Structural brain complexity and cognitive decline in late life — A longitudinal study in the Aberdeen 1936 Birth Cohort

Abstract

Brain morphology and cognitive ability change with age. Gray and white matter volumes decrease markedly by the 7th decade of life when cognitive decreases first become readily detectable. As a consequence, the shape complexity of the cortical mantle may also change. The purposes of this study are to examine changes over a five year period in brain structural complexity in late life, and to investigate cognitive correlates of any changes. Brain magnetic resonance images at 1.5 Tesla were acquired from the Aberdeen 1936 Birth Cohort at about ages 68 years (243 participants) and 73 years (148 participants returned). Measures of brain complexity were extracted using Fractal Dimension (FD) and calculated using the box-counting method. White matter complexity, brain volumes and cognitive performance were measured at both 68 and 73 years. Childhood ability was measured at age 11 using the Moray House Test. FD and brain volume decrease significantly from age 68 to 73 years. Using a multilevel linear modeling approach, we conclude that individual decreases in late life white matter complexity are not associated with differences in executive function but are linked to information processing speed, auditory-verbal learning, and reasoning in specific models-with adjustment for childhood mental ability. A significant association was found after adjustment for age, brain volume and childhood mental ability. Complexity of white matter is associated with higher fluid cognitive ability and, in a longitudinal study, predicts retention of cognitive ability within late life.

Ambient air pollution and neurotoxicity on brain structure: Evidence from women's health initiative memory study

“…Future studies, especially those planned with integrated analyses of brain imaging and cognitive function data, need to put these heterogeneous results in the context of growing knowledge about brain morphology in relation to cognitive aging across life span. 46 Results of our analyses revealed no statistically significant association between GM volume and PM 2.5 exposure, consistent with the current toxicological literature with only limited evidence for neuronal death in animals with inhaled exposures to concentrated fine particles. 17 However, interpretation of this observation needs to consider the selected study population, exposure characteristics, and analytic approaches.…”

“…Future studies, especially those planned with integrated analyses of brain imaging and cognitive function data, need to put these heterogeneous results in the context of growing knowledge about brain morphology in relation to cognitive aging across life span. 46 …”

Cortical complexity as a measure of age-related brain atrophy

“…In a cohort of over 200 adults aged about 68 years old, Mustafa et al (2012) found that individuals with greater whole-brain white-matter complexity had higher fluid intelligence scores and less evidence of age-related cognitive decline (also see Sandu et al, 2014). King et al (2010) also provide evidence that fractal dimensionality of the cortical ribbon correlated with scores on a cognitive battery, and that this correlation was qualitatively stronger than comparable correlations using cortical thickness and gyrification index.…”

“…Prior studies have demonstrated that cortical complexity is related to cognitive performance (Im et al, 2006; Mustafa et al, 2012; Sandu et al, 2014) and differs between several neurological patient populations relative to healthy controls (e.g., Alzheimer's disease: King et al, 2009, 2010; schizophrenia: Sandu et al, 2008; Nenadic et al, 2014; Yotter et al, 2011; multiple sclerosis: Esteban et al, 2009; frontal lobe epilepsy: Cook et al, 1995; multiple system atrophy: Wu et al, 2010; William's syndrome: Thompson et al, 2005). Here we investigated age-related differences in fractal dimensionality of the cortical ribbon and parcellated regions of cortex in a large sample of adults across the lifespan, using structural images obtained from an open-access dataset.…”

Age-related differences in the structural complexity of subcortical and ventricular structures

“…While the current work applies fractal dimensionality analyses to subcortical structures, others have used fractal dimensionality to characterize the structural complexity of segmented grey or white matter structure (e.g., King et al, 2009; Madan & Kensinger, 2016; Mustafa et al, 2012; Sandu et al, 2008). Using these approaches, fractal dimensionality has been related to inter-individual differences in measures of fluid intelligence (Mustafa et al, 2012; Sandu et al, 2014), IQ (Im et al, 2006), and performance on the cognitive subscale of the Alzheimer’s Disease Assessment Scale (King et al, 2010). Fractal dimensionality has also been shown to differ between healthy adults and a number of patient populations, particularly in Alzheimer’s disease (King et al, 2009, 2010; Thompson et al, 1998) and schizophrenia (Ha et al, 2005; Narr et al, 2004; Nenadic et al, 2014; Sandu et al, 2008; Yotter et al, 2011; Zhao et al, 2016).…”