Publications by authors named "Martin Ystad"

Motivated by the frontal- and white matter (WM) retrogenesis hypotheses and the assumptions that fronto-striatal circuits are especially vulnerable in normal aging, the goal of the present study was to identify fiber bundles connecting subcortical nuclei and frontal areas and obtain site-specific information about age related fractional anisotropy (FA) changes. Multimodal magnetic resonance image acquisitions [3D T1-weighted and diffusion weighted imaging (DWI)] were obtained from healthy older adults (N = 76, range 49-80 years at inclusion) at two time points, 3 years apart. A subset of the participants (N = 24) was included at a third time-point.

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Background: Post-adolescence is known to be a period of general maturation and development in the human brain. In brain imaging, volumetric and morphologic cortical grey-matter changes can easily be assessed, but the analysis of cortical complexity seems to have been broadly neglected for this age interval.

Methods: Magnetic resonance imaging (MRI) was used to acquire structural brain images.

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Objective: To investigate brain-behavior relationships between morphometric brain measures and salient executive function (EF) measures of inhibition and switching.

Method: One hundred participants (49-80 years) performed the Color Word Interference Test from the Delis-Kaplan Executive Function System (D-KEFS). Salient measures of EF components of inhibition and switching, of which the effect of more fundamental skills were regressed out, were analyzed using linear models and a conditional inference trees analysis taking intercorrelations between predictor variables (brain volumes, age, gender, and education) into account.

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In this work we describe an integrated and automated workflow for a comprehensive and robust analysis of multimodal MR images from a cohort of more than hundred subjects. Image examinations are done three years apart and consist of 3D high-resolution anatomical images, low resolution tensor-valued DTI recordings and 4D resting state fMRI time series. The integrated analysis of the data requires robust tools for segmentation, registration and fiber tracking, which we combine in an automated manner.

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Resting state fMRI studies have found that cognitive decline in aging is associated with alterations in functional connectivity of distributed neural systems in the brain. While functional connections have been shown to rely on the underlying structural connectivity, direct structural connections have been studied in only a few distributed cortical systems so far. It is well known that subcortical nuclei have structural connections to the entire cortex.

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Imaging research into age-related changes in episodic memory has mainly focused on changes in cortical areas in the medial temporal lobe and the hippocampus. However, several lines of evidence indicate that subcortical structures such as the basal ganglia and the thalamus are also involved in episodic memory function. Recent studies have revealed age-related changes in functional connectivity between different brain areas, as measured by resting state fMRI.

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Background: The frontal lobe has been associated to a wide range of cognitive control functions and is also vulnerable to degeneration in old age. A recent study by Thomsen and colleagues showed a difference between a young and old sample in grey matter density and activation in the left middle frontal cortex (MFC) and performance on a dichotic listening task. The present study investigated this brain behaviour association within a sample of healthy older individuals, and predicted a positive correlation between performance in a condition requiring executive attention and measures of grey matter structure of the posterior left MFC.

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Background: Normal aging involves a decline in cognitive function that has been shown to correlate with volumetric change in the hippocampus, and with genetic variability in the APOE-gene. In the present study we utilize 3D MR imaging, genetic analysis and assessment of verbal memory function to investigate relationships between these factors in a sample of 170 healthy volunteers (age range 46-77 years).

Methods: Brain morphometric analysis was performed with the automated segmentation work-flow implemented in FreeSurfer.

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