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1.
Article in English | MEDLINE | ID: mdl-34555562

ABSTRACT

BACKGROUND: Most psychiatric disorders emerge in the second decade of life. In the present study, we examined whether environmental adversity, developmental antecedents, major depressive disorder, and functional impairment correlate with deviation from normative brain development in adolescence. METHODS: We trained a brain age prediction model using 189 structural magnetic resonance imaging brain features in 1299 typically developing adolescents (age range 9-19 years, mean = 13.5, SD = 3.04), validated the model in a holdout set of 322 adolescents (mean = 13.5, SD = 3.07), and used it to predict age in an independent risk-enriched cohort of 150 adolescents (mean = 13.6, SD = 2.82). We tested associations between the brain age gap and adversity, early antecedents, depression, and functional impairment. RESULTS: We accurately predicted chronological age in typically developing adolescents (mean absolute error = 1.53 years). The model generalized to the validation set (mean absolute error = 1.55 years, 1.98 bias adjusted) and to the independent at-risk sample (mean absolute error = 1.49 years, 1.86 bias adjusted). The brain age estimate was reliable in repeated scans (intraclass correlation = 0.94). Experience of environmental adversity (ß = 0.18; 95% CI, 0.04 to 0.31; p = .02), diagnosis of major depressive disorder (ß = 0.61; 95% CI, 0.23 to 0.99; p = .01), and functional impairment (ß = 0.16; 95% CI, 0.05 to 0.27; p = .01) were associated with a positive brain age gap. CONCLUSIONS: Risk factors, diagnosis, and impact of mental illness are associated with an older-appearing brain during development.


Subject(s)
Depressive Disorder, Major , Adolescent , Adult , Brain , Child , Depression , Depressive Disorder, Major/psychology , Humans , Infant , Magnetic Resonance Imaging/methods , Neuroimaging , Young Adult
2.
Brain Behav ; 10(6): e01609, 2020 06.
Article in English | MEDLINE | ID: mdl-32304355

ABSTRACT

INTRODUCTION: A new generation of large-scale studies is using neuroimaging to investigate adolescent brain development across health and disease. However, imaging artifacts such as head motion remain a challenge and may be exacerbated in pediatric clinical samples. In this study, we assessed the scan-rescan reliability of multimodal MRI in a sample of youth enriched for risk of mental illness. METHODS: We obtained repeated MRI scans, an average of 2.7 ± 1.4 weeks apart, from 50 youth (mean age 14.7 years, SD = 4.4). Half of the sample (52%) had a diagnosis of an anxiety disorder; 22% had attention-deficit/hyperactivity disorder (ADHD). We quantified reliability with the test-retest intraclass correlation coefficient (ICC). RESULTS: Gray matter measurements were highly reliable with mean ICCs as follows: cortical volume (ICC = 0.90), cortical surface area (ICC = 0.89), cortical thickness (ICC = 0.82), and local gyrification index (ICC = 0.85). White matter volume reliability was excellent (ICC = 0.98). Diffusion tensor imaging (DTI) components were also highly reliable. Fractional anisotropy was most consistently measured (ICC = 0.88), followed by radial diffusivity (ICC = 0.84), mean diffusivity (ICC = 0.81), and axial diffusivity (ICC = 0.78). We also observed regional variability in reconstruction, with some brain structures less reliably reconstructed than others. CONCLUSIONS: Overall, we showed that developmental MRI measures are highly reliable, even in youth at risk for mental illness and those already affected by anxiety and neurodevelopmental disorders. Yet, caution is warranted if patterns of results cluster within regions of lower reliability.


