Sandhya Ramrakha

The paper introduces DunedinPACNI, a brain MRI-based aging measure that predicts dementia conversion, morbidity, and mortality.

This paper introduces a stacking machine learning approach to improve predictability, test-retest reliability, and generalisability of brain-wide associations for cognitive abilities using multimodal MRI data.

The paper examines how functional topography of neocortical networks in midlife relates to covariation in motor and cognitive abilities, finding that motor ability reflects functional topography differences while hearing does not.

The paper examines the association between retinal neuronal layer thickness and brain structural integrity in midlife, finding thinner retinal layers correlate with reduced brain structural measures.

The paper identifies a common pattern of reduced neocortical thickness across multiple psychiatric disorder categories and the general psychopathology factor.

Task-fMRI regional activation has poor test-retest reliability: meta-analytic mean intraclass correlation .397 across 77 un-thresholded substudies, and .067 to .485 for 11 tasks in HCP and Dunedin retest samples.

This study shows that older brain-age in midlife is linked to accelerated biological aging and cognitive decline, supporting brain-age as a biomarker while highlighting its potential to reflect lifelong CNS variation.

The paper replicates structural brain alterations linked to the p factor in a longitudinal cohort, finding support for cerebello-thalamo-cortical circuit and visual association cortex associations but not cerebellar morphology.

This paper introduces General Functional Connectivity (GFC) to improve reliability and heritability of intrinsic connectivity estimates by combining resting-state and task fMRI data.

This study finds that white matter hyperintensities (WMHs) are common in midlife and are associated with lower IQ and greater cognitive decline from childhood to midlife.