Accelerated Brain Aging in Patients With Obsessive-Compulsive Disorder

Attention over vulnerable brain regions associating cerebral palsy disorder and biological markers

INTRODUCTION

Cerebral palsy (CP) is a neurological disorder caused by cerebral ischemia and hypoxia during fetal brain development.Early intervention in CP favors medications and therapies; however, monitoring early brain development in children with CP is critical. It is essential to thoroughly examine brain-vulnerable regions associated with biological traits (BTs).Variations in BTs were evident in children with CP; however, it is critical to explore the BTs' impact on the brains of healthy controls (HC) and CP-disordered children.

OBJECTIVE

This study associates BTs with HC and CP children.This study investigates the neurodevelopment of HC and CP underlying BTs. This study establishes a benchmark for the association of BT with HC and CP children.

METHOD

The proposed AWG-Net is composed of customized spatial-channel (CSC) and multi-head self (MHA) attentions, where CSC blocks are incorporated at the first few stages, MHA at later stages, and cumulative-dense structures to propagate susceptible regions to deeper layers. The training samples include T1-w, T2-w, Flair, and Sag, annotated with age, gender, and weight.

RESULTS

The significant results for HC and CP are age (HC: MAE = 1.05, MCS10=85.63, R2=0.844; CP: MAE = 1.16, MCS10=84.79, R2=0.717), gender (HC: Acc = 82.98%, CP: Acc = 82.00%), and weight (HC: MAE = 4.65, MCS10=56.30, R2=0.78; CP: MAE = 2.85, MCS10=70.24, R2=0.82). Vulnerable regions for age are the cerebellar hemisphere, frontal, occipital, and parietal bones in HC and inconsistent in CP. HC and CP commonalities are in the frontal bone and cerebellar hemisphere with HC and discrepant in the occipital and temporal bones for CP. Similarly, gender differences are found for HC and CP.

CONCLUSION

Age and gender are marginally less affected by the brain regions vulnerable to CP than weight estimation. T1-w is appropriate for age, weight, and gender. The learned trends are different for HC and CP in brain development and gender while slightly different in the case of weight.

Bidirectional causal relational between frailty and mental illness: a two-sample Mendelian randomization study

Background

Frailty has been associated with mental illness (MI) observational studies, but the causal relationship between these factors remains uncertain. We aimed to assess the bidirectional causality between frailty and MI by two-sample Mendelian randomization (MR) analyses.

Methods

To investigate the causal relationship among them, summary statistics of frailty index (FI) and six types of MI: anxiety, depression, affective disorder, mania, schizophrenia, and obsessive-compulsive disorder (OCD) were included in this MR study. This MR analysis was performed using inverse variance weighting (IVW), MR-Egger regression, and weighted median. The stability of the results was evaluated using Cochran's Q test, MR-Egger intercept test, Funnel Plots, and leave-one-out analysis.

Results

Genetic predisposition to FI was significantly associated with increased anxiety (odds ratio [OR] = 1.62, 95% confidence interval [CI] 1.13-2.33, P = 8.18E-03), depression (OR = 1.88, 95% CI 1.30-2.71, P = 8.21E-04), affective disorder (OR = 1.70, 95% CI 1.28-2.27, P = 2.57E-04). However, our study findings do not demonstrate a causal relationship between FI and mania (OR = 1.02, 95% CI 0.99-1.06, P = 2.20E-01), schizophrenia (OR = 1.02, 95% CI 0.07-0.86, P = 9.28E-01). In particular, although the IVW results suggest a potential causal relationship between FI and OCD (OR = 0.64, 95% CI 0.07-0.86, P = 2.85E-02), the directions obtained from the three methods we employed ultimately show inconsistency. Therefore, the result must be interpreted with caution. The results of the reverse MR analysis indicated a statistically significant and causal relationship between anxiety (OR = 1.06, 95% CI 1.01-1.11, P = 2.00E-02), depression (OR = 1.14, 95% CI 1.04-1.26, P = 7.99E-03), affective disorder (OR = 1.15, 95% CI 1.09-1.21, P = 3.39E-07), and schizophrenia (OR = 1.02, 95% CI 1.01-1.04, P = 1.70E-03) with FI. However, our findings do not provide support for a link between mania (OR = 1.46, 95% CI 0.79-2.72, P = 2.27E-01), OCD (OR = 1.01, 95% CI 1.00-1.02, P = 2.11E-01) and an increased risk of FI.

