Detection of advanced brain aging in schizophrenia and its structural underpinning by using normative brain age metrics

Chen, Changle; Hwang, Tzung-Jeng; Tung, Yung-Liang; Yang, Liying; Hsu, Ying Shao; Liu, Chih-Min; Lin, Yi-Tin; Hsieh, Meng-Long; Chien, Yi‐Ling; Hwu, Hai‐Gwo; Tseng, Wen‐Yih Isaac

doi:10.1016/j.nicl.2022.103003

Cited by 15 publications

(11 citation statements)

References 78 publications

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“…A recent study 52 showed that early antipsychotic medication use might reduce the brain age gap in patients with first-episode schizophrenia. In contrast, the illness duration of the participants was longer in our study (mean: 15.56 years, ranging from 0 to 38 years), and our result showed a lack of association between medication and the brain age gap, which is consistent with the previous studies 17 , 18 , 23 . Therefore, we speculate that the brain age gap may be more sensitive to first-episode schizophrenia patients with early medication use but less sensitive to chronic schizophrenia patients with antipsychotic medication use.…”

Section: Discussionsupporting

confidence: 91%

“…Demro et al demonstrated that the disease course of psychotic illness was not significantly correlated with their brain age gap 14 . Chen et al also found that the normalized predicted age difference did not show a significant correlation with the duration of illness in schizophrenia 23 . We found that previous studies had inconsistent results on the association between the brain age gap and duration of illness in individuals with schizophrenia 14,17,21,22 .…”

Section: Introductionmentioning

confidence: 96%

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Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia

et al. 2023

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Brain-age prediction is a novel approach to assessing deviated brain aging trajectories in different diseases. However, most studies have used an average brain age gap (BAG) of individuals with schizophrenia of different illness durations for comparison with healthy participants. Therefore, this study investigated whether declined brain structures as reflected by BAGs may be present in schizophrenia in terms of brain volume, cortical thickness, and fractional anisotropy across different illness durations. We used brain volume, cortical thickness, and fractional anisotropy as features to train three models from the training dataset. Three models were applied to predict brain ages in the hold-out test and schizophrenia datasets and calculate BAGs. We divided the schizophrenia dataset into multiple groups based on the illness duration using a sliding time window approach for ANCOVA analysis. The brain volume and cortical thickness models revealed that, in comparison with healthy controls, individuals with schizophrenia had larger BAGs across different illness durations, whereas the BAG in terms of fractional anisotropy did not differ from that of healthy controls after disease onset. Moreover, the BAG at the initial stage of schizophrenia was the largest in the cortical thickness model. In contrast, the BAG from approximately two decades after disease onset was the largest in the brain volume model. Our findings suggest that schizophrenia differentially affects the decline of different brain structures during the disease course. Moreover, different trends of decline in thickness and volume-based measures suggest a differential decline in dimensions of brain structure throughout the course of schizophrenia.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 96%

Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia

et al. 2023

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“…Previous studies on brain-age prediction have also demonstrated that individuals with schizophrenia exhibit deviations in brain aging trajectories, as indicated in both T1-weighted magnetic resonance imaging (MRI) [16][17][18][19][20][21][22][23][24] and DTI [25,26] findings. The recent studies used multimodal MRI to evaluate the brain age gap in patients with schizophrenia and obtained results consistent with those of studies using a single neuroimaging modality [27][28][29][30].…”

Section: Introductionmentioning

confidence: 53%

“…In our study, most brain regions exhibited brain aging acceleration in individuals with schizophrenia, but only 22 brain regions and 10 white matter tracts had positive associations with illness duration. In previous studies, brain age gap was nonsignificantly correlated with illness duration because the studies did not construct brain-region-differentiated prediction models of brain age [18,26,28].…”

Section: Discussionmentioning

confidence: 96%

“…regions [27]. Chen et al found that cortical thickness significantly contributes to normalized predicted age differences in schizophrenia in the frontal lobe, bilateral precunei, middle temporal gyri, temporal poles, lateral orbitofrontal gyri, and superior parietal gyri [28]. Man et al found that the subcortical regions and medial and lateral prefrontal cortices had the most significant negative correlations between GM volume and the brain age gap [21].…”

Section: Discussionmentioning

confidence: 99%

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Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study

