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Objective: Investigate the association of chronic depressive symptomatology (chrDS) with cortical atrophy rates and conversion to Alzheimer dementia (AD) over 3 years in mild cognitive impairment (MCI)., Methods: In a multicenter, clinic-based study, MCI elderly participants were selected from the Alzheimer's Disease Neuroimaging Initiative repository, based on availability of both serial structural magnetic resonance imaging and chrDS endorsed on three depression-related items from the Neuropsychiatric Inventory Questionnaire (chrDS N = 32 or no depressive symptoms N = 62) throughout follow-up. Clinical and laboratory investigations were performed every 6 months during the first 2 years and yearly thereafter (median follow-up: 3 years; interquartile range: 1.5-4.0 years). Cortical atrophy rates in 16 predefined frontotemporoparietal regions affected in major depression and AD and the rate of incident AD at follow-up., Results: ChrDS in a single domain amnestic MCI sample were associated with accelerated cortical atrophy in the frontal lobe and anterior cingulate but not with atrophy rates in temporomedial or other AD-affected regions. During follow-up, 38 participants (42.7%) developed AD. Participants with chrDS had 60% shorter conversion time to AD than those without depressive symptoms. This association remained significant in survival models adjusted for temporomedial atrophy rates and showed the same trend in models adjusted for frontal cortical atrophy rate, which all increased the risk of AD., Conclusion: Our results suggest that chrDS associated with progressive atrophy of frontal regions may represent an additional risk factor for conversion to dementia in MCI as opposite to representing typical prodromal AD symptomatology., (Published by Elsevier Inc.)

Objective: Cortical atrophy has been associated with late-life depression (LLD) and recent findings suggest that reduced right hemisphere cortical thickness is associated with familial risk for major depressive disorder, but cortical thickness abnormalities in LLD have not been explored. Furthermore, cortical atrophy has been posited as a contributor to poor antidepressant treatment response in LLD, but the impact of cortical thickness on psychotherapy response is unknown. This study was conducted to evaluate patterns of cortical thickness in LLD and in relation to psychotherapy treatment outcomes., Methods: Participants included 22 individuals with LLD and 12 age-matched comparison subjects. LLD participants completed 12 weeks of psychotherapy and treatment response was defined as a 50% reduction in depressive symptoms. All participants underwent magnetic resonance imaging of the brain, and cortical mapping of gray matter tissue thickness was calculated., Results: LLD individuals demonstrated thinner cortex than controls prominently in the right frontal, parietal, and temporal brain regions. Eleven participants (50%) exhibited positive psychotherapy response after 12 weeks of treatment. Psychotherapy nonresponders demonstrated thinner cortex in bilateral posterior cingulate and parahippocampal cortices, left paracentral, precuneus, cuneus, and insular cortices, and the right medial orbitofrontal and lateral occipital cortices relative to treatment responders., Conclusions: Our findings suggest more distributed right hemisphere cortical abnormalities in LLD than have been previously reported. In addition, our findings suggest that reduced bilateral cortical thickness may be an important phenotypic marker of individuals at higher risk for poor response to psychotherapy., (Copyright © 2013 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.)

