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An automated labeling system for subdiving the human cerebral cortex on MRI scans into gyral based regions of interest
Abstract
In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
... FWE-correction was performed to control for multiple comparisons (P < 0.05). Anatomical allocations were performed using standard atlases 15,16 . No significant voxel association was observed for depression, as measured by the GDS-15. ...
... The family-wise error (FWE)method was employed to correct for multiple comparisons (i.e., across voxels). Cortical morphometry including cortical thickness, cortical surface area, and gray-matter volume was further compared between groups using the FreeSurfer V6.0 toolbox with default parameters 14 ; cortical parcellation was performed thereafter using the Desikan-Killiany atlas (DKT) 16 . Age and sex served as nuisance covariates and the FWE method was applied to correct for multiple comparisons (i.e., across DKT-ROIs). ...
... Age and sex served as nuisance covariates and FWEcorrection was applied to control for multiple comparisons (i.e., across voxels). The resulting areas of significance were converted to a binary mask and anatomical allocation was performed secondarily using various neuroanatomical atlases as references [15][16][17][18] . ...
After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition, marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with Post-COVID-Condition, healthy controls, and individuals that contracted COVID-19 without long-term symptoms. We reveal widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in close anatomical-functional relationship with the respective symptoms.
... Finally, average BEN values were extracted from 68 regions of the Desikan-Killiany cortical parcellation (25). ...
... total GMV (mm 3 ), total SA (mm 2 ), and average CT (mm). In addition, GMV, SA, and CT values were extracted from the regions of the Desikan-Killiany cortical parcellation (25). ...
In recent years, brain entropy (BEN) has been ossociated with a number of neurocognitive, biological, and sociodemographic variables. However, its link with brain morphology is still unknown. In this study, we use resting-state fMRI (rsfMRI) data to estimate BEN maps and investigate their associations with three metrics of brain morphology: gray matter volume (GMV), surface area (SA), and cortical thickness (CT). Separate analyses will be performed on BEN maps derived from four distinct rsfMRI runs, and using both a voxelwise and a regions of interest (ROIs) approach. Our findings consistently showed that lower BEN (i.e., higher temporal coherence of brain activity) was related to increased GMV and SA in the lateral frontal and temporal lobes, inferior parietal lobules, and precuneus. We hypothesize that lower BEN and higher SA might both reflect higher brain reserve as well as increased information processing capacity.
... In the first-level analysis, Seed-based connectivity maps (SBC) were estimated to identify the spatial patterns of functional connectivity associated with specified seed regions. Eight regions of interest (ROIs) from the Harvard-Oxford atlas were selected (57). The basis of selection was the literature proposing these regions as key elements of resilience (24,25). ...
Recent resilience research has increasingly emphasized the importance of focusing on investigating the protective factors in mentally healthy populations, complementing the traditional focus on psychopathology. Social support has emerged as a crucial element within the complex interplay of individual and socio-environmental factors that shape resilience. However, the neural underpinnings of the relationship between social support and resilience, particularly in healthy subjects, remain largely unexplored. With advances in neuroimaging techniques, such as ultra-high field MRI at 7T and beyond, researchers can more effectively investigate the neural mechanisms underlying these factors. Thus, our study employed ultra-high field rs-fMRI to explore how social support moderates the relationship between psychological resilience and functional connectivity in a healthy cohort. We hypothesized that enhanced social support would amplify resilience-associated connectivity within neural circuits essential for emotional regulation, cognitive processing, and adaptive problem-solving, signifying a synergistic interaction where strong social networks bolster the neural underpinnings of resilience. (n = 30). Through seed-based functional connectivity analyses and interaction analysis, we aimed to uncover the neural correlates at the interplay of social support and resilience. Our findings indicate that perceived social support significantly (p<0.001) alters functional connectivity in the right and left FP, PCC, and left hippocampus, affirming the pivotal roles of these regions in the brain’s resilience network. Moreover, we identified significant moderation effects of social support across various brain regions, each showing unique connectivity patterns. Specifically, the right FP demonstrated a significant interaction effect where high social support levels were linked to increased connectivity with regions involved in socio-cognitive processing, while low social support showed opposite effects. Similar patterns by social support levels were observed in the left FP, with connectivity changes in clusters associated with emotional regulation and cognitive functions. The PCC’s connectivity was distinctly influenced by support levels, elucidating its role in emotional and social cognition. Interestingly, the connectivity of the left hippocampus was not significantly impacted by social support levels, indicating a unique pattern within this region. These insights highlight the importance of high social support levels in enhancing the neural foundations of resilience and fostering adaptive neurological responses to environmental challenges.
... We implemented the fully automated 'Recon-All' pipeline provided by Freesurfer (27). Eight different types of radiomic features i.e., the total gray matter volume (GMV, mm^3), the number of vertices, the average cortical thickness (mm), the total surface areas (mm^2), the integrated rectified mean curvature (mm^-1), the integrated rectified Gaussian curvature (mm^-2), the folding index, and the intrinsic curvature index) were calculated for the precuneus and fusiform gyrus from both left and right hemisphere. ...
Alzheimer’s disease (AD) is acknowledged as one of the most common types of dementia. Various brain regions were found to associated with AD pathology. Precuneus and fusiform gyrus are two notable regions whose role has been implicated in cognitive function. However, a thorough investigation was lacking to link these regions with AD pathology. In this study, we conducted a comprehensive radiomic based investigation using magnetic resonance imaging (MRI) scans to link precuneus and fusiform gyrus with AD pathology. We obtained T1 weighted MR scans of AD (n=133), MCI (n=311) and CN (n=195) subjects from ADNI database at three different time points (i.e., 0, 6 and 12 months). Then, we conducted statistical analysis to compare these features among AD, MCI and CN subjects. We found significant decline in gray matter volume (GMV) and cortical thickness of both precuneus and fusiform gyrus in AD as compared to the MCI and CN subjects. Further, we utilized these features to develop machine learning classifiers to classify AD from MCI and CN subjects and achieved accuracy of 97.78% and 94.41% respectively. These results strengthen the connection of precuneus and fusiform gyrus with AD pathology and opens a new avenue of AD research.
... In some cases, however, classes were added for specific software as examples of analyses or where the software is ubiquitous with the analysis type -e.g. FreeSurfer (Desikan et al., 2006;Fischl, 2012;Fischl et al., 1999) for cortical thickness estimation. Table 2 contains a breakdown of the software included in MRIO. ...
Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a “common language” for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.
... Neurophet AQUA is a brain MRI segmentation software based on the deep-learning algorithm, the Split-attention U-net (SAU-Net), as previously described [30]. Briefly, regions of interest (ROIs) were defined based on the Desikan-Killiany atlas as in FreeSurfer [31]. Neurophet AQUA was then trained with the ROI definitions reviewed and corrected by neuroradiologists. ...
Background: Application of visual scoring scales for regional atrophy in Alzheimer’s disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement. Objective: To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI). Methods: We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability. Results: Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool. Conclusions: Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.
... surface affected) and across these functional networks (i.e., percent of the functional network affected). Specific brain regions were identified according to the Desikan cortical atlas [56]. All brain maps were visualized using fsbrain in R [57]. ...
Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2–13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.
... To improve interpretability, rather than us-ing raw time series data as predictors, we map the signals recorded from the scalp onto the brain surface. We then consolidate the voxel-level time series into region-level aggregates, which leads to a 68-dimensional functional predictor based on the Desikan-Killiany (DK) atlas (Desikan et al. 2006). Finally, Fourier transformations are applied independently to each brain signal to generate region-wise PSDs over the whole brain. ...
As medical devices become more complex, they routinely collect extensive and complicated data. While classical regressions typically examine the relationship between an outcome and a vector of predictors, it becomes imperative to identify the relationship with predictors possessing functional structures. In this article, we introduce a novel inference procedure for examining the relationship between outcomes and large-scale functional predictors. We target testing the linear hypothesis on the functional parameters under the generalized functional linear regression framework, where the number of the functional parameters grows with the sample size. We develop the estimation procedure for the high dimensional generalized functional linear model incorporating B-spline functional approximation and amenable regularization. Furthermore, we construct a procedure that is able to test the local alternative hypothesis on the linear combinations of the functional parameters. We establish the statistical guarantees in terms of non-asymptotic convergence of the parameter estimation and the oracle property and asymptotic normality of the estimators. Moreover, we derive the asymptotic distribution of the test statistic. We carry out intensive simulations and illustrate with a new dataset from an Alzheimer's disease magnetoencephalography study.
... Briefly, Freesurfer was applied to qualified T1 weighted data to generate the cortical volume (VL), cortical thickness (CT), and surface area (SA) of 130 brain regions (http://surfer.nmr.mgh.harvard.edu/fswiki/ FreeSurferMethodsCitation) using the Desikan-Killiany atlas and the Destrieux atlas (Desikan et al., 2006;Reuter et al., 2012), which was provided by the ADNI-1 (Fischl, 2012;Schwarz et al., 2016). ...
