Maturational Changes in Human Dorsal and Ventral Visual Networks.

Developmental neuroimaging studies report the emergence of increasingly diverse cognitive functions as closely entangled with a rise-fall modulation of cortical thickness (CTh), structural cortical and white-matter connectivity, and a time-course for the experience-dependent selective elimination of the overproduced synapses. We examine which of two visual processing networks, the dorsal (DVN; prefrontal, parietal nodes) or ventral (VVN; frontal-temporal, fusiform nodes) matures first, thus leading the neuro-cognitive developmental trajectory. Three age-dependent measures are reported: (i) the CTh at network nodes; (ii) the matrix of intra-network structural connectivity (edges); and (iii) the proficiency in network-related neuropsychological tests. Typically developing children (age ~6 years), adolescents (~11 years), and adults (~21 years) were tested using multiple-acquisition structural T1-weighted magnetic resonance imaging (MRI) and neuropsychology. MRI images reconstructed into a gray/white/pial matter boundary model were used for CTh evaluation. No significant group differences in CTh and in the matrix of edges were found for DVN (except for the left prefrontal), but a significantly thicker cortex in children for VVN with reduced prefrontal ventral-fusiform connectivity and with an abundance of connections in adolescents. The higher performance in children on tests related to DVN corroborates the age-dependent MRI structural connectivity findings. The current findings are consistent with an earlier maturational course of DVN.

Segmental brain volumes and cognitive and perceptual correlates in 15-year-old adolescents with low birth weight.

OBJECTIVE: To determine whether preterm very low birth weight (VLBW) or term born small for gestational age (SGA) adolescents have reduced regional brain volumes. We also asked which perinatal factors are related to reduced brain volume in VLBW adolescents, which regional brain volumes are associated with cognitive and perceptual functioning, and if these differ between the groups. STUDY DESIGN: Fifty adolescent preterm VLBW (< or =1500 g) births and 49 term SGA births (birth weight <10th percentile) were compared with 57 normal-weight term births. An automated MRI segmentation technique was used. Cognitive and perceptual functions were evaluated by WISC-III and Visual Motor Integration (VMI) tests. RESULTS: The VLBW group had reduced volumes for thalamus and cerebellar white matter (P < .002). The SGA group had smaller total brains, and proportionally smaller regional brain volumes. Cerebellar white matter in the VLBW, hippocampus in the SGA, and cerebral cortical in the control group were volumes that significantly predicted cognitive and perceptual functions. CONCLUSIONS: We speculate that white matter injury may explain the impaired cognitive and perceptual functioning in the prematurely born, whereas hippocampal injury may be related to cognitive dysfunction in term SGA adolescents.

Collaborative computational anatomy: an MRI morphometry study of the human brain via diffeomorphic metric mapping.

This article describes a large multi-institutional analysis of the shape and structure of the human hippocampus in the aging brain as measured via MRI. The study was conducted on a population of 101 subjects including nondemented control subjects (n = 57) and subjects clinically diagnosed with Alzheimer's Disease (AD, n = 38) or semantic dementia (n = 6) with imaging data collected at Washington University in St. Louis, hippocampal structure annotated at the Massachusetts General Hospital, and anatomical shapes embedded into a metric shape space using large deformation diffeomorphic metric mapping (LDDMM) at the Johns Hopkins University. A global classifier was constructed for discriminating cohorts of nondemented and demented subjects based on linear discriminant analysis of dimensions derived from metric distances between anatomical shapes, demonstrating class conditional structure differences measured via LDDMM metric shape (P < 0.01). Localized analysis of the control and AD subjects only on the coordinates of the population template demonstrates shape changes in the subiculum and the CA1 subfield in AD (P < 0.05). Such large scale collaborative analysis of anatomical shapes has the potential to enhance the understanding of neurodevelopmental and neuropsychiatric disorders.

Cortical folding patterns and predicting cytoarchitecture.

