Public Health.

Background: Traumatic brain injury (TBI) is a risk factor for dementia and is linked to earlier age of onset. This study investigated whether later-life changes in everyday cognition and behavior - risk markers of AD - could be observed in cognitively unimpaired older persons who sustained suspected mild TBI (smTBI) earlier in life and whether these cognitive and behavioral changes behavior mediated the link between smTBI and daily function.

Method: Data for 1274 participants from the Canadian Platform for Research Online to Investigate Health, Quality of Life, Cognition, Behaviour, Function, and Caregiving in Aging (CAN-PROTECT) study were analyzed.

Cognitive, behavioral, and functional outcomes of suspected mild traumatic brain injury in community-dwelling older persons without mild cognitive impairment or dementia.

Introduction: Traumatic brain injury (TBI) is associated with greater risk and earlier onset of dementia. This study investigated whether later-life changes in subjective cognition and behavior - potential markers of AD - could be observed in cognitively unimpaired older persons with a history of suspected mild TBI (smTBI) earlier in life and whether changes in cognition and behavior mediated the link between smTBI and daily function.

Methods: Data for 1392 participants from the Canadian Platform for Research Online to Investigate Health, Quality of Life, Cognition, Behaviour, Function, and Caregiving in Aging (CAN-PROTECT) were analyzed.

Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

Purpose: External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always possible, so recent research has proposed using machine learning to directly estimate respiratory variation (RV), potentially obviating the need for external monitoring. In this study, we propose an extended method for reconstructing RV waveforms directly from resting state BOLD-fMRI data in healthy adult participants with the inclusion of both BOLD signals and derived head motion parameters.

Analysis and visualization of the effect of multiple sclerosis on biological brain age.

Introduction: The rate of neurodegeneration in multiple sclerosis (MS) is an important biomarker for disease progression but can be challenging to quantify. The brain age gap, which quantifies the difference between a patient's chronological and their estimated biological brain age, might be a valuable biomarker of neurodegeneration in patients with MS. Thus, the aim of this study was to investigate the value of an image-based prediction of the brain age gap using a deep learning model and compare brain age gap values between healthy individuals and patients with MS.