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Background The use of different 3D T1-weighted magnetic resonance (T1 MR) imaging protocols induces image incompatibility across multicenter studies, negating the many advantages of multicenter studies. A few methods have been developed to address this problem, but significant image incompatibility still remains. Thus, we developed a novel and convenient method to improve image compatibility. Methods W-score standardization creates quality reference values by using a healthy group to obtain normalized disease values. We developed a protocol-specific w-score standardization to control the protocol effect, which is applied to each protocol separately. We used three data sets. In dataset 1, brain T1 MR images of normal controls (NC) and patients with Alzheimer’s disease (AD) from two centers, acquired with different T1 MR protocols, were used (Protocol 1 and 2, n = 45/group). In dataset 2, data from six subjects, who underwent MRI with two different protocols (Protocol 1 and 2), were used with different repetition times, echo times, and slice thicknesses. In dataset 3, T1 MR images from a large number of healthy normal controls (Protocol 1: n = 148, Protocol 2: n = 343) were collected for w-score standardization. The protocol effect and disease effect on subjects' cortical thickness were analyzed before and after the application of protocol-specific w-score standardization. Results As expected, different protocols resulted in differing cortical thickness measurements in both NC and AD subjects. Different measurements were obtained for the same subject when imaged with different protocols. Multivariate pattern difference between measurements was observed between the protocols. Classification accuracy between two protocols was nearly 90%. After applying protocol-specific w-score standardization, the differences between the protocols substantially decreased. Most importantly, protocol-specific w-score standardization reduced both univariate and multivariate differences in the images while maintaining the AD disease effect. Compared to conventional regression methods, our method showed the best performance for in terms of controlling the protocol effect while preserving disease information. Conclusions Protocol-specific w-score standardization effectively resolved the concerns of conventional regression methods. It showed the best performance for improving the compatibility of a T1 MR post-processed feature, cortical thickness.

Noninvasive parcellation of the human cerebral cortex is an important goal for understanding and examining brain functions. Recently, the patterns of anatomical connections using diffusion tensor imaging (DTI) have been used to parcellate brain regions. Here, we present a noninvasive parcellation approach that uses “functional fingerprints” obtained by correlation measures on resting state functional magnetic resonance imaging (fMRI) data to parcellate brain regions. In other terms, brain regions are parcellated based on the similarity of their connection – as reflected by correlation during resting state – to the whole brain. The proposed method was used to parcellate the medial frontal cortex (MFC) into supplementary motor areas (SMA) and pre-SMA subregions. In agreement with anatomical landmark-based parcellation, we find that functional fingerprint clustering of the MFC results in anterior and posterior clusters. The probabilistic maps from 12 subjects showed that the anterior cluster is mainly located rostral to the vertical commissure anterior (VCA) line, whereas the posterior cluster is mainly located caudal to VCA line, suggesting the homologues of pre-SMA and SMA. The functional connections from the putative pre-SMA cluster were connected to brain regions which are responsible for complex/cognitive motor control, whereas those from the putative SMA cluster were connected to brain regions which are related to the simple motor control. These findings demonstrate the feasibility of the functional connectivity-based parcellation of the human cerebral cortex using resting state fMRI.

We investigated the changes of sulcal shape (average mean curvature in folded regions and sulcal depth) in mild cognitive impairment (MCI) and Alzheimer's disease (AD) using quantitative surface-based methods in a large sample of magnetic resonance imaging data. Moreover, we observed their relationships with cortical thickness and gyral white matter (WM) volume, while considering age effect. This study involved 85 normal controls (n [men/women]: 36/49, age [mean+/-SD]: 71.1+/-4.9 years), and 100 MCI (44/56, 71.8+/-6.5) and 145 AD subjects (53/92, 72.7+/-7.3). We found significantly lower average mean curvature (greater sulcal widening) and shallower sulcal depth with disease progression from controls to MCI and MCI to AD. The most remarkable change in MCI and AD was sulcal widening observed in the temporal lobe (average mean curvature, control [mean]: 0.564, MCI: 0.534 (5.3% decrease from control), AD: 0.486 (13.8% and 9.0% decrease from control and MCI respectively)). Of the four measurements, the sulcal widening measurement showed the highest sensitivity in revealing group differences between control and MCI, which might be useful for detecting early dementia. Significant reductions in cortical thickness and gyral WM volume also occurred in MCI and AD. Multiple regression analysis demonstrated that a wider and shallower sulcal shape was primarily associated with decreased cortical thickness and gyral WM volume in each group. Age-related trends for the sulcal shape were not strongly found when cortical thickness and gyral WM volume were considered.

Amnestic mild cognitive impairment (aMCI) can be classified into single domain (S-aMCI) and multiple domain (M-aMCI) subtypes. However, there have been no studies that specifically investigate the structural differences that support this classification. In an attempt to compare regional cortical thickness in two subtypes of aMCI, we aimed to map the distribution of cortical thinning using a surface based cortical analysis of magnetic resonance imaging. The cortical thickness across the entire brain was measured in 9 patients with S-aMCI, 22 patients with M-aMCI, and 61 normal healthy subjects. Differences in the patterns of cortical thinning between S-aMCI and M-aMCI were assessed using ANCOVA on a vertex-by-vertex basis, and statistical maps of differences in cortical thickness between the groups were constructed using a surface model. Relative to controls, S-aMCI patients showed cortical thinning in the left medial temporal lobe, and M-aMCI patients showed cortical thinning in the left medial temporal lobe, precuneus, and anterior and inferior basal temporal, insular, and temporal association cortices. When the two MCI groups were directly compared, M-aMCI patients showed cortical thinning in left precuneus. Our studies suggest that M-aMCI is a transitional state between S-aMCI and Alzheimer's disease, and that the cortical thinning is evidence that the precuneus is responsible for the multiple cognitive impairments in M-aMCI.