The high prevalence of conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) makes early prevention of AD extremely critical. Neuroticism, a heritable personality trait associated with mental health, has been considered a risk factor for conversion from aMCI to AD. However, whether the neuroticism genetic risk could predict the conversion of aMCI and its underlying neural mechanisms is unclear. Neuroticism polygenic risk score (N‐PRS) was calculated in 278 aMCI patients with qualified genomic and neuroimaging data from ADNI. After 1‐year follow‐up, N‐PRS in patients of aMCI‐converted group was significantly greater than those in aMCI‐stable group. Logistic and Cox survival regression revealed that N‐PRS could significantly predict the early‐stage conversion risk from aMCI to AD. These results were well replicated in an internal dataset and an independent external dataset of 933 aMCI patients from the UK Biobank. One sample Mendelian randomization analyses confirmed a potentially causal association from higher N‐PRS to lower inferior parietal surface area to higher conversion risk of aMCI patients. These analyses indicated that neuroticism genetic risk may increase the conversion risk from aMCI to AD by impairing the inferior parietal structure.
... Values of gyrification were defined through LGI, which is the ratio of the inner to outer smoothed cortical surface. LGI values were extracted across both hemispheres for the cortical lobes (the frontal, parietal, temporal, occipital, and cingulate) as well as the 33 individual regions defined through FreeSurfer cortical parcellation procedures using the Desikan-Killany cortical atlas and the insula (Desikan et al., 2006). Estimated total intracranial volume was extracted from FreeSurfer. ...
... In this context, V was defined as vertices of triangles on the mesh, and proximal links were defined as sides of triangles in the mesh (Fig. 2a). A set of districts A was defined according to Desikan-Killiany cortical atlas [39], resulting in |A| = 34 × 2 for both hemispheres (Fig. 2b). ...
Tau tangles in the brain cortex spread along the brain network in distinct patterns among Alzheimer's patients. We aim to simulate their network-based spreading within the cortex, tailored to each individual along the Alzheimer's continuum, without assuming any assumptions about the network architecture. A group-level intrinsic spreading network was constructed to model the pathways for the proximal and distal spreading of tau tangles by optimizing the biophysical model based on a discovery dataset of longitudinal tau positron emission tomography images for 78 amyloid-positive individuals. Group-level spreading parameters were also obtained and subsequently adjusted to produce individuated tau trajectories. By simulating these individuated tau spreading models for every individual in the discovery dataset, we successfully captured proximal and distal tau spreading, allowing reliable inferences about the underlying mechanism of tau spreading. Simulating the models also allowed highly accurate prediction of future tau topography for both discovery and independent validation datasets.
... FreeSurfer v.5.3 (ref. 61) was utilized to estimate surface area and cortical thickness corresponding to the Desikan-Killiany atlas 62 (refer to Supplementary Method 3 for imaging acquisition and preprocessing) 63 . Structural images that did not pass quality control were excluded, and data of 5,121 adolescents entered the neuroimaging analyses in this study (Supplementary Fig. 1). ...
Emotion dysregulation is common in attention deficit hyperactivity disorder (ADHD), which is known to be clinically heterogeneous. However, it remains unclear whether emotion dysregulation represents a neuropsychological pathway to ADHD. Here, using a large population-based cohort (n = 6,053), we show that emotion dysregulation was associated with ADHD symptoms (partial eta2 = 0.21) and this persisted after controlling for the cognitive and motivational deficits. Emotion dysregulation mediated the association between smaller surface area of the right pars orbitalis and greater ADHD symptoms at 1-year follow-up, indicating an emotion pathway for ADHD. This pathway was associated with immune responses by both transcriptomic analyses and white blood cell markers. In an independent clinical sample for ADHD (n = 672), the emotion pathway improved the case/control classification accuracy. These findings suggest that emotion dysregulation is a core symptom and route to ADHD, which may not respond to the current pharmacological treatments for ADHD.
... This process consisted of several step, including motion corretion in the images to enable accurate anatomical measurements, transformation the images into Talairach space to accurately map brain structures, removal of the skull and other external tissues to preserve only the brain tissue, classification of the brain tissue into white matter, gray matter, cerebrospinal fluid, etc., for anatomical analysis, correction of the topology of the brain surface, generation of the outer and central brain surfaces for three-dimensional visualization of brain structure, division of the brain into multiple regions for structural analysis, and measurement of the seven morphometric features. These features were extracted for the 68 regions of interest (ROIs), delineated according to the Desikan-Killiany atlas [17]. Pearson's correlation was utilized to evaluate the morphometric similarity between every pair of ROIs, generating a 68 × 68 matrix [18,19]. ...
Purpose
The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis.
Methods
Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies.
Results
We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls.
Conclusion
Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.
... The composition of each voxel as WM or GM was determined by calculating the percentage of subjects classifying them as such (threshold 0.6 for WM, 0.2 for GM). The Harvard-Oxford Atlas (Desikan et al., 2006) was used to identify and label voxels corresponding to specific subcortical structures as GM, which were removed from the WM mask. Finally, the obtained mask was compared with subjects' mean functional images, and regions with ≥80% corresponding voxels were designated as part of the group-level WM or GM masks. ...
Background. The maturation and ageing of the human brain involve intricate biological processes that result in complex changes in brain structure and function. Although the effects of aging on functional connectivity within gray matter (GM) regions have been extensively studied, the investigation of white matter (WM) functional changes remains limited.
Methods. In this study, our aim was to investigate age-related trajectory in WM functional dynamics using resting-state functional magnetic resonance imaging (rs-fMRI) data from a large lifespan sample of 494 individuals. First, GM and WM functional networks (FNs) were identified by k-means clustering. Next, we performed static and dynamic analysis of WM functional network connectivity (FNC) to explore age effect on WM-FNs. Furthermore, we investigated recurrent patterns of dynamic FNC. Finally, we conducted several validation analyses to ensure replicability.
Results. We identified 9 reliable WM and 12 GM FNs. The findings revealed age-related effects on WM FNC strength and WM-GM FNC dynamics, primarily including linear positive and U-shaped age trajectories in static FNC strength, as well as linear negative and inverted U-shaped age trajectories in FNC temporal variability. Additionally, we identified three distinct brain states with significant age-related differences. The aforementioned findings were largely replicated in the validation analysis.
Conclusion. High integration and low temporal variability in WN-GM FNC may reflect a less efficient network system in older adults. These findings enhance our understanding of brain aging processes and provide insights into the trajectory of WM functional dynamics during normal ageing.
... To avoid neighboring generators to blur the results, sources in opposite directions were ipped [42]. Finally, the 15000 sources were grouped into 68 regions of interest (ROIs), according to the Desikan-Killiany atlas [32,33,43] EEG analyses: estimating the neural networks There are very different ways of analyzing the FC patterns: considering the phase, with metrics as the Phase Lag Index (PLI); studying the amplitude-based couplings, with metrics as the Amplitude Envelope Correlation; or assessing the spectral distribution, with metrics as the Coherence [44]. In this study, we computed the PLI, as it has been widely used in other studies employing EEG signals, and it showed a high robustness against volume conduction effects [45][46][47][48]. ...
Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.
... Atlas-based automated labeling is a more effective and efficient way to study brain morphology and parcellate the brain for subsequent analyses, compared with time-consuming manual labeling. While brain atlases are relatively well established for adults (e.g., Collins et al., 1994;Desikan et al., 2006;Fan et al., 2016;Mori et al., 2008;Oishi et al., 2008) and neonates (e.g., Alexander et al., 2017;de Macedo Rodrigues et al., 2015;Feng et al., 2019;Gousias et al., 2012;Kabdebon et al., 2014;Oishi et al., 2011), they are not fully developed for young children aged 1 and 2 years. ...
Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas‐based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age‐specific 1‐ and 2‐year‐old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high‐quality DTI and structural MRI data were obtained from 50 healthy children to build up three‐dimensional age‐specific 1‐ and 2‐year‐old brain templates and atlases. Age‐specific templates include a single‐subject template as well as two population‐averaged templates from linear and nonlinear transformation, respectively. Each age‐specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age‐specific atlases. These established 1‐ and 2‐year‐old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.
... harva rd. edu) to automatically extract the dentate gyrus as well as the whole hippocampus from the Desikan-Killiany atlas 59,60 . The hypothalamus was delineated via nonlinear registration of the participant's T1 image to the MNI template for the use of the CTI168 high-resolution subcortical brain nucleus atlas 61,62 . ...
Reduced hippocampal volume occurs in major depressive disorder (MDD), potentially due to elevated glucocorticoids from an overactivated hypothalamus–pituitary–adrenal (HPA) axis. To examine this in humans, hippocampal volume and hypothalamus (HPA axis) metabolism was quantified in participants with MDD before and after antidepressant treatment. 65 participants (n = 24 males, n = 41 females) with MDD were treated in a double-blind, randomized clinical trial of escitalopram. Participants received simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) before and after treatment. Linear mixed models examined the relationship between hippocampus/dentate gyrus volume and hypothalamus metabolism. Chi-squared tests and multivariable logistic regression examined the association between hippocampus/dentate gyrus volume change direction and hypothalamus activity change direction with treatment. Multiple linear regression compared these changes between remitter and non-remitter groups. Covariates included age, sex, and treatment type. No significant linear association was found between hippocampus/dentate gyrus volume and hypothalamus metabolism. 62% (38 of 61) of participants experienced a decrease in hypothalamus metabolism, 43% (27 of 63) of participants demonstrated an increase in hippocampus size (51% [32 of 63] for the dentate gyrus) following treatment. No significant association was found between change in hypothalamus activity and change in hippocampus/dentate gyrus volume, and this association did not vary by sex, medication, or remission status. As this multimodal study, in a cohort of participants on standardized treatment, did not find an association between hypothalamus metabolism and hippocampal volume, it supports a more complex pathway between hippocampus neurogenesis and hypothalamus metabolism changes in response to treatment.
