Complexity analysis of cortical surface detects changes in future Alzheimer's disease converters

Abstract

Alzheimer's disease (AD) is a neurological disorder that creates neurodegenerative changes at several structural and functional levels in human brain tissue. The fractal dimension (FD) is a quantitative parameter that characterizes the morphometric variability of the human brain. In this study we investigate spherical harmonic-based FD (SHFD), thickness and local gyrification index (LGI) to assess whether they identify cortical surface abnormalities toward the conversion to AD. We study 33 AD patients, 122 mild cognitive impairment (MCI) patients (50 MCI-converters and 29 MCI-non converters) and 32 healthy controls (HC). SHFD, thickness and LGI methodology allowed us to perform not only global but also local level assessments in each cortical surface vertex. First, we found that global SHFD decreased in AD and future MCI-converters compared to HC, and in MCI-converters compared to MCI-non-converters. Second, we found that local white matter SHFD was reduced in AD compared to HC and MCI mainly in medial temporal lobe. Third, local white matter SHFD was significantly reduced in MCI-converters compared to MCI-non-converters in distributed areas, including the medial frontal lobe. Thickness and LGI metrics presented a reduction in AD compared to HC. Thickness was significantly reduced in MCI-converters compared to healthy controls in entorhinal cortex and lateral temporal. In summary, SHFD was the only surface measure showing differences between MCI individuals that will convert or remain stable in the next four years. We suggest that SHFD may be an optimal complement to thickness loss analysis in monitoring longitudinal changes in preclinical and clinical stages of AD.

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple‐acquisition MRI

“…Moreover, we here compute FD estimates on global tissue segmentations. Given the increasingly sophisticated brain parcellation methods, however, region‐ and substructure‐specific fractal analysis is also being developed and is likely to yield interesting additional information, especially in the clinical context (see Eickhoff, Yeo, & Genon, ; Glasser et al, ; Goñi et al, ; Madan, ; Madan & Kensinger, ; Ruiz de Miras et al, ). As such, future work is warranted to expand upon the utility of fractal analysis for empirical neuroimaging, specifically with respect to clinical applications.…”

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI

“…Given the increasingly sophisticated brain parcellation methods, however, region-and substructure-specic fractal analysis is also being developed and is likely to yield interesting additional information, especially in the clinical context (see e.g. Goñi et al 2013;Glasser et al 2016;Madan and Kensinger 2017;Ruiz de Miras et al 2017;Madan 2018).…”

“…human bronchial and vascular ramications) (Di Ieva et al, 2014a, 2015, 2016Mandelbrot, 1983Mandelbrot, , 1967. In biomedical neuroimaging, fractal analysis has been applied in the anatomical description of cortical geometry (Kiselev et al, 2003;Im et al, 2006), and the fractal dimension has shown promise as a biomarker in the detection of early tissue alterations in multiple sclerosis (Esteban et al, 2007(Esteban et al, , 2009), brain abnormalities in infants with intrauterine growth restriction (Esteban et al, 2010), atherosclerotic white matter lesions (Takahashi et al, 2006), morphological changes in multiple system atrophy of the cerebellar type (Wu et al, 2010), angioarchitecture of cerebral arteriovenous malformations (Di Ieva et al, 2014b), the cortical features in Alzheimer's disease (King et al, 2009(King et al, , 2010Ruiz de Miras et al, 2017), cerebral tumors (Iftekharuddin et al, 2009) as well as age-related brain atrophy (Madan and Kensinger, 2016) and age-induced structural changes in white matter tissue (Reishofer et al, 2018).…”

Age‐related changes in cortical and subcortical structures of healthy adult brains: A surface‐based morphometry study

“…Cortical thickness changes during normal development and aging as well as with neuropsychiatric and other disorders . Additionally, some references reported using the local gyrification index (LGI) to identify the abnormal cortical complexity of psychosis and neuropathy . For example, Nanda et al reported significant hypogyria in the left rostral anterior cingulate in schizophrenia and in the right superior temporal and inferior parental regions in schizoaffective disorder.…”