? The spatial degree and BC distributions corresponded closely to those from group-derived networks. All network property values were reproducible over the 2 time points examined. Our results demonstrate that intracortical similarities can be used to provide a robust statistical description of individual gray matter morphology.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Tijms, Betty M., Series, Peggy, Willshaw, David J. & Lawrie, Stephen M..
Keywords: (graph theory, gray matter, individual networks, magnetic resonance imaging, morphometry, , , . )
Number of pages: 12
Pages: 1530-1541
Publication Date: 2012
Publication Information
Category: Article
Journal: Cerebral Cortex
Volume: 22
Issue number: 7
Original Language: English
DOIs: 10.1093/cercor/bhr221
2010
  Individual anatomical networks, constructed from grey matter MRI scans
Tijms, B, Series, P, Willshaw, D, Lawrie, S & Job, D 2010, 'Individual anatomical networks, constructed from grey matter MRI scans' 16th Annual Meeting of the Organization for Human Brain Mapping, Barcelona, Spain, 6/06/10 - 10/06/10, .
Recently graph theoretical techniques have demonstrated that functional and structural MRI scans show a small-world topology (Bullmore & Sporns, 2009). A small world topology is argued to be evolutionary optimal, as it minimizes wiring length while optimizing network connectivity (Chklovskii et al., 2004). While techniques exist to extract individual networks from functional MRI and DTI data, no such technique exists for grey matter. The cortex is very variable over people, a property related to individual differences in cognitive function (Kennedy et al., 1998).
We developed a method to construct individual anatomical networks from grey matter structural MRI scans. The method is sensitive for focal abnormalities that are of most interest in clinical populations, because it operates in the scan’s native space, thereby minimizing addition of noise from registration and warping procedures. The method was applied to healthy subjects to assess its validity and stability.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Tijms, Betty, Series, Peggy, Willshaw, David, Lawrie, Stephen & Job, Dominic.
Publication Date: 2010
Publication Information
Category: Poster
Original Language: English
2009
  A voxel based method for binarized white matter extraction that leaves high frequency components intact.
Tijms, BM, Whalley, HC, Job, DE, Willshaw, DJ, Lawrie, SM & Moorhead, TW 2009, 'A voxel based method for binarized white matter extraction that leaves high frequency components intact.' NeuroImage, vol 47, Supplement 1, no. 0, pp. S99 -. DOI: http://dx.doi.org/10.1016/S1053-8119(09)70834-1
We have developed an algorithm to recover white matter from MRI scans that includes high frequency spatial components. Such components are of interest in clinical populations and essential for estimates of cortical thickness, but are absent in techniques that involve smoothing. The proposed method builds upon the established tissue classification provided by SPM (Ashburner & Friston, 2000; 2005) extending white matter classification to recover the full extent of white matter. To generate a continuous white matter surface, we binarized the white matter classification. We validated our algorithm by comparison with manual segmentation.
General Information
Organisations: Deanery of Molecular, Genetic and Population Health Sciences.
Authors: Tijms, B.M., Whalley, H.C., Job, D.E., Willshaw, D.J., Lawrie, S.M. & Moorhead, T.W..
Pages: S99 -
Publication Date: 2009
Publication Information
Category: Article
Journal: NeuroImage
Volume: 47, Supplement 1
Issue number: 0
ISSN: 1053-8119
Original Language: English
DOIs: 10.1016/S1053-8119(09)70834-1
  Cortical thickness in a cohort of individuals at high genetic risk of schizophrenia.
Tijms, BM, Moorhead, TW, Whalley, HC, Job, DE, Willshaw, DJ, McIntosh, AM, Johnstone, EC & Lawrie, SM 2009, 'Cortical thickness in a cohort of individuals at high genetic risk of schizophrenia.' NeuroImage, vol 47, Supplement 1, no. 0, pp. S121 -. DOI: http://dx.doi.org/10.1016/S1053-8119(09)71152-8
The Edinburgh High Risk Study for schizophrenia aims to find factors related to the development of schizophrenia. We previously reported an increased gyrification of the right prefrontal cortex of high risk people who became ill in comparison to high risk people who remained well (Harris et al., 2004). The current objective was to study whether cortical thickness could also be different in this cohort. Cortical thickness was extracted from baseline structural MRI scans by computing the distance between the white matter boundary and the gyrification trace with a voxel based city block algorithm (Lohman et al., 2003). This method was developed in the Imaging Laboratory (Sackler Institute of Psychobiological Research) at the Division of Psychiatry, Edinburgh University.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Tijms, B.M., Moorhead, T.W., Whalley, H.C., Job, D.E., Willshaw, D.J., McIntosh, A.M., Johnstone, E.C. & Lawrie, S.M..
Pages: S121 -
Publication Date: 2009
Publication Information
Category: Article
Journal: NeuroImage
Volume: 47, Supplement 1
Issue number: 0
ISSN: 1053-8119
Original Language: English
DOIs: 10.1016/S1053-8119(09)71152-8

Projects:
Structural cortical connectivity changes in a cohort of people at high risk at schizophrenia (PhD)