Sulcal pit analysis has been providing novel insights into brain function and development. The purpose of this study was to evaluate the reliability of sulcal pit extraction with respect to the effects of scan session, scanner, and surface extraction tool. Five subjects were scanned 4 times at 3 MRI centers and other 5 subjects were scanned 3 times at 2 MRI centers, including 1 test-retest session. Sulcal pits were extracted on the white matter surfaces reconstructed with both Montreal Neurological Institute and Freesurfer pipelines. We estimated similarity of the presence of sulcal pits having a maximum value of 1 and their spatial difference within the same subject. The tests showed high similarity of the sulcal pit presence and low spatial difference. The similarity was more than 0.90 and the spatial difference was less than 1.7 mm in most cases according to different scan sessions or scanners, and more than 0.85 and about 2.0 mm across surface extraction tools. The reliability of sulcal pit extraction was more affected by the image processing-related factors than the scan session or scanner factors. Moreover, the similarity of sulcal pit distribution appeared to be largely influenced by the presence or absence of the sulcal pits on the shallow and small folds. We suggest that our sulcal pit extraction from MRI is highly reliable and could be useful for clinical applications as an imaging biomarker.

There are some studies identifying the association between kidney dysfunction and cognitive impairment through various mechanisms including small vessel disease. However, results concerning the relationship between kidney dysfunction and cortical atrophy have been inconsistent. Thus, we aimed to evaluate the relationship among kidney dysfunction, small vessel disease, and cortical thinning in probable Alzheimer's disease (AD) dementia patients. Patients consisted of 162 subjects with probable AD dementia who underwent high-resolution T1-weighted volumetric magnetic resonance imaging (MRI) scans using the same scanner. The estimated glomerular filtration rate (GFR) was calculated and divided into the quartiles of patients for comparison. Volume of white matter hyperintensities (WMH) was automatically measured. Two neurologists counted the number of lacunes. Cortical thickness was measured using a surface-based method. GFR was not associated with WMH and the number of lacunes. However, the lowest quartile group of GFR (GFR 1) had cortical thinning in each lobe, compared to the highest quartile group of GFR (GFR 4). The topography of cortical thinning in the GFR 1 group was distributed predominantly in temporoparietal regions, compared to GFR 4. After further adjustment of small vessel disease MRI markers, the association between GFR and the cortical thinning remained. Our findings suggested that kidney dysfunction, represented by GFR, was related to temporoparietal thinning independent of small vessel disease in probable AD dementia patients.

Sulcal depth that is one of the quantitative measures of cerebral cortex has been widely used as an important marker for brain morphological studies. Several studies have employed Euclidean (EUD) or geodesic (GED) algorithms to measure sulcal depth, which have limitations that ignore sulcal geometry in highly convoluted regions and result in under or overestimated depth. In this study, we proposed an automated measurement for sulcal depth on cortical surface reflecting geometrical properties of sulci, which named the adaptive distance transform (ADT). We first defined the volume region of cerebrospinal fluid between the 3D convex hull and the cortical surface, and constructed local coordinates for that restricted region. Dijkstra's algorithm was then used to compute the shortest paths from the convex hull to the vertices of the cortical surface based on the local coordinates, which may be the most proper approach for defining sulcal depth. We applied our algorithm to both a clinical dataset including patients with mild Alzheimer's disease (AD) and 25 normal controls and a simulated dataset whose shape was similar to a single sulcus. The mean sulcal depth in the mild AD group was significantly lower than controls (p = 0.007, normal [mean±SD]: 7.29±0.23 mm, AD: 7.11±0.29) and the area under the receiver operating characteristic curve was relatively high, showing the value of 0.818. Results from clinical dataset that were consistent with former studies using EUD or GED demonstrated that ADT was sensitive to cortical atrophy. The robustness against inter-individual variability of ADT was highlighted through simulation dataset. ADT showed a low and constant normalized difference between the depth of the simulated data and the calculated depth, whereas EUD and GED had high and variable differences. We suggest that ADT is more robust than EUD or GED and might be a useful alternative algorithm for measuring sulcal depth.