... We performed ROI-based analysis by extracting the mean rs-CBF values from the mask of each cortical and subcortical ROI of the Harvard-Oxford atlas (Desikan et al., 2006;Frazier et al., 2005;Goldstein et al., 2007;Makris et al., 2006), as well as the tissue type segmentation (grey matter, white matter, and CSF), and the cerebellum from the MNI structural atlas. ...
... We studied 2,730 unmedicated healthy individuals (12-68 years, 47% male) with varying levels of self-reported positive and negative schizotypy derived from 31 sites of the worldwide ENIGMA Schizotypy Working Group (Tables S1-4). By applying standardized protocols to 3D brain MRI scans, cortical thickness (CT) and surface area (SA) were measured of 68 gray matter brain regions, based on the Desikan-Killiany-Tourville (DKT) anatomical atlas 51 , and subcortical volume (SV) of 16 subcortical regions using FreeSurfer 52,53 (http://surfer.nmr.mgh.harvard.edu) and standardized ENIGMA protocols (http:// enigma.ini.usc.edu/protocols/imaging-protocols/). ...
Positive and negative schizotypy reflect distinct patterns of subclinical traits in the general population associated with neurodevelopmental and schizophrenia-spectrum pathologies. Yet, a comprehensive characterization of the unique and shared neuroanatomical signatures of these schizotypy dimensions is lacking. Leveraging 3D brain MRI data from 2,730 unmedicated healthy individuals, we identified neuroanatomical profiles of positive and negative schizotypy and systematically compared them to disorder-specific, micro-architectural, connectome, and neurotransmitter-level measures. Positive and negative schizotypy were associated with thinner frontal and thicker paralimbic cortical areas, respectively, and were differentially linked to cortical patterns of schizophrenia-spectrum and neurodevelopmental conditions. Furthermore, these schizotypal cortical patterns mapped onto local attributes of gene expression, cortical myelination, D1 and histamine receptor distributions. Network models identified cortical hub vulnerability to schizotypy-related thickness reduction and epicenters in sensorimotor-to-association and paralimbic areas. This study yields insights into the complex cortical signatures of schizotypy and their relationship to diverse features of cortical organization.
... The purpose of this section is to investigate how brain-wise spatial harmonization affects locally within a predefined region. We use Desikan-Killiany Atlas (Desikan et al., 2006), which includes 34 regions of interest (ROIs) in each hemisphere (after excluding corpus callosum). ...
In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter‐scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter‐scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex‐level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex‐level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner‐invariant and scanner‐specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.
... One AD patient with skull strip errors and one control with white matter segment errors were manually corrected and re-run. The cerebral cortex was parcellated into 68 brain regions using the Desikan-Killiany template (Desikan et al., 2006), and the average cortical thickness for each region was obtained. ...
While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.
... In this study, we denote the N subjects as S 1 ,S 2 ,⋯,S N . Firstly, the brain is partitioned into different ROIs based on the Harvard-Oxford (HO) atlas [24]. From each ROI, a set of time series is extracted and subsequently normalised. ...
Diagnosing individuals with autism spectrum disorder (ASD) accurately faces great challenges in clinical practice, primarily due to the data's high heterogeneity and limited sample size. To tackle this issue, the authors constructed a deep graph convolutional network (GCN) based on variable multi‐graph and multimodal data (VMM‐DGCN) for ASD diagnosis. Firstly, the functional connectivity matrix was constructed to extract primary features. Then, the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel sizes. Furthermore, the authors brought the non‐imaging information into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between subjects. After extracting the deeper features of population graphs using the deep GCN(DeepGCN), the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD patients. The proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I (ABIDE I) dataset, achieving an accuracy of 91.62% and an area under the curve value of 95.74%. These results demonstrated its outstanding performance compared to other ASD diagnostic algorithms.
... using its default recon-all pipeline. Regional volume and thickness data were then extracted from the preprocessed images using the Desikan-Killiany atlas [21], which consists of 34 cortical regions of interest (ROIs) per hemisphere. ...
BACKGROUND: We investigated the association between path-integration (PI) errors related to entorhinal cortex function detectable using a 3D virtual reality (VR) navigation system and various biomarkers to explore its potential as an early AD indicator.
METHODS: The PI capabilities of 111 healthy adults were assessed using a head-mounted 3D VR system. Demographic and cognitive assessments, AD-related plasma biomarkers, and apolipoprotein E genotypes were also evaluated. Predictive factors for PI errors were identified using multivariate linear regression, logistic regression, and random forest.
RESULTS: PI errors positively correlated with age, plasma levels of glial fibrillary acidic protein [GFAP], neurofilament light, and p-tau181. Multivariate analysis identified plasma GFAP and p-tau181 levels as significant predictors. Random forest analysis and receiver operating characteristic curves underscored plasma p-tau181 levels as the most substantial predictor.
DISCUSSION: PI errors, particularly in conjunction with plasma p-tau181 levels, could reflect early AD pathophysiology, highlighting their potential as early biomarkers.
... In this study, the fMRI scan data were obtained from the Configurable Connectome Analysis Pipeline (CPAC) 31 in the Preprocessed Connectome Project, which includes AAL atlas 32 , Harvard-Oxford (HO) atlas 33 , and Craddock200 (CC200) atlas 34 . Each of the three atlases defines a different ROI and uses BOLD signal imaging that can indirectly reflect the metabolism of brain activity. ...
The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance. To address the privacy preservation issue as well as to improve ASD diagnosis, we propose a deep learning framework using multimodal feature fusion and hypergraph neural networks for disease prediction in federated learning (FedHNN). By introducing the federated learning strategy, each local model is trained and computed independently in a distributed manner without data sharing, allowing rapid scaling of medical datasets to achieve robust and scalable deep learning predictive models. To further improve the performance with privacy preservation, we improve the hypergraph model for multimodal fusion to make it suitable for autism spectrum disorder (ASD) diagnosis tasks by capturing the complementarity and correlation between modalities through a hypergraph fusion strategy. The results demonstrate that our proposed federated learning-based prediction model is superior to all local models and outperforms other deep learning models. Overall, our proposed FedHNN has good results in the work of using multi-site data to improve the performance of ASD identification.
... This was done by extracting a subset of voxels from the average relevance map R dementia belonging to each region ρ (Eq. (16)) from the Harvard-Oxford cortical and subcortical atlases 77 . ...
Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.
... Anatomical connectome reconstruction was performed using the CATO toolbox [53]. Hundred and fourteen nodes (i.e., cortical brain regions, based on the Cammoun subdivision of Freesurfer's Desikan-Killiany atlas [54,55]) were obtained from T1-weighted MRI, while edges (i.e., the mean fractional anisotropy (FA) of a fiber tract connecting a pair of nodes) were reconstructed from diffusion-weighted MRI. FA is an established parameter of WM integrity that has been associated with MDD and cognitive performance in previous neuroimaging studies [18,[56][57][58]. ...
Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.
... For our interest in auditory and linguistic processing, we restricted our analysis to vertices in cortical regions that are previously known to be involved in speech processing so as to avoid unnecessary computations. Specifically, from the automatic parcellation based on the Desikan-Killiany cortical atlas (Desikan et al. 2006), the following 19 labels were included: "bankssts," "caudalmiddlefrontal," "inferiorparietal," "inferiortemporal," "lateralorbitofrontal," "middletemporal," "parsopercularis," "parsorbitalis," "parstriangularis," "postcentral," "precentral," "rostralmiddlefrontal," "superiorparietal," "superiortemporal," "supramarginal," "frontalpole," "temporalpole," "transversetemporal," and "insula." The regions of interest are visualized in Supplementary Fig. S6. ...
Speech comprehension entails the neural mapping of the acoustic speech signal onto learned linguistic units. This acousto-linguistic transformation is bi-directional, whereby higher-level linguistic processes (e.g. semantics) modulate the acoustic analysis of individual linguistic units. Here, we investigated the cortical topography and linguistic modulation of the most fundamental linguistic unit, the phoneme. We presented natural speech and “phoneme quilts” (pseudo-randomly shuffled phonemes) in either a familiar (English) or unfamiliar (Korean) language to native English speakers while recording functional magnetic resonance imaging. This allowed us to dissociate the contribution of acoustic vs. linguistic processes toward phoneme analysis. We show that (i) the acoustic analysis of phonemes is modulated by linguistic analysis and (ii) that for this modulation, both of acoustic and phonetic information need to be incorporated. These results suggest that the linguistic modulation of cortical sensitivity to phoneme classes minimizes prediction error during natural speech perception, thereby aiding speech comprehension in challenging listening situations.
