A possible white matter compensating mechanism of relatives of people affected by psychosis
Poster No:
512
Submission Type:
Abstract Submission
Authors:
Yaron Caspi1
Institutions:
1UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
First Author:
Yaron Caspi
UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht
Utrecht, The Netherlands
UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht
Utrecht, The Netherlands
Introduction:
Extensive findings concerning subjects diagnosed with psychosis (D), their first-degree relatives, and the general population fit the 'Familial Risk' model. I.e., some alterations in the brain of the D group will be detected to a lesser extent in the brain of their relatives (1,2).
An alternative, though not mutually exclusive, model ('Compensating Mechanisms') suggests that relatives of the D group will exhibit some features different from those of the affected probands but also different from the general population (3).
Since such a model might represent some plasticity or resilience (3,4) features of the relatives' brains, it is valuable to consider the evidence. This abstract presents such evidence based on a recent publication (5).
An alternative, though not mutually exclusive, model ('Compensating Mechanisms') suggests that relatives of the D group will exhibit some features different from those of the affected probands but also different from the general population (3).
Since such a model might represent some plasticity or resilience (3,4) features of the relatives' brains, it is valuable to consider the evidence. This abstract presents such evidence based on a recent publication (5).
Methods:
We used the Diffusion Tensor Imaging (DTI) results from the three epochs of the Utrecht GROUP cohort. Namely, a D group (82 people), a control group (89 people), and relatives (all siblings) (122 people) that had two or three consecutive scans and passed our quality control (6,7).
For each individual, we averaged the results of the different epochs and calculated a set of diffusivity measures (for 48 distinct tracts) for four different diffusivity modes (Fractional Anisotropy (FA), mean diffusivity (MD), Axial Diffusivity (AD), and Radial Diffusivity (RD)), and a set of connectivity measures (thirteen graph matrix measures) for four different modes of connectivity matrix calculation: (a) deterministic tracking algorithm; (b) probabilistic tracking algorithm; (c) sampling FA along the tracts obtained from the deterministic algorithm; (d) sampling FA along the tracts obtained from the probabilistic algorithm.
We analyzed these results (diffusivity and connectivity) on two levels. First, we used analysis of covariance (ANCOVA) Measure ~ Age + Ggroup followed by Tukey's test analysis (for representative results see Fig. 1). Second, we directly calculated the age-dependent epoch-averaged slope for each group, fitted the data to one out of three linear models (independent variables – Age, Age + Sex, Age + Sex + GROUP) based on quality of fit, and studied cases where the p-value of the model slope was below 0.05.
Finally, we calculated scoring results for each diffusivity mode and the four connectivity matrix calculation methods. E.g., for the ANCONA analysis, the score was equal to ((0.05- T)*M) for each relevant measure from each analysis mode. T is the Tukey's False Discovery Rate (FDR) corrected p-value. The multiplication factor (M) was equal to 1, 0.5, and 1/3 for cases where only one, two, or three of the three group comparisons had a p-value below 0.05. Finally, we summed up the scores for all measures related to each diffusivity or connectivity analysis mode. Such scoring calculation, which represents the main results of this study, identified a general pattern of difference between the three groups.
For each individual, we averaged the results of the different epochs and calculated a set of diffusivity measures (for 48 distinct tracts) for four different diffusivity modes (Fractional Anisotropy (FA), mean diffusivity (MD), Axial Diffusivity (AD), and Radial Diffusivity (RD)), and a set of connectivity measures (thirteen graph matrix measures) for four different modes of connectivity matrix calculation: (a) deterministic tracking algorithm; (b) probabilistic tracking algorithm; (c) sampling FA along the tracts obtained from the deterministic algorithm; (d) sampling FA along the tracts obtained from the probabilistic algorithm.
We analyzed these results (diffusivity and connectivity) on two levels. First, we used analysis of covariance (ANCOVA) Measure ~ Age + Ggroup followed by Tukey's test analysis (for representative results see Fig. 1). Second, we directly calculated the age-dependent epoch-averaged slope for each group, fitted the data to one out of three linear models (independent variables – Age, Age + Sex, Age + Sex + GROUP) based on quality of fit, and studied cases where the p-value of the model slope was below 0.05.
Finally, we calculated scoring results for each diffusivity mode and the four connectivity matrix calculation methods. E.g., for the ANCONA analysis, the score was equal to ((0.05- T)*M) for each relevant measure from each analysis mode. T is the Tukey's False Discovery Rate (FDR) corrected p-value. The multiplication factor (M) was equal to 1, 0.5, and 1/3 for cases where only one, two, or three of the three group comparisons had a p-value below 0.05. Finally, we summed up the scores for all measures related to each diffusivity or connectivity analysis mode. Such scoring calculation, which represents the main results of this study, identified a general pattern of difference between the three groups.
Results:
For score results, see Fig. 2.
For Tukey's test, the scoring results of the FA stood as an almost unique characteristic, showing scores sum differences between the relatives and the other two groups (mainly the general population). Only the scores sum of connectivity measures using deterministic tractography supports the 'Familial Risk' model.
For the epoch-averaged slope analysis, only for AD and RD, the scoring system supports a naive 'Familial Risk' model where the value of the relatives' group is intermediate between that of the control and that of the diagnosed group. By contrast, the relatives' group showed either the most or the least epoch-averaged age-dependent behavior relative to the two other groups for FA, MD, and three of the four connectivity modalities.
For Tukey's test, the scoring results of the FA stood as an almost unique characteristic, showing scores sum differences between the relatives and the other two groups (mainly the general population). Only the scores sum of connectivity measures using deterministic tractography supports the 'Familial Risk' model.
For the epoch-averaged slope analysis, only for AD and RD, the scoring system supports a naive 'Familial Risk' model where the value of the relatives' group is intermediate between that of the control and that of the diagnosed group. By contrast, the relatives' group showed either the most or the least epoch-averaged age-dependent behavior relative to the two other groups for FA, MD, and three of the four connectivity modalities.
Conclusions:
We showed evidence for a putative white matter-based structural compensation mechanism in relatives' brains. Such a mechanism might protect the relatives group against the deleterious load associated with their genetic background. These findings contribute to the growing discussion about brain plasticity in psychiatry (8).
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Keywords:
Plasticity
Psychiatric
Schizophrenia
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
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3. Dazzan P (2018), 'Not just risk: there is also resilience and we should understand its neurobiological basis'. Schizophrenia Research, vol. 193, pp. 293–294
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5. Caspi Y (2022), 'A Possible White Matter Compensating Mechanism in the Brain of Relatives of People Affected by Psychosis Inferred from Repeated Long-Term DTI Scans'. Schizophrenia Bulletin Open, vol. 3, no. 1, pp. sgac055
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8. Chen X, Tan W, Cheng Y, Huang D, Liu D, Zhang J, et al. (2023), 'Polygenic risk for schizophrenia and the language network: Putative compensatory reorganization in unaffected siblings', Psychiatry Research, vol. 326, pp. 115319