Prior studies have shown that patients with AD have decreased functional or structural connectivity between the hippocampus and other brain areas. To the best of our knowledge, however, there have been no studies investigating the topography of cortical thinning areas and correlations with HA using surface based morphometry of three-dimensional (3D) T1-weighted magnetic resonance (MR) images. Cortical thickness was measured using SBM, and hippocampal volume was measured using an automated method, in 219 patients with AD and 54 subjects with no cognitive impairment (NCI). A partial correlation model was used in analysis of cortical thinning related to HA. Cortical thinning areas related to HA were found mostly within areas associated with polysynaptic or direct pathways of the hippocampus, a finding consistent with the disconnection hypothesis. Therefore, the cortical atrophy related to HA in patients with AD may represent disrupted cortical brain networks in connection with HA. However, since the topography of HA-related cortical thinning in groups with Clinical Dementia Ratings (CDR) of 0.5 and 1 corresponded to the stages I-II and III-IV of Braak and Braak staging, respectively, we could not exclude the possibility of the "concomitant hypothesis," i.e. that these areas are affected concomitantly with the hippocampus.

Cardiovascular risk factors are associated with cognitive impairments. However, the effects of cardiovascular risk factors on the topography of cortical thinning have not yet been studied in patients with mild cognitive impairment (MCI) or dementia. Thus, we aimed to evaluate the topography of cortical thinning related to cardiovascular risk factors and the relationships among cardiovascular risk factors, white matter hyperintensities (WMH), and cortical atrophy. Participants included 226 patients with Alzheimer disease or subcortical vascular dementia and 135 patients with amnestic MCI or subcortical vascular MCI. We automatically measured the volume of WMH and cortical thickness. Hypertension was associated with cortical thinning in the frontal and perisylvian regions, and cortical thinning related to diabetes mellitus (DM) occurred in the frontal region. In path analyses, hypertension accounted for 0.04 of the frontal thinning with the mediation of WMH and 0.16 without the mediation of WMH. In case of DM, it accounted for 0.02 of the frontal thinning with the mediation of WMH and 0.13 without the mediation of WMH. Hypertension and DM predominantly affected frontal thinning both with and without the mediation of WMH, where the effects without the mediation of WMH were greater than those with the mediation of WMH.

Previous studies showed that white matter hyperintensities (WMH) are related to cognitive decline in patients with mild cognitive impairment (MCI) or dementia. Moreover, periventricular WMH (periventricular white matter hyperintensities (PWMH)) and deep WMH (deep white matter hyperintensities (DWMH)) may have different effects on cognition. The purpose of this study is to investigate the contributions of PWMH and DWMH to the topography of cortical thinning and to investigate the relationship among WMH, cortical thinning, and cognitive impairments. Participants included 226 patients with Alzheimer's disease or subcortical vascular dementia, and 135 patients with amnestic MCI or subcortical vascular MCI. Cortical thickness was measured using the surface based method. The topography of cortical thinning related to WMH was distributed in the frontal and perisylvian regions, which was similar to that of PWMH. In contrast, there were only small areas of cortical thinning inversely associated with DWMH, which were distributed in medial frontal and lingual gyrus. PWMH, but not DWMH, were associated with the frontal thinning and executive dysfunction; where both PWMH and frontal thinning were independently associated with executive dysfunction. Our results suggest that PWMH are associated with frontal thinning, which is further associated with frontal executive dysfunction.

Patients with probable Alzheimer's disease (AD) and the amnesic form of mild cognitive impairment (aMCI) often demonstrate several types of neuropsychological deficits. These deficits are often related to cortical atrophy, induced by neuronal degradation. The purpose of this study is to investigate whether different anatomic patterns of cortical atrophy are associated with specific neuropsychological deficits. The participants were 170 patients with AD and 99 patients with aMCI. All participants underwent the Seoul Neuropsychological Screening Battery (SNSB), which includes tests that assess attention, language, visuospatial functions, verbal and visual memory, and frontal/executive functions. Cortical atrophy (thinning) was quantified by measuring the thickness of the cortical mantle across the entire brain using automated, three-dimensional magnetic resonance imaging. The relationship between cortical thickness and neuropsychological performance was analysed using stepwise multiple linear regression analyses. These analyses (corrected P.001) showed that several specific brain regions with cortical thinning were associated with cognitive dysfunction including: digit span backward, verbal and picture recall, naming and fluency, drawing-copying, response inhibition and selective attention. Some of the other functions, however, were not associated with specific foci of cortical atrophy (digit span forward, the word reading portion of the Stroop test, word and picture recognition). Our study, involving a large sample of participants with aMCI and AD, provides support for the postulate that cortical thinning-atrophy in specific anatomic loci are pathological markers for specific forms of cognitive dysfunction.

