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Abstract: 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. [Copyright &y& Elsevier]

Abstract The concept of cognitive reserve (CR) originated from discrepancies between the degree of brain pathology and the severity of clinical manifestations. CR has been characterized through CR proxies, such as education and occupation complexity; however, such approaches have inherent limitations. Although several methods have been developed to overcome these limitations, they fail to reflect the entire Alzheimer's disease (AD) pathology. Meanwhile, graph theory analysis, one of most powerful and flexible approaches, have established remarkable network properties of the brain. The functional and structural brain networks are damaged in neurodegenerative diseases. Therefore, network analysis has been applied to clarify the characteristics of the disease or give insight. Here, using multimodal neuroimaging, we propose an intuitive model to estimate CR based on its original definition, and explore the neural substrates of CR from the perspective of networks and functional connectivity. A total of 87 subjects (21 AD, 32 mild cognitive impairment, and 34 normal aging) underwent tau and amyloid PET, 3D T1-weighted MR, and resting-state fMRI. We hypothesized CR as a residual of actual cognitive performance and expected performance to be related to quantitative factors, such as AD pathology, demographics, and a genetic factor. Then, we correlated this marker using education and occupation complexity as conventional CR proxies. We validated this marker by testing whether it would modulate the effect of brain pathology on memory function. To examine the neural substrates associated with CR, we performed graph analysis to investigate the association between the CR marker and network measures at different granularities in total subjects, AD spectrum and normal aging, respectively. The CR marker from our model was well associated with education and occupation complexity. More directly, the CR marker was revealed to modify the relationship between brain pathology and memory function among AD spectrum. The CR marker was correlated with the global efficiency of the entire network, nodal clustering coefficient, and local efficiency of the right middle-temporal pole. In connectivity analysis, one cluster of edges centered on right middle-temporal pole was significantly correlated with the CR marker. In subgroup analysis, the network measures of right middle-temporal pole still correlated with the CR marker among AD spectrum. However, right precentral gyrus was revealed to be associated with the CR marker in normal aging. This study demonstrates that our intuitive model using multimodal neuroimaging and network perspective adequately and comprehensively captures CR. From a network perspective, CR is associated with the capacity to process information efficiently in the brain. The right middle-temporal pole was revealed to be a pivotal neural substrate of CR in AD spectrum. These findings foster understanding of AD and will be useful to help identify individuals with vulnerability or resistance to AD pathology, and characterize patients for intervention or drug trials. Highlights • We generated an equation to quantify cognitive reserve (CR) reflecting overall AD neuropathology using multimodal neuroimaging techniques. • CR was proved to modify the relationship between brain pathology and memory function in AD spectrum. • CR was associated with global network efficiency and nodal clustering coefficient and local efficiency of the right middle-temporal pole. • Graph parameters of right middle-temporal pole was revealed to modulate the effect of brain atrophy on cognition in AD spectrum. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Abstract: Recent quantitative analyses of the corpus callosum (CC) have tried to assess the interhemispheric connectivity. Based on histological results showing an expansion of callosal extent at the midsagittal plane, without fiber density alterations, callosal extent was interpreted as an index of interhemispheric connectivity. The microstructural properties of the CC have also been investigated extensively using diffusion tensor imaging, to assess interhemispheric connectivity. The relationships between axonal density and callosal extent need to be investigated to understand how these parameters reflect interhemispheric connectivity. We used a semi-automated CC segmentation scheme in T1-weighted magnetic resonance image and fractional anisotropy (FA) image, respectively. The parameterization method of the segmented CC was applied to 47 right-handed healthy adult subjects. The callosal extent and microstructural properties were measured using the callosal thickness and diffusion indices (FA, mean diffusivity, and axial and radial diffusivity), respectively. Our results revealed a correlation between callosal thickness and FA on the posterior body and isthmus of the CC, which suggests that these regions are more sensitive to fiber alterations than other regions. Based on this result, we suggest that both the extent of the CC and its microstructural properties should be considered together in the estimation of interhemispheric connectivity in healthy adult populations. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: Analyzing cortical sulci is important for studying cortical morphology and brain functions. Although sulcal lines on cortical surfaces can be defined in various ways, it is critical in a neuroimaging study to define a sulcal line along the valley of a cortical surface with a high curvature within a sulcus. To extract the sulcal lines automatically, we present a new geometric algorithm based on the computation of anisotropic skeletons of sulcal regions. Because anisotropic skeletons are highly adaptive to the anisotropic nature of the surface shape, the resulting sulcal lines lie accurately on the valleys of the sulcal areas. Our sulcal lines remain unchanged under local shape variabilities in different human brains. Through experiments, we show that the errors of the sulcal lines for both synthetic data and real cortical surfaces were nearly as constant as the function of random noise. By measuring the changes in sulcal shape in Alzheimer''s disease (AD) patients, we further investigated the effectiveness of the accuracy of our sulcal lines using a large sample of MRI data. This study involved 70 normal controls (n [men/women]: 29/41, age [mean±SD]: 71.7±4.9 years), and 100 AD subjects (37/63, 72.3±5.5). We observe significantly lower absolute average mean curvature and shallower sulcal depth in AD subjects, where the group difference becomes more significant if we measure the quantities along the sulcal lines rather than over the entire sulcal area. The most remarkable difference in the AD patients was the average sulcal depth (control: 11.70 and AD: 11.34). [Copyright &y& Elsevier]