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.
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.
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.
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.
Although structural magnetic resonance imaging (MRI) has revealed partly non-overlapping brain abnormalities in schizophrenia and bipolar disorder, it is unknown whether structural MRI scans can be ...used to separate individuals with schizophrenia from those with bipolar disorder. An algorithm capable of discriminating between these two disorders could become a diagnostic aid for psychiatrists. Here, we scanned 66 schizophrenia patients, 66 patients with bipolar disorder and 66 healthy subjects on a 1.5T MRI scanner. Three support vector machines were trained to separate patients with schizophrenia from healthy subjects, patients with schizophrenia from those with bipolar disorder, and patients with bipolar disorder from healthy subjects, respectively, based on their gray matter density images. The predictive power of the models was tested using cross-validation and in an independent validation set of 46 schizophrenia patients, 47 patients with bipolar disorder and 43 healthy subjects scanned on a 3T MRI scanner. Schizophrenia patients could be separated from healthy subjects with an average accuracy of 90%. Additionally, schizophrenia patients and patients with bipolar disorder could be distinguished with an average accuracy of 88%.The model delineating bipolar patients from healthy subjects was less accurate, correctly classifying 67% of the healthy subjects and only 53% of the patients with bipolar disorder. In the latter group, lithium and antipsychotics use had no influence on the classification results. Application of the 1.5T models on the 3T validation set yielded average classification accuracies of 76% (healthy vs schizophrenia), 66% (bipolar vs schizophrenia) and 61% (healthy vs bipolar). In conclusion, the accurate separation of schizophrenia from bipolar patients on the basis of structural MRI scans, as demonstrated here, could be of added value in the differential diagnosis of these two disorders. The results also suggest that gray matter pathology in schizophrenia and bipolar disorder differs to such an extent that they can be reliably differentiated using machine learning paradigms.
•We separated patients with schizophrenia and bipolar disorder based on their sMRI scans.•We trained a support vector machine (SVM) model to do this in a 1.5T ‘discovery set’.•Using cross-validation the model obtained a classification accuracy of >80% in this set.•Applying this model to an independent 3T ‘validation set’ yielded 66% accuracy.•Patterns of brain abnormalities differ between schizophrenia and bipolar disorder.
Surface rendering of MRI brain scans may lead to identification of the participant through facial characteristics. In this study, we evaluate three methods that overwrite voxels containing ...privacy‐sensitive information: Face Masking, FreeSurfer defacing, and FSL defacing. We included structural T1‐weighted MRI scans of children, young adults and older adults. For the young adults, test–retest data were included with a 1‐week interval. The effects of the de‐identification methods were quantified using different statistics to capture random variation and systematic noise in measures obtained through the FreeSurfer processing pipeline. Face Masking and FSL defacing impacted brain voxels in some scans especially in younger participants. FreeSurfer defacing left brain tissue intact in all cases. FSL defacing and FreeSurfer defacing preserved identifiable characteristics around the eyes or mouth in some scans. For all de‐identification methods regional brain measures of subcortical volume, cortical volume, cortical surface area, and cortical thickness were on average highly replicable when derived from original versus de‐identified scans with average regional correlations >.90 for children, young adults, and older adults. Small systematic biases were found that incidentally resulted in significantly different brain measures after de‐identification, depending on the studied subsample, de‐identification method, and brain metric. In young adults, test–retest intraclass correlation coefficients (ICCs) were comparable for original scans and de‐identified scans with average regional ICCs >.90 for (sub)cortical volume and cortical surface area and ICCs >.80 for cortical thickness. We conclude that apparent visual differences between de‐identification methods minimally impact reliability of brain measures, although small systematic biases can occur.
De‐identification methods remove or blur facial characteristics on anatomical MRI scans, but also have a small effect on FreeSurfer brain measures. Furthermore, some methods are too invasive in younger participants or do not always succeed to remove all facial characteristics.
Changes in cortical thickness over time have been related to intelligence, but whether changes in cortical surface area are related to general cognitive functioning is unknown. We therefore examined ...the relationship between intelligence quotient (IQ) and changes in cortical thickness and surface over time in 504 healthy subjects. At 10 years of age, more intelligent children have a slightly thinner cortex than children with a lower IQ. This relationship becomes more pronounced with increasing age: with higher IQ, a faster thinning of the cortex is found over time. In the more intelligent young adults, this relationship reverses so that by the age of 42 a thicker cortex is associated with higher intelligence. In contrast, cortical surface is larger in more intelligent children at the age of 10. The cortical surface is still expanding, reaching its maximum area during adolescence. With higher IQ, cortical expansion is completed at a younger age; and once completed, surface area decreases at a higher rate. These findings suggest that intelligence may be more related to the magnitude and timing of changes in brain structure during development than to brain structure per se, and that the cortex is never completed but shows continuing intelligence-dependent development.