Several attempts for speech brain–computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model’s performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3% (p>0.05;d=0.07)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$( p > 0.05; d=0.07)$$\end{document}. Therefore, the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by employing several overt speeches.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that primarily affects the hippocampus. Since hippocampal studies have highlighted a differential subregional regulation along its longitudinal axis, a more detailed analysis addressing subregional changes along the longitudinal hippocampal axis has the potential to provide new relevant biomarkers. This study included structural brain MRI data of 583 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cognitively normal (CN) subjects, mild cognitively impaired (MCI) subjects and AD patients were conveniently selected considering the age- and sex-match between clinical groups. Structural MRI acquisitions were pre-processed and analysed with a new longitudinal axis segmentation method, dividing the hippocampus in three subdivisions (anterior, intermediate, and posterior). When normalizing the volume of hippocampal sub-divisions to total hippocampus, the posterior hippocampus negatively correlates with age only in CN subjects (r = -0.31). The longitudinal ratio of hippocampal atrophy (anterior sub-division divided by the posterior one) shows a significant increase with age only in CN (r = 0.25). Overall, in AD the posterior hippocampus is predominantly atrophied early on. Consequently, the anterior/posterior hippocampal ratio is an AD differentiating metric at early disease stages with potential for diagnostic and prognostic applications.
Objective
Cross‐sectional definitions of successful cognitive aging have been widely utilized, but longitudinal measurements can identify people who do not decline. We performed this study to contrast maintenance with declining trajectories, including clinical conversion.
Methods
We included baseline cognitively unimpaired Alzheimer's Disease Neuroimaging Initiative participants with 3 or more cognitive testing sessions (n = 539, follow‐up 6.1 ± 3.5 years) and calculated slopes of an episodic memory composite (MEM) to classify them into two groups: maintainers (slope ≥ 0) and decliners (slope < 0). Within decliners, we examined a subgroup of individuals who became clinically impaired during follow‐up. These groups were compared on baseline characteristics and cognitive performance, as well as both cross‐sectional and longitudinal Alzheimer disease (AD) biomarker measures (beta‐amyloid [Aβ], tau, and hippocampal volume).
Results
Forty‐one percent (n = 221) of the cohort were MEM maintainers, and 33% (n = 105) of decliners converted to clinical impairment during follow‐up. Compared to those with superior baseline scores, maintainers had lower education and were more likely to be male. Maintainers and decliners did not differ on baseline MEM scores, but maintainers did have higher non‐MEM cognitive scores. Maintainers had lower baseline global Aβ, lower tau pathology, and larger hippocampal volumes than decliners, even after removing converters. There were no differences in rates of change of any AD biomarkers between any cognitive trajectory groups except for a higher rate of hippocampal atrophy in clinical converters compared to maintainers.
Interpretation
Using longitudinal data to define cognitive trajectory groups reduces education and sex bias and reveals the prognostic importance of early onset of accumulation of AD pathology. ANN NEUROL 2024
Objective
Early Alzheimer’s disease (AD) diagnosis remains challenging, necessitating specific biomarkers for timely detection. This study aimed to identify such biomarkers and explore their associations with cognitive decline.
Methods
A cohort of 1759 individuals across cognitive aging stages, including healthy controls (HC), mild cognitive impairment (MCI), and AD, was examined. Utilizing nine biomarkers from structural MRI (sMRI), diffusion tensor imaging (DTI), and positron emission tomography (PET), predictions were made for Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale Sum of Boxes (CDRSB), and Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS). Biomarkers included four sMRI (e.g., average thickness [ATH]), four DTI (e.g., mean diffusivity [MD]), and one PET Amyloid-β (Aβ) measure. Ensemble regression tree (ERT) technique with bagging and random forest approaches were applied in four groups (HC/MCI, HC/AD, MCI/AD, and HC/MCI/AD).
Results
Aβ emerged as a robust predictor of cognitive scores, particularly in late-stage AD. Volumetric measures, notably ATH, consistently correlated with cognitive scores across early and late disease stages. Additionally, ADAS demonstrated links to various neuroimaging biomarkers in all subject groups, highlighting its efficacy in monitoring brain changes throughout disease progression. ERT identified key brain regions associated with cognitive scores, such as the right transverse temporal region for Aβ, left and right entorhinal cortex, left inferior temporal gyrus, and left middle temporal gyrus for ATH, and the left uncinate fasciculus for MD.
Conclusion
This study underscores the importance of an interdisciplinary approach in understanding AD mechanisms, offering potential contributions to early biomarker development.
INTRODUCTION
Traditional brain imaging genetics studies have primarily focused on how genetic factors influence the volume of specific brain regions, often neglecting the overall complexity of brain architecture and its genetic underpinnings.
METHODS
This study analyzed data from participants across the Alzheimer’s disease (AD) continuum from the ALFA and ADNI studies. We exploited compositional data analysis to examine relative brain volumetric variations that (i) differentiate cognitively unimpaired (CU) individuals, defined as amyloid-negative (A-) based on CSF profiling, from those at different AD stages, and (ii) associated with increased genetic susceptibility to AD, assessed using polygenic risk scores.
RESULTS
Distinct brain signatures differentiated CU A-individuals from amyloid-positive MCI and AD. Moreover, disease stage-specific signatures were associated with higher genetic risk of AD.
DISCUSSION
The findings underscore the complex interplay between genetics and disease stages in shaping brain structure, which could inform targeted preventive strategies and interventions in preclinical AD.
Previous studies have reported sex differences in cortical gyrification. Since most cortical folding is principally defined in utero, sex chromosomes as well as gonadal hormones are likely to influence sex‐specific aspects of local gyrification. Classic congenital adrenal hyperplasia (CAH) causes high levels of androgens during gestation in females, whereas levels in males are largely within the typical male range. Therefore, CAH provides an opportunity to study the possible effects of prenatal androgens on cortical gyrification. Here, we examined the vertex‐wise absolute mean curvature—a common estimate for cortical gyrification—in individuals with CAH (33 women and 20 men) and pair‐wise matched controls (33 women and 20 men). There was no significant main effect of CAH and no significant CAH‐by‐sex interaction. However, there was a significant main effect of sex in five cortical regions, where gyrification was increased in women compared to men. These regions were located on the lateral surface of the brain, specifically left middle frontal (rostral and caudal), right inferior frontal, left inferior parietal, and right occipital. There was no cortical region where gyrification was increased in men compared to women. Our findings do not only confirm prior reports of increased cortical gyrification in female brains but also suggest that cortical gyrification is not significantly affected by prenatal androgen exposure. Instead, cortical gyrification might be determined by sex chromosomes either directly or indirectly—the latter potentially by affecting the underlying architecture of the cortex or the size of the intracranial cavity, which is smaller in women.
Background
Driving is the preferred mode of transportation for adults across the healthy age span. However, motor vehicle crashes are among the leading causes of injury and death, especially for older adults, and under distracted driving conditions. Understanding the neuroanatomical basis of driving may inform interventions that minimize crashes. This exploratory study examined the neuroanatomical correlates of undistracted and distracted simulated straight driving.
Methods
One-hundred-and-thirty-eight participants (40.6% female) aged 17–85 years old (mean and SD = 58.1 ± 19.9 years) performed a simulated driving task involving straight driving and turns at intersections in a city environment using a steering wheel and foot pedals. During some straight driving segments, participants responded to auditory questions to simulate distracted driving. Anatomical T1-weighted MRI was used to quantify grey matter volume and cortical thickness for five brain regions: the middle frontal gyrus (MFG), precentral gyrus (PG), superior temporal cortex (STC), posterior parietal cortex (PPC), and cerebellum. Partial correlations controlling for age and sex were used to explore relationships between neuroanatomical measures and straight driving behavior, including speed, acceleration, lane position, heading angle, and time speeding or off-center. Effects of interest were noted at an unadjusted p-value threshold of 0.05.
Results
Distracted driving was associated with changes in most measures of straight driving performance. Greater volume and cortical thickness in the PPC and cerebellum were associated with reduced variability in lane position and heading angle during distracted straight driving. Cortical thickness of the MFG, PG, PPC, and STC were associated with speed and acceleration, often in an age-dependent manner.
Conclusion
Posterior regions were correlated with lane maintenance whereas anterior and posterior regions were correlated with speed and acceleration, especially during distracted driving. The regions involved and their role in straight driving may change with age, particularly during distracted driving as observed in older adults. Further studies should investigate the relationship between distracted driving and the aging brain to inform driving interventions.
Schizophrenia has been considered to exhibit sex-related clinical differences that might be associated with distinctly abnormal brain asymmetries between sexes. One hundred and thirty-two antipsychotic-naïve first-episode patients with schizophrenia and 150 healthy participants were recruited in this study to investigate whether cortical asymmetry would exhibit sex-related abnormalities in schizophrenia. After a 1-yr follow-up, patients were rescanned to obtain the effect of antipsychotic treatment on cortical asymmetry. Male patients were found to show increased lateralization index while female patients were found to exhibit decreased lateralization index in widespread regions when compared with healthy participants of the corresponding sex. Specifically, the cortical asymmetry of male and female patients showed contrary trends in the cingulate, orbitofrontal, parietal, temporal, occipital, and insular cortices. This result suggested male patients showed a leftward shift of asymmetry while female patients showed a rightward shift of asymmetry in these above regions that related to language, vision, emotion, and cognition. Notably, abnormal lateralization indices remained stable after antipsychotic treatment. The contrary trends in asymmetry between female and male patients with schizophrenia together with the persistent abnormalities after antipsychotic treatment suggested the altered brain asymmetries in schizophrenia might be sex-related disturbances, intrinsic, and resistant to the effect of antipsychotic therapy.
