Peripheral inflammation levels associated with degree of advanced brain aging in schizophrenia

Klaus, Federica; Nguyen, Tanya; Michael, Thomas; Liou, Sharon C.; Soontornniyomkij, Benchawanna; Mitchell, Kyle R.; Daly, Rebecca; Sutherland, Ashley; Jeste, Dilip V.; Eyler, Lisa T.

doi:10.3389/fpsyt.2022.966439

Cited by 8 publications

(7 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Gaussian process regression algorithm [24,[48][49][50][51] with fivefold crossvalidation was used to train and assess the 90, 90, and 48 models for GM, Fig. 1 Flow of data processing and brain-age prediction model construction.…”

Section: Brain Age Prediction and Brain Age Gap Calculationmentioning

confidence: 99%

“…This index is mainly used to examine whether the neurological and psychiatric disorders of participants are associated with accelerated brain aging [13]. 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: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Brain Age Prediction and Brain Age Gap Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…In addition, individuals with schizophrenia had higher brain age than their chronological age at early stage 12 , 15 . Notably, a recent study found a positive correlation between the brain age gap and cytokine TNFα in schizophrenia 16 . Constantinides et al included 26 international cohorts via the ENIGMA consortium to test the brain age gap between individuals with schizophrenia and healthy controls based on 77 FreeSurfer features.…”

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 96%

“…The difference between brain age and chronological age, termed the brain age gap, can serve as a parameter for determining correlations with participants' clinicodemographic characteristics 9 . T1-weighted MRI studies have used brain age to examine disease-related brain deterioration in schizophrenia [10][11][12][13][14][15][16] . Schnack and colleagues reported that the brain ages of individuals with schizophrenia were approximately 3.36 years higher than their chronological ages 10 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Ageing-related changes in the regulation of microglia and their interaction with neurons

von Bernhardi,

Eugenín

2025

Neuropharmacology

View full text Add to dashboard Cite

Peripheral inflammation levels associated with degree of advanced brain aging in schizophrenia

Cited by 8 publications

References 67 publications

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

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

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

Ageing-related changes in the regulation of microglia and their interaction with neurons

Contact Info

Product

Resources

About