Weekly Digest

Weekly ToC Digest (week of 2026-05-25)

Prioritizing articles related to brain-age modeling, neuroimaging biomarkers, harmonization, and computational methods. Prioritized brain age prediction and neuroimaging biomarkers. No items relevant to brain-aging, brain-age models, or related computational methods were found in this week’s feed. No papers related to brain age modeling were found in this week’s selection.

Included: 3 (score ≥ 0.35)
Scored: 5 total items


Brain age prediction in generalized anxiety disorder using a convolutional neural network

Trans Psychiatry
Score: 1.00
Published: 2026-05-24T00:00:00+00:00 Tags: brain age, generalization, CNN, neuroimaging

Title focuses directly on brain age prediction using computational models (CNNs) and relates to psychiatric disorders, aligning well with multiple interests: brain age, model application in psychiatry.

RSS summary

Translational Psychiatry, Published online: 24 May 2026; doi:10.1038/s41398-026-04078-3

Brain age prediction in generalized anxiety disorder using a convolutional neural network


Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning

bioRxiv
Score: 0.90
Published: 2026-05-23T00:00:00+00:00 Tags: MRI, machine learning, neuroimaging

Integrates neuroimaging and machine learning to predict neuropathology, relevant for computational modeling in brain aging.

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Accurate in vivo prediction of neuropathology is critical for advancing diagnosis and treatment of Alzheimer’s disease and related dementias (ADRDs). As many individuals with ADRDs have mixed pathologies ({beta}-amyloid, pathologic tau, cerebrovascular disease, vascular brain injury, pathologic TDP-43, hippocampal sclerosis, Lewy bodies), there is interest in determining how accurately we can infer these pathologic changes from clinical data, biofluid assays (e.g., CSF), and neuroimaging. Here w…


The Hidden Landscape of Missed Effects in Human Functional Neuroimaging

bioRxiv
Score: 0.65
Published: 2026-05-24T00:00:00+00:00 Tags: neuroimaging, calibration, bias correction

Focuses on computational improvements in effect size estimation in neuroimaging, relevant for harmonization and bias correction in brain-age models.

RSS summary

Functional neuroimaging aims to uncover brain processes underlying behavior and disease, yet studies are often underpowered to detect these effects. How this literature has shaped our understanding of brain function remains unknown, and little guidance exists for planning better powered studies. An underappreciated barrier is that commonly reported effect sizes across the brain are inflated, biasing study planning. Here, we introduce a correction for this inflation bias and show how more accurat…