Introduction
CHRFAM7A, a uniquely human fusion gene, has been associated with neuropsychiatric disorders including Alzheimer’s disease, schizophrenia, anxiety, and attention deficit disorder. Understanding the physiological function of CHRFAM7A in the human brain is the first step to uncovering its role in disease. CHRFAM7A was identified as a potent modulator of intracellular calcium and an upstream regulator of Rac1 leading to actin cytoskeleton reorganization and a switch from filopodia to lamellipodia implicating a more efficient neuronal structure. We performed a neurocognitive-MRI correlation exploratory study on 46 normal human subjects to explore the effect of CHRFAM7A on human brain.
Methods
Dual locus specific genotyping of CHRFAM7A was performed on genomic DNA to determine copy number (TaqMan assay) and orientation (capillary sequencing) of the CHRFAM7A alleles. As only the direct allele is expressed at the protein level and affects α7 nAChR function, direct allele carriers and non-carriers are compared for neuropsychological and MRI measures. Subjects underwent neuropsychological testing to measure motor (Timed 25-foot walk test, 9-hole peg test), cognitive processing speed (Symbol Digit Modalities Test), Learning and memory (California Verbal Learning Test immediate and delayed recall, Brief Visuospatial Memory Test—Revised immediate and delayed recall) and Beck Depression Inventory—Fast Screen, Fatigue Severity Scale. All subjects underwent MRI scanning on the same 3 T GE scanner using the same protocol. Global and tissue-specific volumes were determined using validated cross-sectional algorithms including FSL’s Structural Image Evaluation, using Normalization, of Atrophy (SIENAX) and FSL’s Integrated Registration and Segmentation Tool (FIRST) on lesion-inpainted images. The cognitive tests were age and years of education-adjusted using analysis of covariance (ANCOVA). Age-adjusted analysis of covariance (ANCOVA) was performed on the MRI data.
Results
CHRFAM7A direct allele carrier and non-carrier groups included 33 and 13 individuals, respectively. Demographic variables (age and years of education) were comparable. CHRFAM7A direct allele carriers demonstrated an upward shift in cognitive performance including cognitive processing speed, learning and memory, reaching statistical significance in visual immediate recall (FDR corrected p = 0.018). The shift in cognitive performance was associated with smaller whole brain volume (uncorrected p = 0.046) and lower connectivity by resting state functional MRI in the visual network (FDR corrected p = 0.027) accentuating the cognitive findings.
Conclusion
These data suggest that direct allele carriers harbor a more efficient brain consistent with the cellular biology of actin cytoskeleton and synaptic gain of function. Further larger human studies of cognitive measures correlated with MRI and functional imaging are needed to decipher the impact of CHRFAM7A on brain function.
Stroke is the leading cause of long-term disability worldwide. Incurred brain damage can disrupt cognition, often with persisting deficits in language and executive capacities. Yet, despite their clinical relevance, the commonalities and differences between language versus executive control impairments remain under-specified. To fill this gap, we tailored a Bayesian hierarchical modelling solution in a largest-of-its-kind cohort (1080 patients with stroke) to deconvolve language and executive control with respect to the stroke topology. Cognitive function was assessed with a rich neuropsychological test battery including global cognitive function (tested with the Mini-Mental State Exam), language (assessed with a picture naming task), executive speech function (tested with verbal fluency tasks), executive control functions (Trail Making Test and Digit Symbol Coding Task), visuospatial functioning (Rey Complex Figure), as well as verbal learning and memory function (Soul Verbal Learning). Bayesian modelling predicted interindividual differences in eight cognitive outcome scores three months after stroke based on specific tissue lesion topologies. A multivariate factor analysis extracted four distinct cognitive factors that distinguish left- and right-hemispheric contributions to ischaemic tissue lesions. These factors were labelled according to the neuropsychological tests that had the strongest factor loadings: One factor delineated language and general cognitive performance and was mainly associated with damage to left-hemispheric brain regions in the frontal and temporal cortex. A factor for executive control summarized mental flexibility, task switching and visual-constructional abilities. This factor was strongly related to right-hemispheric brain damage of posterior regions in the occipital cortex. The interplay of language and executive control was reflected in two distinct factors that were labelled as executive speech functions and verbal memory. Impairments on both factors were mainly linked to left-hemispheric lesions. These findings shed light onto the causal implications of hemispheric specialization for cognition; and make steps towards subgroup-specific treatment protocols after stroke.
Purpose
Magnetization transfer saturation (MT sat ) mapping is commonly used to examine the macromolecular content of brain tissue. This study compared variable flip angle (VFA) T 1 mapping against compressed‐sensing MP2RAGE (csMP2RAGE) T 1 mapping for accelerating MT sat imaging.
Methods
VFA, MP2RAGE, and csMP2RAGE were compared against inversion‐recovery T 1 in an aqueous phantom at 3 T. The same 1‐mm VFA, MP2RAGE, and csMP2RAGE protocols were acquired in 4 healthy subjects to compare T 1 and MT sat . Bloch‐McConnell simulations were used to investigate differences between the phantom and in vivo T 1 results. Ten healthy controls were imaged twice with the csMP2RAGE MT sat protocol to quantify repeatability.
Results
The MP2RAGE and csMP2RAGE protocols were 13.7% and 32.4% faster than the VFA protocol, respectively. At these scan times, all approaches provided strong repeatability and accurate T 1 times (< 5% difference) in the phantom, but T 1 accuracy was more impacted by T 2 for VFA than for MP2RAGE. In vivo, VFA estimated longer T 1 times than MP2RAGE and csMP2RAGE. Simulations suggest that the differences in the T 1 measured using VFA, MP2RAGE, and inversion recovery could be explained by the magnetization‐transfer effects. In the test–retest experiment, we found that the csMP2RAGE has a minimum detectable change of 2.3% for T 1 mapping and 7.8% for MT sat imaging.
Conclusions
We demonstrated that MP2RAGE can be used in place of VFA T 1 mapping in an MT sat protocol. Furthermore, a shorter scan time and high repeatability can be achieved using the csMP2RAGE sequence.
Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer’s disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent ¹⁸F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at < 0.1, there were no differences in global and regional SUVR between CU A+ and prodromal AD groups. Regional volume differences between the two groups were observed in the amygdala, hippocampus, entorhinal cortex, insula, parahippocampal gyrus, and inferior temporal and parietal cortices. LASSO logistic regression model showed significant associations between prodromal AD and atrophy in the entorhinal cortex, inferior parietal cortex, both amygdalae, and left hippocampus. The mean SUVR in the right superior parietal cortex (beta coefficient = 0.0172) and its interaction with the regional volume (0.0672) were also selected in the LASSO model. The mean SUVR in the right superior parietal cortex was associated with an increased likelihood of prodromal AD (Odds ratio [OR] 1.602, p = 0.014), particularly in participants with lower regional volume (OR 3.389, p < 0.001). Only regional volume differences, not amyloid deposition, were observed between CU A+ and prodromal AD. The reduced volume in the superior parietal cortex may play a significant role in the progression to prodromal AD through its interaction with amyloid deposition in that region.
This chapter reviews volumetric studies of age-related differences in the human brain observed in vivo using magnetic resonance imaging (MRI), and presents several examples of their impact on cognitive performance in healthy adults. Age-related differences in brain anatomy are regional and differential. The preponderance of the evidence indicated that prefrontal cortex is the region of the greatest age-related vulnerability. However, to date, almost all studies were conducted in a cross-sectional design, and a few longitudinal studies were limited in their duration and scope of the examined brain locations. © Roger A. Dixon, Lars Bäckman, and Lars Göran-Nilsson 2004. All rights reserved.
We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.
We propose an algorithm allowing the construction of a structural representation of the cortical topography from a T1-weighted 3D MR image. This representation is an attributed relational graph (ARG) inferred from the 3D skeleton of the object made up of the union of gray matter and cerebro-spinal fluid enclosed in the brain hull. In order to increase the robustness of the skeletonization, topological and regularization constraints are included in the segmentation process using an original method: the homotopically deformable regions. This method is halfway between deformable contour and Markovian segmentation approaches. The 3D skeleton is segmented in simple surfaces (SSs) constituting the ARG nodes (mainly cortical folds). The ARG relations are of two types: first, theSS pairs connected in the skeleton; second, theSS pairs delimiting a gyrus. The described algorithm has been developed in the frame of a project aiming at the automatic detection and recognition of the main cortical sulci. Indeed, the ARG is a synthetic representation of all the information required by the sulcus identification. This project will contribute to the development of new methodologies for human brain functional mapping and neurosurgery operation planning.
The degree of cortical folding found in adult human brains has been analyzed using a gyrification index (GI). This parameter permits the description of a mean value for the whole brain, but also a local specific analysis of different brain regions. Correlation analyses of the GI with age, body weight, body length, brain weight and volume of the prosencephalon and the cortex show no significant results. GI values do not differ significantly between male and female brains, right and left hemispheres or right and left sides of the superior temporal plane. The GI shows maximal values over the prefrontal and the parieto-temporo-occipital association cortex. A comparison between the rostro-caudal GI patterns of human brains and those of prosimians and Old World monkeys shows the largest difference over the prefrontal cortex. The mean GI increases from prosimians to human brains with the highest values for non-human primates being in the pongid group.
The goal of the study was to examine the volume of selected brain regions in a group of mildly impaired patients with Alzheimer's disease (AD). Five regions were selected for analysis, all of which have been reported to show substantial change in the majority of patients with AD at some time in the course of disease.
Case-control study with the experimenter "blinded."
Hospital-based magnetic resonance imaging center.
