TY - JOUR
T1 - Use of neuroanatomical pattern regression to predict the structural brain dynamics of vulnerability and transition to psychosis
AU - Koutsouleris, Nikolaos
AU - Gaser, Christian
AU - Bottlender, Ronald
AU - Davatzikos, Christos
AU - Decker, Petra
AU - Jäger, Markus
AU - Schmitt, Gisela
AU - Reiser, Maximilian
AU - Möller, Hans Jürgen
AU - Meisenzahl, Eva M.
N1 - Funding Information:
No funding was provided for the acquisition of MRI data. The German Research Network on Schizophrenia funded by the German Federal Ministry for Education and Research (BMBF) (grant 01 GI 9935) provided financial support for the recruitment and clinical evaluation of the prodromal subjects. Furthermore, the development of methodological procedures used in this study was supported by the BMBF research grants 01EV0709 and 01GW0740 (to Christian Gaser). The funding sources had no involvement in the study design, the collection and analysis of the data or the writing of the manuscript.
PY - 2010/11
Y1 - 2010/11
N2 - Background: The at-risk mental state for psychosis (ARMS) has been associated with abnormal structural brain dynamics underlying disease transition or non-transition. To date, it is unknown whether these dynamic brain changes can be predicted at the single-subject level prior to disease transition using MRI-based machine-learning techniques. Methods: First, deformation-based morphometry and partial-least-squares (PLS) was used to investigate patterns of volumetric changes over time in 25 ARMS individuals versus 28 healthy controls (HC) (1) irrespective of the clinical outcome and (2) according to illness transition or non-transition. Then, the baseline MRI data were employed to predict the expression of these volumetric changes at the individual level using support-vector regression (SVR). Results: PLS revealed a pattern of pronounced morphometric changes in ARMS versus HC that affected predominantly the right prefrontal, as well as the perisylvian, parietal and periventricular structures (p<0.011), and that was more pronounced in the converters versus the non-converters (p<0.010). The SVR analysis facilitated a reliable prediction of these longitudinal brain changes in individual out-of training cases (HC vs ARMS: r=0.83, p<0.001; HC vs converters vs non-converters: r=0.83, p<0.001) by relying on baseline patterns that involved ventricular enlargements, as well as prefrontal, perisylvian, limbic, parietal and subcortical volume reductions. Conclusions: Abnormal brain changes over time may underlie an elevated vulnerability for psychosis and may be most pronounced in subsequent converters to psychosis. Pattern regression techniques may facilitate an accurate prediction of these structural brain dynamics, potentially allowing for an early recognition of individuals at risk of developing psychosis-associated neuroanatomical changes over time.
AB - Background: The at-risk mental state for psychosis (ARMS) has been associated with abnormal structural brain dynamics underlying disease transition or non-transition. To date, it is unknown whether these dynamic brain changes can be predicted at the single-subject level prior to disease transition using MRI-based machine-learning techniques. Methods: First, deformation-based morphometry and partial-least-squares (PLS) was used to investigate patterns of volumetric changes over time in 25 ARMS individuals versus 28 healthy controls (HC) (1) irrespective of the clinical outcome and (2) according to illness transition or non-transition. Then, the baseline MRI data were employed to predict the expression of these volumetric changes at the individual level using support-vector regression (SVR). Results: PLS revealed a pattern of pronounced morphometric changes in ARMS versus HC that affected predominantly the right prefrontal, as well as the perisylvian, parietal and periventricular structures (p<0.011), and that was more pronounced in the converters versus the non-converters (p<0.010). The SVR analysis facilitated a reliable prediction of these longitudinal brain changes in individual out-of training cases (HC vs ARMS: r=0.83, p<0.001; HC vs converters vs non-converters: r=0.83, p<0.001) by relying on baseline patterns that involved ventricular enlargements, as well as prefrontal, perisylvian, limbic, parietal and subcortical volume reductions. Conclusions: Abnormal brain changes over time may underlie an elevated vulnerability for psychosis and may be most pronounced in subsequent converters to psychosis. Pattern regression techniques may facilitate an accurate prediction of these structural brain dynamics, potentially allowing for an early recognition of individuals at risk of developing psychosis-associated neuroanatomical changes over time.
KW - At-risk mental state
KW - Deformation-based morphometry
KW - Early psychosis
KW - Multivariate analyis
KW - Support-vector regression
UR - http://www.scopus.com/inward/record.url?scp=77957908851&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77957908851&partnerID=8YFLogxK
U2 - 10.1016/j.schres.2010.08.032
DO - 10.1016/j.schres.2010.08.032
M3 - Article
C2 - 20850276
AN - SCOPUS:77957908851
SN - 0920-9964
VL - 123
SP - 175
EP - 187
JO - Schizophrenia Research
JF - Schizophrenia Research
IS - 2-3
ER -