Prior studies on healthy children have demonstrated regional variations and a complex and dynamic relationship between intelligence and cerebral tissue. Yet, there is little information regarding the neuroanatomical correlates of general intelligence in children with epilepsy compared to healthy controls. In vivo imaging techniques, combined with methods for advanced image processing and analysis, offer the potential to examine quantitative mapping of brain development and its abnormalities in childhood epilepsy. A surface-based, computational high resolution 3-D magnetic resonance image analytic technique was used to compare the relationship of cortical thickness with age and intelligence quotient (IQ) in 65 children and adolescents with complex partial seizures (CPS) and 58 healthy controls, aged 6-18 years. Children were grouped according to health status (epilepsy; controls) and IQ level (average and above; below average) and compared on age-related patterns of cortical thickness. Our cross-sectional findings suggest that disruption in normal age-related cortical thickness expression is associated with intelligence in pediatric CPS patients both with average and below average IQ scores., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Background: At least 60% of those treated for an alcohol use disorder will relapse. Empirical study of the integrity of the brain reward system (BRS) is critical to understanding the mechanisms of relapse as this collection of circuits is implicated in the development and maintenance of all forms of addictive disorders. This study compared thickness, surface area, and volume in neocortical components of the BRS among nonsmoking light-drinking controls (controls), individuals who remained abstinent and those who relapsed after treatment., Methods: Seventy-five treatment-seeking alcohol-dependent individuals (abstinent for 7±3 days) and 43 controls completed 1.5T proton magnetic resonance imaging studies. Parcellated morphological data were obtained for following bilateral components of the BRS: rostral and caudal anterior cingulate cortex, insula, medial and lateral orbitofrontal cortex (OFC), rostral and caudal middle and superior frontal gyri, amygdala and hippocampus as well as for 26 other bilateral neocortical regions. Alcohol-dependent participants were followed over 12-months after baseline study and were classified as abstainers (no alcohol consumption; n=24) and relapsers (any alcohol consumption; n=51) at follow-up., Results: Relapsers and abstainers demonstrated lower cortical thickness in the vast majority of BRS regions as well as lower global thickness compared to controls. Relapsers had lower total BRS surface area than both controls and abstainers, but abstainers were not significantly different from controls on any surface area measure. Relapsers demonstrated lower volumes than controls in the majority of regions, while abstainers showed lower volumes than controls in the superior frontal gyrus, insula, amygdala, and hippocampus, bilaterally. Relapsers exhibited smaller volumes than abstainers in the right rostral middle and caudal middle frontal gyri and the lateral OFC, bilaterally. In relapsers, lower baseline volumes and surface areas in multiple regions were associated with a greater magnitude of post-treatment alcohol consumption., Conclusions: Results suggest relapsers demonstrated morphological abnormalities in regions involved in the "top down" regulation/modulation of internal drive states, emotions, reward processing, and behavior, which may impart increased risk for the relapse/remit cycle that afflicts many with an alcohol use disorder. Results also highlight the importance of examining both cortical thickness and surface area to better understand the nature of regional volume loss frequently observed in alcohol use disorders. Results from this report are consistent with previous research implicating plastic neurobiological changes in the BRS in the maintenance of addictive disorders., (Copyright © 2011 by the Research Society on Alcoholism.)

In this study, we used surface-based morphometry to examine whether age-related changes in gray matter tissue thickness and depth of sulcal regions at high spatial resolution across the cortex differed in children with childhood absence epilepsy (CAE) compared to healthy control subjects. In addition, the possibility of variable brain-cognition relationships in the CAE compared to the control group was investigated. The main findings of this study are as follows: (1) From the developmental perspective, children with CAE did not demonstrate the normal regional age-related changes involving a decrease in cortical thickness and increase in sulcal depth. (2) None of the seizure variables, including age of onset, seizure frequency, and AEDs had a significant effect on the association between age and cortical morphometry measures in the CAE population. (3) Even though the CAE group had mean VIQ and PIQ scores in the average range, our findings suggest that they use different brain regions to perform these cognitive functions compared to healthy controls. This first study on brain morphometry and cognition in children with childhood absence seizures has important implications for advancing our understanding of brain development and cognitive comorbidity in CAE, as well as for revisiting the clinical notion that CAE is a benign disorder., (Copyright © 2010 Wiley-Liss, Inc.)

Previously it was reported that Alzheimer's disease (AD) patients have reduced amyloid (Aβ 1-42) and elevated total tau (t-tau) and phosphorylated tau (p-tau 181p) in the cerebro-spinal fluid (CSF), suggesting that these same measures could be used to detect early AD pathology in healthy elderly (CN) and mild cognitive impairment (MCI). In this study, we tested the hypothesis that there would be an association among rates of regional brain atrophy, the CSF biomarkers Aβ 1-42, t-tau, and p-tau 181p and ApoE ε4 status, and that the pattern of this association would be diagnosis specific. Our findings primarily showed that lower CSF Aβ 1-42 and higher tau concentrations were associated with increased rates of regional brain tissue loss and the patterns varied across the clinical groups. Taken together, these findings demonstrate that CSF biomarker concentrations are associated with the characteristic patterns of structural brain changes in CN and MCI that resemble to a large extent the pathology seen in AD. Therefore, the finding of faster progression of brain atrophy in the presence of lower Aβ 1-42 levels and higher p-tau levels supports the hypothesis that CSF Aβ 1-42 and tau are measures of early AD pathology. Moreover, the relationship among CSF biomarkers, ApoE ε4 status, and brain atrophy rates are regionally varying, supporting the view that the genetic predisposition of the brain to amyloid and tau mediated pathology is regional and disease stage specific.