Fifteen subjects, eight patients with the diagnosis of probable dementia of the Alzheimer type made in concordance with National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association criteria and seven age-matched healthy control subjects.
Three of the volumetric measures were significantly different between patients with AD and controls: the hippocampus, the temporal horn of the lateral ventricles, and the temporal lobe. Two of the measures did not significantly differentiate patients with AD and controls: the amygdala and the basal forebrain. A discriminant function analysis demonstrated that a linear combination of the volumes of the hippocampus and the temporal horn of the lateral ventricles differentiated 100% of the patients and controls from one another.
The results suggest that the hippocampus and the temporal horn of the lateral ventricles may be useful as antemortem markers of AD in mildly impaired patients.
This magnetic resonance imaging (MRI)-based morphometric analysis of cortical topography in the human brain is based upon the segmentation and parcellation of volumetric T1-weighted MRI data for a set of 20 young adult brains including 10 males and 10 females. For the most part, each parcellation unit (PU) of the neocortex corresponds to a single or a portion of a single gyrus. The volumes of each PU were computed for each brain. Subsets of PUs were also grouped so as to represent the neocortex for the frontal, temporal, parietal and occipital lobes. The coefficient of variation of the mean volume of total neocortex and that of the neocortex assigned to individual lobes cluster around 10%, whereas that of neocortex assigned to the individual gyri (PU) is more than twice that value. Approximately 80% of the total variance in gyral volume arises from determinants interactive for individual and specific gyri, while only approximately 10% of the total variance appears to be a reflection of uniform scaling to total neocortical volume. Sexual dimorphism contributes a pervasive though relatively small component of this variance. These results have implications for the study of structure-function correlation, and the proper statistical methods of handling volumetric data in morphometric studies. In addition, the nature of the covariance structure of the data will lead to future hypotheses regarding the relationships between the various potential genetic and epigenetic gyral influencing factors.
A sulcal extraction and assisted labeling (SEAL) system has been developed that would automatically extract two-dimensional surface ribbons that represent the median axis of the cerebral sulci and would also neuroanatomically label these entities. Sulcal labeling is performed semi-automatically by selecting a sulcal entity in the cortical sulcal schematic topography (CSST) and selecting from a menu of candidate sulcus names. To help users in the labeling task, the menu is restricted to the most likely candidates by using priors for the expected sulcal spatial distribution.
Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.
Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. Even single voxel misclassifications can result in erroneous connections being created between adjacent banks of a sulcus, resulting in a topologically inaccurate model. These topological defects cause the cortical model to no longer be homeomorphic to a sheet, preventing the accurate inflation, flattening, or spherical morphing of the reconstructed cortex. Surface deformation techniques can guarantee the topological correctness of a model, but are time-consuming and may result in geometrically inaccurate models. In order to address this need we have developed a technique for taking a model of the cortex, detecting and fixing the topological defects while leaving that majority of the model intact, resulting in a surface that is both geometrically accurate and topologically correct.
We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This procedure incorporates both geometric information derived from the cortical model, and neuroanatomical convention, as found in the training set. The result is a complete labeling of cortical sulci and gyri. Examples are given from two different training sets generated using different neuroanatomical conventions, illustrating the flexibility of the algorithm. The technique is shown to be comparable in accuracy to manual labeling.
Clinicians and those in health sciences are frequently called upon to measure subjective states such as attitudes, feelings, quality of life, educational achievement and aptitude, and learning style in their patients. This fifth edition of Health Measurement Scales enables these groups to both develop scales to measure non-tangible health outcomes, and better evaluate and differentiate between existing tools.
Health Measurement Scales is the ultimate guide to developing and validating measurement scales that are to be used in the health sciences. The book covers how the individual items are developed; various biases that can affect responses (e.g. social desirability, yea-saying, framing); various response options; how to select the best items in the set; how to combine them into a scale; and finally how to determine the reliability and validity of the scale. It concludes with a discussion of ethical issues that may be encountered, and guidelines for reporting the results of the scale development process. Appendices include a comprehensive guide to finding existing scales, and a brief introduction to exploratory and confirmatory factor analysis, making this book a must-read for any practitioner dealing with this kind of data.
We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.
We describe a system of parcellation of the human brain that is based on the functional anatomy of the cerebral cortex and that is applied to the analysis of magnetic resonance images. This system is designed to support investigations of hemispheric asymmetries and quantitative lesion localization studies in cognitive neuroscience. The system of cortical subdivision is a neural systems oriented model that approximates subdivisions supported by previous architectonic and functional analyses. It is based primarily on boundaries determined by "limiting fissures." It is completed by a set of coronal planes, keyed to visible anatomic landmarks, which "close" the borders of the parcellation subdivisions. The method depends on computational reconstruction of the primary image data in multiple planes so as to allow the observed pattern of limiting fissures in a given brain to be digitized. In this presentation, the method is applied in order to define the surface anatomy of the cerebral hemispheres in a normal subject. Volumetric measurements of individual cortical regions are compared as hemispheric percentiles to areal perceniiles derived from the analysis of Jouandet et al. (1989), a conceptually related though methodologically different approach. We specifically address the approach to the study of interhemispheric differences and interindividual variations in cortical anatomy.
The human cortical surface is a highly complex, folded structure. Sulci, the spaces between the folds, define location on the cortex and provide a parcella- tion into anatomically distinct areas. A topic that has recently received increased attention is the segmenta- tion of these sulci from magnetic resonance images, with most work focusing on extracting either the sul- cal spaces between the folds or curve representations of sulci. Unlike these methods, we propose a technique that extracts actual regions of the cortical surface that surround sulci, which we call "sulcal regions." The method is based on a watershed algorithm applied to a geodesic depth measure on the cortical surface. A well-known problem with the watershed algorithm is a tendency toward oversegmentation, meaning that a single region is segmented as several pieces. To ad- dress this problem, we propose a postprocessing algo- rithm that merges appropriate segments from the wa- tershed algorithm. The sulcal regions are then manually labeled by simply selecting the appropriate regions with a mouse click and a preliminary study of sulcal depth is reported. Finally, a scheme is pre- sented for computing a complete parcellation of the cortical surface. © 2002 Elsevier Science
Rates of hippocampal volume loss have been shown to distinguish subjects with dementia of the Alzheimer type (DAT) from nondemented controls. In this study, we obtained magnetic resonance scans in 18 subjects with very mild DAT (CDR 0.5) and 26 age-matched nondemented controls (CDR 0) 2 years apart. Large-deformation high-dimensional brain mapping was used to quantify and compare changes in hippocampal shape as well as volume in the two groups of subjects. Hippocampal volume loss over time was significantly greater in the CDR 0.5 subjects (left = 8.3%, right = 10.2%) than in the CDR 0 subjects (left = 4.0%, right = 5.5%) (ANOVA, F = 7.81, P = 0.0078). We used singular-value decomposition and logistic regression models to quantify hippocampal shape change across time within individuals, and this shape change in the CDR 0.5 and CDR 0 subjects was found to be significantly different (Wilks's lambda, P = 0.014). Further, at baseline, CDR 0.5 subjects, in comparison to CDR 0 subjects, showed inward deformation over 38% of the hippocampal surface; after 2 years this difference grew to 47%. Also, within the CDR 0 subjects, shape change between baseline and follow-up was largely confined to the head of the hippocampus and subiculum, while in the CDR 0.5 subjects, shape change involved the lateral body of the hippocampus as well as the head region and subiculum. These results suggest that different patterns of hippocampal shape change in time as well as different rates of hippocampal volume loss distinguish very mild DAT from healthy aging.
The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the cortical surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the cortical surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D cortical sheet. In order to address the need for a more natural surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain.
Objectives: Prostate cancer accounted for more than 180,000 new cases and almost 40,000 deaths in the United States in 1998. Higher rates of mortality have been noted among racial minorities and lower socioeconomic status groups, primarily because of advanced stage of cancer at presentation. Understanding barriers toward early detection of prostate cancer may help diminish these variations.
Materials and Methods: Thirty-two focus group sessions with individuals of lower socioeconomic status addressed attitudes toward physical examinations, prostate cancer, and sources of health-care information. Barriers to early detection were identified, based on transcripts that were analyzed using the Health Belief Model.
Results: Most men of lower socioeconomic status viewed physical examinations negatively, with barriers including time, monetary costs, negative impressions of the prostate examination, and lack of belief in early detection. Among the minority of men who had had prostate examinations, they typically did so as part of examinations for chronic medical conditions or because of employer requirements for routine check-ups. The rectal examination was viewed very negatively because of concerns of physical pain, social embarrassment, and uncertain value. Fear and fatalism regarding prostate cancer were expressed by the majority of attendees. With respect to sources of health information, men typically received health-care information from the media, with television being the most common source. No significant differences in barriers to early detection efforts were observed between focus groups composed of white versus African American poor men.
Conclusions: The Health Belief Model provides a framework for evaluating the perceptions of men of lower socioeconomic status toward the early detection of prostate cancer. Negative perceptions regarding physical examinations and skepticism about the value of early detection were major barriers to the early detection of prostate cancer. Targeted strategies directed at each of these barriers are needed to improve the rates of early detection in men of lower socioeconomic status.