The amnestic form of mild cognitive impairment (aMCI) is likely a precursor of Alzheimer disease (AD). Both verbal and visual memory tests are used in the diagnosis of aMCI; however, it is unknown which type of test is superior at predicting the underlying pathologic changes associated with AD. In this study, we compared the topography of cortical thinning among 3 subtypes of patients with aMCI: 33 patients with predominant verbal memory impairment (verbal-aMCI), 35 with predominant visual memory impairment (visual-aMCI), and 56 with both verbal and visual memory-predominant impairment (both-aMCI), and 143 patients with normal cognition. As a result, patients with verbal-aMCI showed cortical thinning in the left anterior and medial temporal regions compared with individuals with normal cognition, while those with visual-aMCI did not show significant cortical thinning. The cortical thinning areas of both-aMCI group overlapped those of verbal-aMCI but were more widespread involving the bilateral temporal regions. These findings suggest that the verbal-aMCI and both-aMCI are more likely to be a precursor of AD than visual-aMCI, and that both-aMCI may be more advanced subtype than verbal-aMCI on the spectrum from MCI to AD.

In this paper, we deal with a subcortical surface registration problem. Subcortical structures including hippocampi and caudates have a small number of salient features such as heads and tails unlike cortical surfaces. Therefore, it is hard, if not impossible, to perform subcortical surface registration with only such features. It is also non-trivial for neuroanatomical experts to select landmarks consistently for subcortical surfaces of different subjects. We therefore present a landmark-free approach for subcortical surface registration by measuring the amount of mesh distortion between subcortical surfaces assuming that the surfaces are represented by meshes. The input meshes can be constructed using any surface modeling tool available in the public domain since our registration method is independent of a surface modeling process. Given the source and target surfaces together with their representing meshes, the vertex positions of the source mesh are iteratively displaced while preserving the underlying surface shape in order to minimize the distortion to the target mesh. By representing each surface mesh as a point on a high-dimensional Riemannian manifold, we define a distance metric on the manifold that measures the amount of distortion from a given source mesh to the target mesh, based on the notion of isometry while penalizing triangle flipping. Under this metric, we reduce the distortion minimization problem to the problem of constructing a geodesic curve from the moving source point to the fixed target point on the manifold while satisfying the shape-preserving constraint. We adopt a multi-resolution framework to solve the problem for distortion-minimizing mapping between the source and target meshes. We validate our registration scheme through several experiments: distance metric comparison, visual validation using real data, robustness test to mesh variations, feature alignment using anatomic landmarks, consistency with previous clinical findings, and comparison with a surface-based registration method, LDDMM-surface.

The global pattern of cortical sulci provides important information on brain development and functional compartmentalization. Sulcal patterns are routinely used to determine fetal brain health and detect cerebral malformations. We present a quantitative method for automatically comparing and analyzing the sulcal pattern between individuals using a graph matching approach. White matter surfaces were reconstructed from volumetric T1 MRI data and sulcal pits, the deepest points in local sulci, were identified on this surface. The sulcal pattern was then represented as a graph structure with sulcal pits as nodes. The similarity between graphs was computed with a spectral-based matching algorithm by using the geometric features of nodes (3D position, depth and area) and their relationship. In particular, we exploited the feature of graph topology (the number of edges and the paths between nodes) to highlight the interrelated arrangement and patterning of sulcal folds. We applied this methodology to 48 monozygotic twins and showed that the similarity of the sulcal graphs in twin pairs was significantly higher than in unrelated pairs for all hemispheres and lobar regions, consistent with a genetic influence on sulcal patterning. This novel approach has the potential to provide a quantitative and reliable means to compare sulcal patterns.