Atypical Brain Aging and Its Association With Working Memory Performance in Major Depressive Disorder.

BACKGROUND: Patients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to those seen in aging. However, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool that quantifies normative neurodevelopmental trajectories. METHODS: A total of 304 participants with MDD and 236 control participants without depression were recruited and scanned from 3 studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for 1) differences between participants with MDD and control participants; 2) differences between individuals with versus without severe childhood maltreatment; and 3) correlations with depressive symptom severity, neurocognitive assessment domains, and escitalopram treatment response. RESULTS: Brain centiles were significantly lower in the MDD group than in the control group. Brain centile was also significantly correlated with working memory in the control group but not the MDD group. No significant associations were observed between depression severity or antidepressant treatment response and brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles. CONCLUSIONS: Consistent with previous work on machine learning models that predict brain age, brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications for neurocognitive deficits associated with aging-related cognitive function.

Considerations when asking about "disability" in disability inclusive research.

PURPOSE: There are several ways to include "disability" in research studies, which can be confusing or overwhelming for researchers, community members, and students. The aim of this paper is to share conceptualizations of disability and how to ask about "disability" in research studies. The paper provides a general introduction and brief analysis of the methodological approaches which can be used. METHODS: We used reviews of the literature and extensive discussions to identify key articles, books, websites, and reports that provide guidance and examples of asking about disability in research. RESULTS: Four primary approaches to asking study participants about disability were identified. For each of these, we provide background information, key points about the ways to use the approach including tools that have been developed, and example studies. A comparison table provides a high-level overview of similarities and differences in approaches. Other approaches and tools were also identified and are briefly described. CONCLUSION: Researchers involved in disability and rehabilitation research should be aware that there is not one best or singular way to ask about disability when conducting research. The approach or approaches chosen for a particular study need to match the purpose of the study. It is important that researchers take time to carefully consider their options and choose the best fit for their study.

There are several different ways to ask about disability and functioning when conducting research that aims to include a disability component or focus.Researchers need to carefully select the best option(s) for their study.Whenever possible, researchers should use more than one approach and should allow for more than one type of disability or impairment to be selected.Researchers often require training to understand how to include disability in research.Allow adequate time and resources for training research team members so that the tools are implemented correctly.

Gray matter volume drives the brain age gap in schizophrenia: a SHAP study.

Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term ß = 1.71 [0.53; 3.23]; pcorr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.