Structural brain complexity and cognitive decline in late life--a longitudinal study in the Aberdeen 1936 Birth Cohort.

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.

Brain structural complexity and life course cognitive change.

Fractal measures such as fractal dimension (FD) can quantify the structural complexity of the brain. These have been used in clinical neuroscience to investigate brain development, ageing and in studies of psychiatric and neurological disorders. Here, we examined associations between the FD of white matter and cognitive changes across the life course in the absence of detectable brain disease. The FD was calculated from segmented cerebral white matter MR images in 217 subjects aged about 68years, in whom archived intelligence scores from age 11years were available. Cognitive test scores of fluid and crystallised intelligence were obtained at the time of MR imaging. Significant differences were found (intracranial volume, brain volume, white matter volume and Raven's Progressive Matrices score) between men and women at age 68years and novel associations were found between FD and measures of cognitive change over the life course from age 11 to 68years. Those with greater FD were found to have greater than expected fluid abilities at age 68years than predicted by their childhood intelligence and less cognitive decline from age 11 to 68years. These results are consistent with other reports that FD measures of cortical structural complexity increase across the early life course during maturation of the cerebral cortex and add new data to support an association between FD and cognitive ageing.

The balance between cognitive reserve and brain imaging biomarkers of cerebrovascular and Alzheimer's diseases.

The cognitive reserve hypothesis explains the disparity between clinical and pathological phenotypes and why, in two individuals with the same extent of neuropathology, one may be demented while the other remains cognitively intact. We examined the balance between brain magnetic resonance imaging measures of the two most common pathologies associated with brain ageing, cerebrovascular disease and Alzheimer's disease, and parameters of cerebral reserve in well-characterized participants born in 1936, for whom childhood intelligence is known. Brain magnetic resonance imaging was carried out at 1.5T using fluid attenuation inversion recovery and T(1)-weighted volumetric sequences in 249 participants. Cerebrovascular disease was quantified by measuring brain white matter hyperintensities on fluid attenuation inversion recovery images using Scheltens' scale and Alzheimer's disease was measured from volumetric data using FreeSurfer to extract whole brain volume and hippocampal volumes in turn. The effect of these measures of brain burden on life-long cognitive ageing from the age of 11 to 68 years was compared with the effect of educational attainment and occupational grade using structural equation modelling. Complete brain burden and reserve data were available in 224 participants. We found that educational attainment, but not occupation, has a measurable and positive effect, with a standardized regression weight of +0.23, on late life cognitive ability in people without cognitive impairment aged 68 years, allowing for the influence of childhood intelligence and the two most common subclinical brain pathological burdens in the ageing brain. In addition, we demonstrate that the magnitude of the contribution of education is greater than the negative impact of either neuropathological burden alone, with standardized regression weights of -0.14 for white matter hyperintensities and -0.20 for hippocampal atrophy. This study illustrates how education counteracts the deleterious effects of cerebrovascular disease and Alzheimer's disease and highlights the importance of quantifying cognitive reserve in dementia research.

Automated knowledge-assisted mitosis cells detection framework in breast histopathology images.

Based on the Nottingham Histopathology Grading (NHG) system, mitosis cells detection is one of the important criteria to determine the grade of breast carcinoma. Mitosis cells detection is a challenging task due to the heterogeneous microenvironment of breast histopathology images. Recognition of complex and inconsistent objects in the medical images could be achieved by incorporating domain knowledge in the field of interest. In this study, the strategies of the histopathologist and domain knowledge approach were used to guide the development of the image processing framework for automated mitosis cells detection in breast histopathology images. The detection framework starts with color normalization and hyperchromatic nucleus segmentation. Then, a knowledge-assisted false positive reduction method is proposed to eliminate the false positive (i.e., non-mitosis cells). This stage aims to minimize the percentage of false positive and thus increase the F1-score. Next, features extraction was performed. The mitosis candidates were classified using a Support Vector Machine (SVM) classifier. For evaluation purposes, the knowledge-assisted detection framework was tested using two datasets: a custom dataset and a publicly available dataset (i.e., MITOS dataset). The proposed knowledge-assisted false positive reduction method was found promising by eliminating at least 87.1% of false positive in both the dataset producing promising results in the F1-score. Experimental results demonstrate that the knowledge-assisted detection framework can achieve promising results in F1-score (custom dataset: 89.1%; MITOS dataset: 88.9%) and outperforms the recent works.