•We present Neuroharmony, a harmonization tool for images from unseen scanners.•We developed Neuroharmony using a total of 15,026 sMRI images.•The tool was able to reduce scanner-related bias from ...unseen scans.•Neuroharmony represents a significant step towards imaging-based clinical tools.•Neuroharmony is available at https://github.com/garciadias/Neuroharmony.
Abstract The objective of this study was to examine exercise effects on global brain volume, hippocampal volume, and cortical thickness in schizophrenia patients and healthy controls. Irrespective of ...diagnosis and intervention, associations between brain changes and cardiorespiratory fitness improvement were examined. Sixty-three schizophrenia patients and fifty-five healthy controls participated in this randomised controlled trial. Global brain volumes, hippocampal volume, and cortical thickness were estimated from 3-Tesla MRI scans. Cardiorespiratory fitness was assessed with a cardiopulmonary ergometer test. Subjects were assigned exercise therapy or occupational therapy (patients) and exercise therapy or life-as-usual (healthy controls) for six months 2 h weekly. Exercise therapy effects were analysed for subjects who were compliant at least 50% of sessions offered. Significantly smaller baseline cerebral (grey) matter, and larger third ventricle volumes, and thinner cortex in most areas of the brain were found in patients versus controls. Exercise therapy did not affect global brain and hippocampal volume or cortical thickness in patients and controls. Cardiorespiratory fitness improvement was related to increased cerebral matter volume and lateral and third ventricle volume decrease in patients and to thickening in the left hemisphere in large areas of the frontal, temporal and cingulate cortex irrespective of diagnosis. One to 2 h of exercise therapy did not elicit significant brain volume changes in patients or controls. However, cardiorespiratory fitness improvement attenuated brain volume changes in schizophrenia patients and increased thickness in large areas of the left cortex in both schizophrenia patients and healthy controls.
Although schizophrenia is characterized by impairments in intelligence and the loss of brain volume, the relationship between changes in IQ and brain measures is not clear.
To investigate the ...association between IQ and brain measures in patients with schizophrenia across time.
Case-control longitudinal study at the Department of Psychiatry at the University Medical Center Utrecht, Utrecht, the Netherlands, comparing patients with schizophrenia and healthy control participants between September 22, 2004, and April 17, 2008. Magnetic resonance imaging of the brain and IQ scores were obtained at baseline and the 3-year follow-up. Participants included 84 patients with schizophrenia (mean illness duration, 4.35 years) and 116 age-matched healthy control participants.
Associations between changes in IQ and the total brain, cerebral gray matter, cerebral white matter, lateral ventricular, third ventricles, cortical, and subcortical volumes; cortical thickness; and cortical surface area.
Cerebral gray matter volume (P = .006) and cortical volume (P = .03) and thickness (P = .02) decreased more in patients with schizophrenia across time compared with control participants. Patients showed additional loss in cortical volume and thickness of the right supramarginal, posterior superior temporal, left supramarginal, left postcentral, and occipital regions (P values were between <.001 and .03 after clusterwise correction). Although IQ increased similarly in patients with schizophrenia and control participants, changes in IQ were negatively correlated with changes in lateral ventricular volume (P = .05) and positively correlated with changes in cortical volume (P = .007) and thickness (P = .004) only in patients with schizophrenia. Positive correlations between changes in IQ and cortical volume and thickness were found globally and in widespread regions across frontal, temporal, and parietal cortices (P values were between <.001 and .03 after clusterwise correction). These findings were independent of symptom severity at follow-up, cannabis use, and the use of cumulative antipsychotic medications during the 3 years of follow-up.
Progressive brain tissue loss in schizophrenia is related to relative cognitive decline during the early course of illness.
The purpose of this study is to create a model that can classify schizophrenia patients and healthy controls based on whole brain gray matter densities (voxel-based morphometry, VBM) from structural ...magnetic resonance imaging (MRI) scans. In addition, we investigated the stability of the accuracy of the models, when built with different sample sizes. Using a support vector machine, we built a model from 239 subjects (128 patients and 111 healthy controls) and classified 71.4% correct (leave-one-out). We replicated and validated this result by testing the unaltered model on a completely independent sample of 277 subjects (155 patients and 122 healthy controls), scanned with a different scanner. The classification rate of the validation sample was 70.4%. The model's discriminative pattern showed, amongst other differences, gray matter density decreases in frontal and superior temporal lobes and hippocampus in schizophrenia patients with respect to healthy controls and increases in gray matter density in basal ganglia and left occipital lobe and. Larger training samples gave more reliable models: Models based on sample sizes smaller than N=130 should be considered unstable and can even score below chance.
► We used VBM (sMRI) to separate individuals with schizophrenia from healthy controls. ► A support vector machine model trained on a large sample (N=239) gave 71% accuracy. ► Application of the model to a replication sample (N=277) validated this result: 70%. ► Larger amounts of training subjects gave higher accuracy and more stable models.
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