A methodology was developed for dividing prefrontal cortical gray matter into insular, orbital, inferior, middle, superior, cingulate, and frontal pole regions using anatomical criteria. This methodology was developed as a follow-up to one that measured whole prefrontal gray and white matter volumes in schizophrenic and control subjects. This study showed no overall volume differences in prefrontal cortex between schizophrenic and control subjects. The parcellation of prefrontal cortex was done to increase the probability of detecting abnormalities that were circumscribed to a particular portion of the region. A 1.5 Tesla magnet was used to acquire contiguous 1.5-mm coronal slices of the entire brain. Volumes were then measured in a group of right-handed male (n = 15) subjects. Gray matter was parcellated using criteria that were mainly based on gross anatomy, as visualized in 3-dimensional renderings of the brain. Reliability of the parcellation scheme was very high (ri = 0.80 and above). This methodology should be useful in the study of cortical pathology in a number of neurological disorders, including schizophrenia.
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
The authors used magnetic resonance imaging and an image analysis technique known as cortical pattern matching to map cortical gray matter deficits in elderly depressed patients with an illness onset after age 60 (late-onset depression).
Seventeen patients with late-onset depression (11 women and six men; mean age=75.24, SD=8.52) and 17 group-matched comparison subjects (11 women and six men; mean age=73.88, SD=7.61) were included. Detailed spatial analyses of gray matter were conducted across the entire cortex by measuring local proportions of gray matter at thousands of homologous cortical surface locations in each subject, and these patterns were matched across subjects by using elastic transformations to align sulcal topography. To visualize regional changes, statistical differences were mapped at each cortical surface location in three dimensions.
The late-onset depression group exhibited significant gray matter deficits in the right lateral temporal cortex and the right parietal cortex, where decreases were most pronounced in sensorimotor regions. The statistical maps also showed gray matter deficits in the same regions of the left hemisphere that approached significance after permutation testing. No significant group differences were detected in frontal cortices or any other anatomical region.
Regionally specific decreases of gray matter occur in late-onset depression, supporting the hypothesis that this subset of elderly patients with major depression presents with certain unique neuroanatomical abnormalities that may differ from patients with an earlier onset of illness.
To measure prefrontal cortical volume in a group of schizophrenic subjects who presented with mainly positive symptoms and who were previously shown to have volume reductions in left temporal lobe structures.
Fourteen men with chronic schizophrenia and 15 male control subjects were matched for age, IQ, handedness, and parental socioeconomic status. Magnetic resonance images were obtained by means of a 1.5-T magnet, and contiguous 1.5-mm slices of the entire brain were obtained.
No significant differences were found between schizophrenic and control subjects in mean values for prefrontal white or gray matter on either the right or the left side. However, within the schizophrenic group, there was evidence of a relationship between the volumes of left prefrontal gray matter and left temporal lobe structures that was not present in the control group.
At least in this group of schizophrenic subjects with mainly positive symptoms, temporal lobe abnormalities can exist in conjunction with no gross volumetric abnormalities of the prefrontal cortex.
The process of producing magnetic resonance (MR) volume measurements can be divided into considerations of acquisition and postprocessing of the MR data. With careful attention to both of these, precise and reproducible measurements can be achieved. A statistical description of hippocampal measurements in normal volunteers must be available for comparison if volumetrics are employed either for clinical or research purposes. A wide range in "normal" hippocampal volume is present in the studies of normal young adults that have been reported to date. This variability is most probably due to interinstitutional differences in hippocampal boundary criteria, and in the software employed for counting pixels in a defined region of interest (ROI). Because the numeric output from the volume measurement procedure is highly technique-dependent, the statistical description of "normal" should be determined or calibrated at each institution wishing to use these techniques.
We describe a computerized method to automatically find and label the cortical surface in three-dimensional (3-D) magnetic resonance (MR) brain images. The approach we take is to model a prelabeled brain atlas as a physical object and give it elastic properties, allowing it to warp itself onto regions in a preprocessed image. Preprocessing consists of boundary-finding and a morphological procedure which automatically extracts the brain and sulci from an MR image and provides a smoothed representation of the brain surface to which the deformable model can rapidly converge. Our deformable models are energy-minimizing elastic surfaces that can accurately locate image features. The models are parameterized with 3-D bicubic B-spline surfaces. We design the energy function such that cortical fissure (sulci) points on the model are attracted to fissure points on the image and the remaining model points are attracted to the brain surface. A conjugate gradient method minimizes the energy function, allowing the model to automatically converge to the smoothed brain surface. Finally, labels are propagated from the deformed atlas onto the high-resolution brain surface.
Magnetic resonance imaging (MRI)-based volumetric measurements of medial temporal lobe (MTL) structures can discriminate between normal elderly control subjects and patients with Alzheimer's disease (AD) of moderate to advanced severity. In terms of clinical utility, however, a more important issue concerns the ability of the technique to differentiate between normal elderly control subjects and AD patients with the very mildest form of the disease. We performed MRI-based volumetric measurements of the hippocampus, parahippocampal gyrus, and amygdala in 126 cognitively normal elderly control subjects and 94 patients with probable AD. The diagnosis of AD was made according to NINDS/ADRDA criteria, and disease severity was categorized by Clinical Dementia Rating (CDR) scores. Patients with CDR 0.5 were classified as very mild, CDR 1 as mild, and CDR 2 as moderate disease severity. Volumes of each structure declined with increasing age in control subjects and did so in parallel for men and women. The volume of each measured MTL structure also declined with age in patients with AD. The volume of each MTL structure was significantly smaller in AD patients than control subjects (p < 0.001). Of the several MTL measures, the total hippocampal volumetric measurements were best at discriminating control subjects from AD patients. The mean hippocampal volumes for AD patients relative to control subjects by severity of disease were as follows: very mild AD (CDR 0.5) -1.75 SD below the control mean, mild AD (CDR 1) -1.99 SD, and moderate AD (CDR 2) -2.22 SD. Age- and gender-adjusted, normalized MRI-based hippocampal volumetric measurements provide a sensitive marker of the MTL neuroanatomic degeneration in AD early in the disease process.
This paper describes the design, implementation, and results of a technique for creating a three-dimensional (3D) probabilistic surface atlas of the human brain. We have developed, implemented, and tested a new 3D statistical method for assessing structural variations in a database of anatomic images. The algorithm enables the internal surface anatomy of new subjects to be analyzed at an extremely local level. The goal was to quantify subtle and distributed patterns of deviation from normal anatomy by automatically generating detailed probability maps of the anatomy of new subjects. Connected systems of parametric meshes were used to model the internal course of the following structures in both hemispheres: the parieto-occipital sulcus, the anterior and posterior rami of the calcarine sulcus, the cingulate and marginal sulci, and the supracallosal sulcus. These sulci penetrate sufficiently deeply into the brain to introduce an obvious topological decomposition of its volume architecture. A family of surface maps was constructed, encoding statistical properties of local anatomical variation within individual sulci. A probability space of random transformations, based on the theory of Gaussian random fields, was developed to reflect the observed variability in stereotaxic space of the connected system of anatomic surfaces. A complete system of probability density functions was computed, yielding confidence limits on surface variation. The ultimate goal of brain mapping is to provide a framework for integrating functional and anatomical data across many subjects and modalities. This task requires precise quantitative knowledge of the variations in geometry and location of intracerebral structures and critical functional interfaces. The surface mapping and probabilistic tec...
We assess the value of magnetic resonance (MR) image texture in Alzheimer's disease (AD) both as a diagnostic marker and as a measure of progression. T1-weighted MR scans were acquired from 40 normal controls and 24 AD patients. These were split into a training set (20 controls, 10 AD) and a test set (20 controls, 14 AD). In addition, five control subjects and five AD patients were scanned repeatedly over several years. On each scan a texture feature vector was evaluated over the brain; this consisted of 260 measures derived from the spatial gray-level dependence method. A stepwise discriminant analysis was applied to the training set, to obtain a linear discriminant function. In the test set, this function yielded significantly different values for the control and AD groups (p < 10(-4)) with only small group overlap; a classification rate of 91% was obtained. For the repeatedly scanned control subjects, the median increment in the discriminant function between successive scans of 0.12 was not significantly different from zero (p > 0.05); for the repeatedly scanned AD patients the corresponding median increment of 1.4 was significantly different from zero (p < 0.05). MR image texture may be a useful aid in the diagnosis and tracking of Alzheimer's disease.
Serial 3D MR scan pairs (1 year intervals) from nine control subjects and nine Alzheimer disease (AD) patients were matched using an accurate fast fluid algorithm. Subtraction of matched scans indicated near complete cancellation of brain structure; mean cross-correlation coefficients were 0.946 (controls) and 0.933 (AD). In the AD group, regions of expansion and contraction in the deformation field were consistent with the known patterns of tissue loss and expansion of CSF spaces. Fluid registration may permit better localization of structural change.
The surface of the human cerebral cortex is a highly folded sheet with the majority of its surface area buried within folds. As such, it is a difficult domain for computational as well as visualization purposes. We have therefore designed a set of procedures for modifying the representation of the cortical surface to (i) inflate it so that activity buried inside sulci may be visualized, (ii) cut and flatten an entire hemisphere, and (iii) transform a hemisphere into a simple parameterizable surface such as a sphere for the purpose of establishing a surface-based coordinate system.
Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging.
