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Published on: January 2, 2012
Exploring cortical morphology biomarkers of amnesic mild cognitive impairment using novel fractal dimension-based
Chi-Wen Jao1,2, Yu-Te Wu1,3, Jiann-Horng Yeh4,5
1Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Abstract:
Amnestic mild cognitive impairment (aMCI) is considered as an intermediate stage of Alzheimer's disease, but no MRI biomarkers currently distinguish aMCI from healthy individuals effectively. Fractal dimension, a quantitative parameter, provides superior morphological information compared to conventional cortical thickness methods. Few studies have used cortical fractal dimension values to differentiate aMCI from healthy controls. In this study, we aim to build an automated discriminator for accurately distinguishing aMCI using fractal dimension measures of the cerebral cortex. Thirty aMCI patients and 30 health controls underwent structural MRI of the brain. First, the atrophy of participants' cortical sub-regions of Desikan-Killiany cortical atlas was assessed using fractal dimension and cortical thickness. The fractal dimension is more sensitive than cortical thickness in reducing dimensional effects and may accurately reflect morphological changes of the cortex in aMCI. The aMCI group had significantly lower fractal dimension values in the bilateral temporal lobes, right limbic lobe and right parietal lobe, whereas they showed significantly lower cortical thickness values only in the bilateral temporal lobes. Fractal dimension analysis was able to depict most of the significantly different focal regions detected by cortical thickness, but additionally with more regions. Second, applying the measured fractal dimensions (and cortical thickness) of both cerebral hemispheres, an unsupervised discriminator was built for the aMCI and healthy controls. The proposed fractal dimension-based method achieves 80.54% accuracy in discriminating aMCI from healthy controls. The fractal dimension appears to be a promising biomarker for cortical morphology changes that can discriminate patients with aMCI from healthy controls.
Insights
Fractal dimension analysis of brain MRIs effectively distinguishes amnestic mild cognitive impairment (aMCI) from healthy individuals. This novel biomarker shows higher accuracy than traditional cortical thickness measurements for detecting early Alzheimer's disease changes.
Area of Science:
- Neuroimaging
- Biomarker Discovery
- Alzheimer's Disease Research
Background:
- Amnestic mild cognitive impairment (aMCI) is a precursor to Alzheimer's disease, lacking effective MRI biomarkers for early detection.
- Current methods like cortical thickness have limitations in capturing subtle morphological changes.
- Fractal dimension offers a more sensitive quantitative measure of brain structure.
Purpose of the Study:
- To develop an automated method for distinguishing aMCI from healthy controls using cerebral cortex fractal dimension.
- To compare the efficacy of fractal dimension versus cortical thickness in identifying aMCI-related brain changes.
Main Methods:
- Structural MRI scans were acquired from 30 aMCI patients and 30 healthy controls.
- Cortical atrophy was assessed using fractal dimension and cortical thickness across Desikan-Killiany atlas regions.
- An unsupervised discriminator was trained using fractal dimension and cortical thickness data.
Main Results:
- Fractal dimension was more sensitive than cortical thickness, detecting changes in more brain regions.
- Significantly lower fractal dimension values were observed in the temporal, limbic, and parietal lobes of aMCI patients.
- The fractal dimension-based discriminator achieved 80.54% accuracy in differentiating aMCI from healthy controls.
Conclusions:
- Fractal dimension is a promising MRI biomarker for detecting cortical morphology alterations in aMCI.
- This quantitative measure can effectively discriminate individuals with aMCI from healthy controls.
- Further research can leverage fractal dimension for earlier and more accurate Alzheimer's disease diagnosis.

