Accelerated Brain Aging in Schizophrenia : A Longitudinal Pattern Recognition Study

OBJECTIVE: Despite the multitude of longitudinal neuroimaging studies that have been published, a basic question on the progressive brain loss in schizophrenia remains unaddressed: Does it reflect accelerated aging of the brain, or is it caused by a fundamentally different process? The authors used support vector regression, a supervised machine learning technique, to address this question. METHOD: In a longitudinal sample of 341 schizophrenia patients and 386 healthy subjects with one or more structural MRI scans (1,197 in total), machine learning algorithms were used to build models to predict the age of the brain and the presence of schizophrenia ("schizophrenia score"), based on the gray matter density maps. Age at baseline ranged from 16 to 67 years, and follow-up scans were acquired between 1 and 13 years after the baseline scan. Differences between brain age and chronological age ("brain age gap") and between schizophrenia score and healthy reference score ("schizophrenia gap") were calculated. Accelerated brain aging was calculated from changes in brain age gap between two consecutive measurements. The age prediction model was validated in an independent sample. RESULTS: In schizophrenia patients, brain age was significantly greater than chronological age at baseline (+3.36 years) and progressively increased during follow-up (+1.24 years in addition to the baseline gap). The acceleration of brain aging was not constant: it decreased from 2.5 years/year just after illness onset to about the normal rate (1 year/year) approximately 5 years after illness onset. The schizophrenia gap also increased during follow-up, but more pronounced variability in brain abnormalities at follow-up rendered this increase nonsignificant. CONCLUSIONS: The progressive brain loss in schizophrenia appears to reflect two different processes: one relatively homogeneous, reflecting accelerated aging of the brain and related to various measures of outcome, and a more variable one, possibly reflecting individual variation and medication use. Differentiating between these two processes may not only elucidate the various factors influencing brain loss in schizophrenia, but also assist in individualizing treatment

Accelerated brain aging in schizophrenia : A longitudinal pattern recognition study

OBJECTIVE: Despite the multitude of longitudinal neuroimaging studies that have been published, a basic question on the progressive brain loss in schizophrenia remains unaddressed: Does it reflect accelerated aging of the brain, or is it caused by a fundamentally different process? The authors used support vector regression, a supervised machine learning technique, to address this question. METHOD: In a longitudinal sample of 341 schizophrenia patients and 386 healthy subjects with one or more structural MRI scans (1,197 in total), machine learning algorithms were used to build models to predict the age of the brain and the presence of schizophrenia ("schizophrenia score"), based on the gray matter density maps. Age at baseline ranged from 16 to 67 years, and follow-up scans were acquired between 1 and 13 years after the baseline scan. Differences between brain age and chronological age ("brain age gap") and between schizophrenia score and healthy reference score ("schizophrenia gap") were calculated. Accelerated brain aging was calculated from changes in brain age gap between two consecutive measurements. The age prediction model was validated in an independent sample. RESULTS: In schizophrenia patients, brain age was significantly greater than chronological age at baseline (+3.36 years) and progressively increased during follow-up (+1.24 years in addition to the baseline gap). The acceleration of brain aging was not constant: it decreased from 2.5 years/year just after illness onset to about the normal rate (1 year/year) approximately 5 years after illness onset. The schizophrenia gap also increased during follow-up, but more pronounced variability in brain abnormalities at follow-up rendered this increase nonsignificant. CONCLUSIONS: The progressive brain loss in schizophrenia appears to reflect two different processes: one relatively homogeneous, reflecting accelerated aging of the brain and related to various measures of outcome, and a more variable one, possibly reflecting individual variation and medication use. Differentiating between these two processes may not only elucidate the various factors influencing brain loss in schizophrenia, but also assist in individualizing treatment

Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study

OBJECTIVE: Despite the multitude of longitudinal neuroimaging studies that have been published, a basic question on the progressive brain loss in schizophrenia remains unaddressed: Does it reflect accelerated aging of the brain, or is it caused by a fundamentally different process? The authors used support vector regression, a supervised machine learning technique, to address this question. METHOD: In a longitudinal sample of 341 schizophrenia patients and 386 healthy subjects with one or more structural MRI scans (1,197 in total), machine learning algorithms were used to build models to predict the age of the brain and the presence of schizophrenia ("schizophrenia score"), based on the gray matter density maps. Age at baseline ranged from 16 to 67 years, and follow-up scans were acquired between 1 and 13 years after the baseline scan. Differences between brain age and chronological age ("brain age gap") and between schizophrenia score and healthy reference score ("schizophrenia gap") were calculated. Accelerated brain aging was calculated from changes in brain age gap between two consecutive measurements. The age prediction model was validated in an independent sample. RESULTS: In schizophrenia patients, brain age was significantly greater than chronological age at baseline (+3.36 years) and progressively increased during follow-up (+1.24 years in addition to the baseline gap). The acceleration of brain aging was not constant: it decreased from 2.5 years/year just after illness onset to about the normal rate (1 year/year) approximately 5 years after illness onset. The schizophrenia gap also increased during follow-up, but more pronounced variability in brain abnormalities at follow-up rendered this increase nonsignificant. CONCLUSIONS: The progressive brain loss in schizophrenia appears to reflect two different processes: one relatively homogeneous, reflecting accelerated aging of the brain and related to various measures of outcome, and a more variable one, possibly reflecting individual variation and medication use. Differentiating between these two processes may not only elucidate the various factors influencing brain loss in schizophrenia, but also assist in individualizing treatment

Multi-center MRI prediction models : Predicting sex and illness course in first episode psychosis patients

Structural Magnetic Resonance Imaging (MRI) studies have attempted to use brain measures obtained at the first-episode of psychosis to predict subsequent outcome, with inconsistent results. Thus, there is a real need to validate the utility of brain measures in the prediction of outcome using large datasets, from independent samples, obtained with different protocols and from different MRI scanners. This study had three main aims: 1) to investigate whether structural MRI data from multiple centers can be combined to create a machine-learning model able to predict a strong biological variable like sex; 2) to replicate our previous finding that an MRI scan obtained at first episode significantly predicts subsequent illness course in other independent datasets; and finally, 3) to test whether these datasets can be combined to generate multicenter models with better accuracy in the prediction of illness course. The multi-center sample included brain structural MRI scans from 256 males and 133 females patients with first episode psychosis, acquired in five centers: University Medical Center Utrecht (The Netherlands) (n = 67); Institute of Psychiatry, Psychology and Neuroscience, London (United Kingdom) (n = 97); University of São Paulo (Brazil) (n = 64); University of Cantabria, Santander (Spain) (n = 107); and University of Melbourne (Australia) (n = 54). All images were acquired on 1.5-Tesla scanners and all centers provided information on illness course during a follow-up period ranging 3 to 7 years. We only included in the analyses of outcome prediction patients for whom illness course was categorized as either “continuous” (n = 94) or “remitting” (n = 118). Using structural brain scans from all centers, sex was predicted with significant accuracy (89%; p <0.001). In the single- or multi-center models, illness course could not be predicted with significant accuracy. However, when reducing heterogeneity by restricting the analyses to male patients only, classification accuracy improved in some samples. This study provides proof of concept that combining multi-center MRI data to create a well performing classification model is possible. However, to create complex multi-center models that perform accurately, each center should contribute a sample either large or homogeneous enough to first allow accurate classification within the single-center