This paper describes automatic procedures for extracting sulcal and gyral patterns from magnetic resonance (MR) images of the human brain. Specifically, we present three algorithms for the extraction of gyri, sulci, and sulcal fundi. These algorithms yield highly condensed line representations which can be used to describe the individual properties of the neocortical surface. The algorithms consist of a sequence of image analysis steps applied directly to the volumetric image data without requiring intermediate data representations such as surfaces or three-dimensional renderings. Previous studies have mostly focused on the extraction of surface representations, rather than line representations of cortical structures. We believe that line representations provide a valuable alternative to surface representations.
The orbitofrontal cortex has been examined in Alzheimer's disease (AD) from the viewpoint of neurofibrillary tangle (NFT) pathology, its laminar distribution and topography. NFT pathology in the orbitofrontal cortex is extensive in AD. In cases with extensive cortical pathology, NFTs extend from the pole of the frontal lobe to the orbitoinsular junction. In lesser affected cases, the anterior granular part of the orbital cortex is less invested by NFTs. Layers III and V contain the greatest density of NFTs and these are most dense in the dysgranular areas, posterior to the transverse orbital sulcus. Posterior and medial orbitofrontal areas, forming area 13 and the posterior tip of the paraolfactory gyrus, are the most severely damaged, as are the smaller agranular fields that surround the olfactory tract and cortex. The widespread orbitofrontal damage in AD affecting projection neurons suggests that this pathology may contribute heavily to the many non-memory-related behavior changes observed in this disorder.
We used magnetic resonance imaging (MRI) measurements to determine whether persons in the prodromal phase of Alzheimer's disease (AD) could be accurately identified before they developed clinically diagnosed dementia. Normal subjects (n = 24) and those with mild memory difficulty (n = 79) received an MRI scan at baseline and were then followed annually for 3 years to determine which individuals subsequently met clinical criteria for AD. Patients with mild AD at baseline were also evaluated (n = 16). Nineteen of the 79 subjects with mild memory difficulty "converted" to a diagnosis of probable AD after 3 years of follow-up. Baseline MRI measures of the entorhinal cortex, the banks of the superior temporal sulcus, and the anterior cingulate were most useful in discriminating the status of the subjects on follow-up examination. The accuracy of discrimination was related to the clinical similarity between groups. One hundred percent (100%) of normal subjects and patients with mild AD could be discriminated from one another based on these MRI measures. When the normals were compared with the individuals with memory impairments who ultimately developed AD (the converters), the accuracy of discrimination was 93%, based on the MRI measures at baseline (sensitivity = 0.95; specificity = 0.90). The discrimination of the normal subjects and the individuals with mild memory problems who did not progress to the point where they met clinical criteria for probable AD over the 3 years of follow-up (the "questionables") was 85% and the discrimination of the questionables and converters was 75%. The apolipoprotein E genotype did not improve the accuracy of discrimination. The specific regions selected for each of these discriminations provides information concerning the hierarchical fashion in which the pathology of AD may affect the brain during its prodromal phase.
To determine the feasibility of using high-dimensional brain mapping (HDBM) to assess the structure of the hippocampus in older human subjects, and to compare measurements of hippocampal volume and shape in subjects with early dementia of the Alzheimer type (DAT) and in healthy elderly and younger control subjects.
HDBM represents the typical structures of the brain via the construction of templates and addresses their variability by probabilistic transformations applied to the templates. Local application of the transformations throughout the brain (i.e., high dimensionality) makes HDBM especially valuable for defining subtle deformities in brain structures such as the hippocampus.
MR scans were obtained in 18 subjects with very mild DAT, 18 healthy elderly subjects, and 15 healthy younger subjects. HDBM was used to obtain estimates of left and right hippocampal volume and eigenvectors that represented the principal dimensions of hippocampal shape differences among the subject groups.
Hippocampal volume loss and shape deformities observed in subjects with DAT distinguished them from both elderly and younger control subjects. The pattern of hippocampal deformities in subjects with DAT was largely symmetric and suggested damage to the CA1 hippocampal subfield. Hippocampal shape changes were also observed in healthy elderly subjects, which distinguished them from healthy younger subjects. These shape changes occurred in a pattern distinct from the pattern seen in DAT and were not associated with substantial volume loss.
Assessments of hippocampal volume and shape derived from HDBM may be useful in distinguishing early DAT from healthy aging.
We report the first detailed population-based maps of cortical gray matter loss in Alzheimer's disease (AD), revealing prominent features of early structural change. New computational approaches were used to: (i) distinguish variations in gray matter distribution from variations in gyral patterns; (ii) encode these variations in a brain atlas (n = 46); (iii) create detailed maps localizing gray matter differences across groups. High resolution 3D magnetic resonance imaging (MRI) volumes were acquired from 26 subjects with mild to moderate AD (age 75.8+/-1.7 years, MMSE score 20.0+/-0.9) and 20 normal elderly controls (72.4+/-1.3 years) matched for age, sex, handedness and educational level. Image data were aligned into a standardized coordinate space specifically developed for an elderly population. Eighty-four anatomical models per brain, based on parametric surface meshes, were created for all 46 subjects. Structures modeled included: cortical surfaces, all major superficial and deep cortical sulci, callosal and hippocampal surfaces, 14 ventricular regions and 36 gyral boundaries. An elastic warping approach, driven by anatomical features, was then used to measure gyral pattern variations. Measures of gray matter distribution were made in corresponding regions of cortex across all 46 subjects. Statistical variations in cortical patterning, asymmetry, gray matter distribution and average gray matter loss were then encoded locally across the cortex. Maps of group differences were generated. Average maps revealed complex profiles of gray matter loss in disease. Greatest deficits (20-30% loss, P<0.001-0.0001) were mapped in the temporo-parietal cortices. The sensorimotor and occipital cortices were comparatively spared (0-5% loss, P>0.05). Gray matter loss was greater in the left hemisphere, with different patterns in the heteromodal and idiotypic cortex. Gyral pattern variability also differed in cortical regions appearing at different embryonic phases. 3D mapping revealed profiles of structural deficits consistent with the cognitive, metabolic and histological changes in early AD. These deficits can therefore be (i) charted in a living population and (ii) compared across individuals and groups, facilitating longitudinal, genetic and interventional studies of dementia.
Remarkable developments in magnetic resonance imaging (MRI) technology provide a broad range of potential applications to explore in vivo morphological characteristics of the human cerebral cortex. MR-based parcellation methods of the cerebral cortex may clarify the structural anomalies in specific brain subregions that reflect underlying neuropathological processes in brain illnesses. The present study describes detailed guidelines for the parcellation of the cerebral cortex into 41 subregions. Our method conserves the topographic uniqueness of individual brains and is based on our ability to visualize the three orthogonal planes, the triangulated gray matter isosurface and the three-dimensional (3D) rendered brain simultaneously. Based upon topographic landmarks of individual sulci, every subregion was manually segmented on a set of serial coronal or transaxial slices consecutively. The reliability study indicated that the cerebral cortex could be parcelled reliably; intraclass correlation coefficients for each subregion ranged from 0.60 to 0.99. The validity of the method is supported by the fact that gyral subdivisions are similar to regions delineated in functional imaging studies conducted in our center. Ultimately, this method will permit us to detect subtle morphometric impairments or to find abnormal patterns of functional activation in circumscribed cortical subregions. The description of a thorough map of regional structural and functional cortical abnormalities will provide further insight into the role that different subregions play in the pathophysiology of brain illnesses.
Early diagnosis and monitoring of the progression of Alzheimer's disease is important for the development of therapeutic strategies. To detect the earliest structural brain changes, individuals need to be studied before symptom onset. We used an imaging technique known as voxel-compression mapping to localise progressive atrophy in patients with preclinical Alzheimer's disease.
Four symptom-free individuals from families with early-onset Alzheimer's disease with known autosomal dominant mutations underwent serial magnetic resonance imaging (MRI) over 5-8 years. All four became symptomatic during follow-up. 20 individuals with a clinical diagnosis of probable Alzheimer's disease and 20 control participants also underwent serial MR imaging. A non-linear fluid matching algorithm was applied to register repeat scans onto baseline imaging. Jacobian determinants were used to create the voxel-compression maps.
Progressive atrophy was revealed in presymptomatic individuals, with posterior cingulate and neocortical temporoparietal cortical losses, and medial temporal-lobe atrophy. In patients with known Alzheimer's disease, atrophy was widespread apart from in the primary motor and sensory cortices and cerebellum, reflecting the clinical phenomenology.
Voxel-compression maps confirmed early involvement of the medial temporal lobes, but also showed posterior cingulate and temporoparietal cortical losses at presymptomatic stage. This technique could be applied diagnostically and used to monitor the effects of therapeutic intervention.
A new protocol for measuring the volume of the entorhinal cortex (EC) from magnetic resonance images (MRI) was developed specifically to measure the EC from oblique coronal sections used in hippocampal volumetric studies. The relative positions of the anatomic landmarks demarcating EC boundaries were transposed from standard coronal sections to oblique ones. The lateral EC border, which is the most controversial among anatomists, was defined in a standard and conservative manner at the medial edge of the collateral sulcus. Two raters measured the EC twice for 78 subjects (healthy aged individuals, very mild AD patients, and elderly patients who did not meet criteria for dementia) to study intra- and inter-rater reproducibility and reliability of measurements. The level of accuracy achieved (coefficients of reproducibility of 1.40-3.86%) and reliability of measurements (intraclass correlation coefficients of 0.959-0.997) indicated that this method provides a feasible tool for measuring the volume of the EC in vivo.
An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.