Zhu

Tsai

et al. 2023

Transl Psychiatry

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Although many studies on brain-age prediction in patients with schizophrenia have been reported recently, none has predicted brain age based on different neuroimaging modalities and different brain regions in these patients. Here, we constructed brain-age prediction models with multimodal MRI and examined the deviations of aging trajectories in different brain regions of participants with schizophrenia recruited from multiple centers. The data of 230 healthy controls (HCs) were used for model training. Next, we investigated the differences in brain age gaps between participants with schizophrenia and HCs from two independent cohorts. A Gaussian process regression algorithm with fivefold cross-validation was used to train 90, 90, and 48 models for gray matter (GM), functional connectivity (FC), and fractional anisotropy (FA) maps in the training dataset, respectively. The brain age gaps in different brain regions for all participants were calculated, and the differences in brain age gaps between the two groups were examined. Our results showed that most GM regions in participants with schizophrenia in both cohorts exhibited accelerated aging, particularly in the frontal lobe, temporal lobe, and insula. The parts of the white matter tracts, including the cerebrum and cerebellum, indicated deviations in aging trajectories in participants with schizophrenia. However, no accelerated brain aging was noted in the FC maps. The accelerated aging in 22 GM regions and 10 white matter tracts in schizophrenia potentially exacerbates with disease progression. In individuals with schizophrenia, different brain regions demonstrate dynamic deviations of brain aging trajectories. Our findings provided more insights into schizophrenia neuropathology.

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Contrasting Case-Control and Normative Reference Approaches to Capture Clinically Relevant Structural Brain Abnormalities in Patients With First-Episode Psychosis Who Are Antipsychotic Naive

et al. 2022

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ImportanceTo make progress toward precision psychiatry, it is crucial to move beyond case-control studies and instead capture individual variations and interpret them in the context of a normal range of biological systems.ObjectiveTo evaluate whether baseline deviations from a normative reference range in subcortical volumes are better predictors of antipsychotic treatment response than raw volumes in patients with first-episode psychosis (FEP) who were naive to antipsychotic medication.Design, Setting, and ParticipantsIn this prospective longitudinal study, patients with first-episode psychosis who were referred from different clinical settings (emergency department, inpatient units, and outpatient clinics) at the University of Alabama at Birmingham were included. A total of 286 patients were screened, 114 consented, 104 enrolled in the treatment trial, and 85 completed the trial. Patients were observed for 16 weeks. Controls were matched by age and sex. Data were collected between June 2016 and July 2021, and data were analyzed from August 2021 to June 2022.InterventionsRisperidone on a flexible dosing scheme for 16 weeks. There was an option to switch to aripiprazole for excessive adverse effects.Main Outcomes and MeasuresThe main outcome of this study was to evaluate, in patients with FEP who were naive to antipsychotic medication, the association of baseline raw volumes and volume deviations in subcortical brain regions with response to antipsychotic medication. Raw brain volumes or volume deviation changes after treatment were not examined.ResultsOf 190 included participants, 111 (58.4%) were male, and the mean (SD) age was 23.7 (5.5) years. Volumes and deviations were quantified in 98 patients with FEP, and data from 92 controls were used as comparison for case-control contrasts and reference curve calibration. In case-control contrasts, patients with FEP had lower raw thalamus (P = .002; F = 9.63; df = 1), hippocampus (P = .009; F = 17.23; df = 1), amygdala (P = .01; F = 6.55; df = 1), ventral diencephalon (P = .03; F = 4.84; df = 1), and brainstem volumes (P = .004; F = 8.39; df = 1). Of 98 patients, 36 patients with FEP (36%) displayed extreme deviations. Associations with treatment response significantly differed between raw volume and deviation measures in the caudate (z = −2.17; P = .03) and putamen (z = −2.15; P = .03).Conclusions and RelevanceThese data suggest that normative modeling allows capture of interindividual heterogeneity of regional brain volumes in patients with FEP and characterize structural pathology in a clinically relevant fashion. This holds promise for progress in precision medicine in psychiatry, where group-level studies have failed to derive reliable maps of structural pathology.

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Detection of advanced brain aging in schizophrenia and its structural underpinning by using normative brain age metrics

Cited by 15 publications

References 78 publications

Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia

Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia

Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study

Contrasting Case-Control and Normative Reference Approaches to Capture Clinically Relevant Structural Brain Abnormalities in Patients With First-Episode Psychosis Who Are Antipsychotic Naive

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