Spatial normalization is frequently used to map data to a standard coordinate system by removing intersubject morphological differences, thereby allowing for group analysis to be carried out. The work presented in this paper is motivated by the need for an automated cortical surface normalization technique that will automatically identify homologous cortical landmarks and map them to the same coordinates on a standard manifold. The geometry of a cortical surface is analyzed using two shape measures that distinguish the sulcal and gyral regions in a multiscale framework. A multichannel optical flow warping procedure aligns these shape measures between a reference brain and a subject brain, creating the desired normalization. The partial differential equation that carries out the warping is implemented in a Euclidean framework in order to facilitate a multiresolution strategy, thereby permitting large deformations between the two surfaces. The technique is demonstrated by aligning 33 normal cortical surfaces and showing both improved structural alignment in manually labeled sulci and improved functional alignment in positron emission tomography data mapped to the surfaces. A quantitative comparison between our proposed surface-based spatial normalization method and a leading volumetric spatial normalization method is included to show that the surface-based spatial normalization performs better in matching homologous cortical anatomies.

Two different studies were conducted to assess the accuracy and precision of an algorithm developed for automatic reconstruction of the cerebral cortex from T1-weighted magnetic resonance (MR) brain images. Repeated scans of three different brains were used to quantify the precision of the algorithm, and manually selected landmarks on different sulcal regions throughout the cortex were used to analyze the accuracy of the three reconstructed surfaces: inner, central, and pial. We conclude that the algorithm can find these surfaces in a robust fashion and with subvoxel accuracy, typically with an accuracy of one third of a voxel, although this varies with brain region and cortical geometry. Parameters were adjusted on the basis of this analysis in order to improve the algorithm's overall performance.

With the improvements in techniques for generating surface models from magnetic resonance (MR) images, it has recently become feasible to study the morphological characteristics of the human brain cortex in vivo. Studies of the entire surface are important for measuring global features, but analysis of specific cortical regions of interest provides a more detailed understanding of structure. We have previously developed a method for automatically segmenting regions of interest from the cortical surface using a watershed transform. Each segmented region corresponds to a cortical sulcus and is thus termed a "sulcal region." In this work, we describe two important augmentations of this methodology. First, we describe a user interface that allows for the efficient labeling of the segmented sulcal regions called the Program for Assisted Labeling of Sulcal Regions (PALS). An additional augmentation allows for even finer divisions on the cortex with a methodology that employs the fast marching technique to track a curve on the cortical surface that is then used to separate segmented regions. After regions of interest have been identified, we compute both the cortical surface area and gray matter volume. Reliability experiments are performed to assess both the long-term stability and short-term repeatability of the proposed techniques. These experiments indicate the proposed methodology gives both highly stable and repeatable results.

Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.

Visualization and mapping of function on the cortical surface is difficult because of its sulcal and gyral convolutions. Methods to unfold and flatten the cortical surface for visualization and measurement have been described in the literature. This makes visualization and measurement possible, but comparison across multiple subjects is still difficult because of the lack of a standard mapping technique. In this paper, we describe two methods that map each hemisphere of the cortex to a portion of a sphere in a standard way. To quantify how accurately the geometric features of the cortex -- i.e., sulci and gyri -- are mapped into the same location, sulcal alignment across multiple brains is analyzed, and probabilistic maps for different sulcal regions are generated to be used in automatic labelling of segmented sulcal regions.

Segmentation and mapping of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuroscience and medicine. This paper describes a comprehensive approach for cortical reconstruction, flattening, and sulcal segmentation. Robustness to imaging artifacts and anatomical consistency are key achievements in an overall approach that is nearly fully automatic and computationally fast. Results demonstrating the application of this approach to a study of cortical thickness changes in aging are presented.