Subject(s)
Attention Deficit Disorder with Hyperactivity , Diffusion Tensor Imaging , Adolescent , Attention Deficit Disorder with Hyperactivity/diagnostic imaging , Brain/diagnostic imaging , Child , Humans , Magnetic Resonance Imaging , Neuroimaging , Reproducibility of Results
3.
J Psychiatry Neurosci ; 45(2): 125-133, 2020 03 01.
Article in English | MEDLINE | ID: mdl-31674733

ABSTRACT

Background: Cortical folding is essential for healthy brain development. Previous studies have found regional reductions in cortical folding in adult patients with psychotic illness. It is unknown whether these neuroanatomical markers are present in youth with subclinical psychotic symptoms. Methods: We collected MRIs and examined the local gyrification index in a sample of 110 youth (mean age ± standard deviation 14.0 ± 3.7 yr; range 9­25 yr) with a family history of severe mental illness: 48 with psychotic symptoms and 62 without. Images were processed using the Human Connectome Pipeline and FreeSurfer. We tested for group differences in local gyrification index using mixed-effects generalized linear models controlling for age, sex and familial clustering. Sensitivity analysis further controlled for intracranial volume, IQ, and stimulant and cannabis use. Results: Youth with psychotic symptoms displayed an overall trend toward lower cortical folding across all brain regions. After adjusting for multiple comparisons and confounders, regional reductions were localized to the frontal and occipital lobes. Specifically, the medial (B = ­0.42, pFDR = 0.04) and lateral (B = ­0.39, pFDR = 0.04) orbitofrontal cortices as well as the cuneus (B = ­0.47, pFDR = 0.03) and the pericalcarine (B = ­0.45, pFDR = 0.03) and lingual (B = ­0.38, pFDR = 0.04) gyri. Limitations: Inference about developmental trajectories was limited by the cross-sectional data. Conclusion: Psychotic symptoms in youth are associated with cortical folding deficits, even in the absence of psychotic illness. The current study helps clarify the neurodevelopmental basis of psychosis at an early stage, before medication, drug use and other confounds have had a persistent effect on the brain.


Subject(s)
Cerebral Cortex/diagnostic imaging , Psychotic Disorders/diagnostic imaging , Adolescent , Adult , Cerebral Cortex/growth & development , Child , Cross-Sectional Studies , Female , Frontal Lobe/diagnostic imaging , Frontal Lobe/growth & development , Humans , Magnetic Resonance Imaging , Male , Occipital Lobe/diagnostic imaging , Occipital Lobe/growth & development , Psychotic Disorders/epidemiology , Risk Factors , Young Adult
4.
Aust N Z J Psychiatry ; 53(12): 1179-1188, 2019 12.
Article in English | MEDLINE | ID: mdl-31244332

ABSTRACT

OBJECTIVE: Bipolar disorders increase the risk of dementia and show biological and brain alterations, which resemble accelerated aging. Lithium may counter some of these processes and lower the risk of dementia. However, until now no study has specifically investigated the effects of Li on brain age. METHODS: We acquired structural magnetic resonance imaging scans from 84 participants with bipolar disorders (41 with and 43 without Li treatment) and 45 controls. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants, and calculated BrainAGE by subtracting chronological from the estimated brain age. RESULTS: BrainAGE was significantly greater in non-Li relative to Li or control participants, F(2, 125) = 10.22, p < 0.001, with no differences between the Li treated and control groups. The estimated brain age was significantly higher than the chronological age in the non-Li (4.28 ± 6.33 years, matched t(42) = 4.43, p < 0.001), but not the Li-treated group (0.48 ± 7.60 years, not significant). Even Li-treated participants with partial prophylactic treatment response showed lower BrainAGE than the non-Li group, F(1, 64) = 4.80, p = 0.03. CONCLUSIONS: Bipolar disorders were associated with greater, whereas Li treatment with lower discrepancy between brain and chronological age. These findings support the neuroprotective effects of Li, which were sufficiently pronounced to affect a complex, multivariate measure of brain structure. The association between Li treatment and BrainAGE was independent of long-term thymoprophylactic response and thus may generalize beyond bipolar disorders, to neurodegenerative disorders.


Subject(s)
Bipolar Disorder/drug therapy , Brain/pathology , Lithium Compounds/pharmacology , Neuroprotective Agents/pharmacology , Adult , Age Factors , Bipolar Disorder/diagnostic imaging , Brain/diagnostic imaging , Female , Humans , Machine Learning , Magnetic Resonance Imaging , Male , Middle Aged , Multivariate Analysis
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