Conclusion

The MR results suggest a potential bidirectional causal relationship between FI and anxiety, depression, and affective disorder. Schizophrenia was found to be associated with a higher risk of FI. The evidence was insufficient to support a causal relationship between Fl and other Ml. These findings offer new insights into the development of effective management strategies for frailty and MI.

Sex-difference in the association between social drinking, structural brain aging and cognitive function in older individuals free of cognitive impairment

Background

We investigated a potential sex difference in the relationship between alcohol consumption, brain age gap and cognitive function in older adults without cognitive impairment from the population-based Mayo Clinic Study of Aging.

Methods

Self-reported alcohol consumption was collected using the food-frequency questionnaire. A battery of cognitive testing assessed performance in four different domains: attention, memory, language, and visuospatial. Brain magnetic resonance imaging (MRI) was conducted using 3-T scanners (Signa; GE Healthcare). Brain age was estimated using the Brain-Age Regression Analysis and Computational Utility Software (BARACUS). We calculated the brain age gap as the difference between predicted brain age and chronological age.

Results

The sample consisted of 269 participants [55% men (n=148) and 45% women (n=121) with a mean age of 79.2 ± 4.6 and 79.5 ± 4.7 years respectively]. Women had significantly better performance compared to men in memory, (1.12 ± 0.87 vs 0.57 ± 0.89, P<0.0001) language (0.66 ± 0.8 vs 0.33 ± 0.72, P=0.0006) and attention (0.79 ± 0.87 vs 0.39 ± 0.83, P=0.0002) z-scores. Men scored higher in visuospatial skills (0.71 ± 0.91 vs 0.44 ± 0.90, P=0.016). Compared to participants who reported zero alcohol drinking (n=121), those who reported alcohol consumption over the year prior to study enrollment (n=148) scored significantly higher in all four cognitive domains [memory: F3,268 = 5.257, P=0.002, Language: F3,258 = 12.047, P<0.001, Attention: F3,260 = 22.036, P<0.001, and Visuospatial: F3,261 = 9.326, P<0.001] after correcting for age and years of education. In addition, we found a significant positive correlation between alcohol consumption and the brain age gap (P=0.03). Post hoc regression analysis for each sex with language z-score revealed a significant negative correlation between brain age gap and language z-scores in women only (P=0.008).

Conclusion

Among older adults who report alcohol drinking, there is a positive association between higher average daily alcohol consumption and accelerated brain aging despite the fact that drinkers had better cognitive performance compared to zero drinkers. In women only, accelerated brain aging is associated with worse performance in language cognitive domain. Older adult women seem to be vulnerable to the negative effects of alcohol on brain structure and on certain cognitive functions.

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

Predicting the brain age of children with cerebral palsy using a two-dimensional convolutional neural networks prediction model without gray and white matter segmentation

Background

Abnormal brain development is common in children with cerebral palsy (CP), but there are no recent reports on the actual brain age of children with CP.

Objective

Our objective is to use the brain age prediction model to explore the law of brain development in children with CP.

Methods

A two-dimensional convolutional neural networks brain age prediction model was designed without segmenting the white and gray matter. Training and testing brain age prediction model using magnetic resonance images of healthy people in a public database. The brain age of children with CP aged 5-27 years old was predicted.

Results

The training dataset mean absolute error (MAE) = 1.85, r = 0.99; test dataset MAE = 3.98, r = 0.95. The brain age gap estimation (BrainAGE) of the 5- to 27-year-old patients with CP was generally higher than that of healthy peers (p < 0.0001). The BrainAGE of male patients with CP was higher than that of female patients (p < 0.05). The BrainAGE of patients with bilateral spastic CP was higher than those with unilateral spastic CP (p < 0.05).

Conclusion

A two-dimensional convolutional neural networks brain age prediction model allows for brain age prediction using routine hospital T1-weighted head MRI without segmenting the white and gray matter of the brain. At the same time, these findings suggest that brain aging occurs in patients with CP after brain damage. Female patients with CP are more likely to return to their original brain development trajectory than male patients after brain injury. In patients with spastic CP, brain aging is more serious in those with bilateral cerebral hemisphere injury than in those with unilateral cerebral hemisphere injury.

Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry

It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.