The human cerebral cortex is made up of a mosaic of structural areas, frequently referred to as Brodmann areas (BAs). Despite the widespread use of cortical folding patterns to perform ad hoc estimations of the locations of the BAs, little is understood regarding 1) how variable the position of a given BA is with respect to the folds, 2) whether the location of some BAs is more variable than others, and 3) whether the variability is related to the level of a BA in a putative cortical hierarchy. We use whole-brain histology of 10 postmortem human brains and surface-based analysis to test how well the folds predict the locations of the BAs. We show that higher order cortical areas exhibit more variability than primary and secondary areas and that the folds are much better predictors of the BAs than had been previously thought. These results further highlight the significance of cortical folding patterns and suggest a common mechanism for the development of the folds and the cytoarchitectonic fields.

Abnormal cortical folding patterns within Broca's area in schizophrenia: evidence from structural MRI.

We compared cortical folding patterns between patients with schizophrenia and demographically-matched healthy controls in prefrontal and temporal regions of interest. Using the Freesurfer (http://surfer.nmr.mgh.harvard.edu) cortical surface-based reconstruction methodology, we indirectly ascertained cortical displacement and convolution, together, by measuring the degree of metric distortion required to optimally register cortical folding patterns to an average template. An area within the pars triangularis of the left inferior frontal gyrus (Broca's area) showed significantly reduced metric distortion in the patient group relative to the control group (p=0.0352). We discuss these findings in relation to the neurodevelopmental hypothesis and language dysfunction in schizophrenia.

Cortical surface shape analysis based on spherical wavelets.

In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns.

A technique for the deidentification of structural brain MR images.

Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable. A program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume (Fischl et al. [2002]: Neuron 33:341-355; Fischl et al. [2004]: Neuroimage 23 (Suppl 1):S69-S84). A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses (depressed, Alzheimer's, and elderly and young control groups). Visual inspection showed none had brain tissue removed. In a detailed analysis of the impact of defacing on skull-stripping, 16 datasets were bias corrected with N3 (Sled et al. [1998]: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. [2004]: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy [1997]: IEEE Trans Med Imaging 16:41-54; Shattuck et al. [2001]: Neuroimage 13:856-876); defacing did not appreciably influence the outcome of skull-stripping. Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull-stripping was improved. Analyses support this algorithm as a viable method to allow data sharing with minimal data alteration within large-scale multisite projects.

Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer.

In vivo MRI-derived measurements of human cerebral cortex thickness are providing novel insights into normal and abnormal neuroanatomy, but little is known about their reliability. We investigated how the reliability of cortical thickness measurements is affected by MRI instrument-related factors, including scanner field strength, manufacturer, upgrade and pulse sequence. Several data processing factors were also studied. Two test-retest data sets were analyzed: 1) 15 healthy older subjects scanned four times at 2-week intervals on three scanners; 2) 5 subjects scanned before and after a major scanner upgrade. Within-scanner variability of global cortical thickness measurements was <0.03 mm, and the point-wise standard deviation of measurement error was approximately 0.12 mm. Variability was 0.15 mm and 0.17 mm in average, respectively, for cross-scanner (Siemens/GE) and cross-field strength (1.5 T/3 T) comparisons. Scanner upgrade did not increase variability nor introduce bias. Measurements across field strength, however, were slightly biased (thicker at 3 T). The number of (single vs. multiple averaged) acquisitions had a negligible effect on reliability, but the use of a different pulse sequence had a larger impact, as did different parameters employed in data processing. Sample size estimates indicate that regional cortical thickness difference of 0.2 mm between two different groups could be identified with as few as 7 subjects per group, and a difference of 0.1 mm could be detected with 26 subjects per group. These results demonstrate that MRI-derived cortical thickness measures are highly reliable when MRI instrument and data processing factors are controlled but that it is important to consider these factors in the design of multi-site or longitudinal studies, such as clinical drug trials.

A magnetic resonance imaging study of cortical thickness in animal phobia.

BACKGROUND: Despite the high prevalence of specific phobia (SP), its neural substrates remain undetermined. Although an initial series of functional neuroimaging studies have implicated paralimbic and sensory cortical regions in the pathophysiology of SP, to date contemporary morphometric neuroimaging methods have not been applied to test specific hypotheses regarding structural abnormalities. METHODS: Morphometric magnetic resonance imaging (MRI) methods were used to measure regional cortical thickness in 10 subjects with SP (animal type) and 20 healthy comparison (HC) subjects. RESULTS: Consistent with a priori hypotheses, between-group differences in cortical thickness were found within paralimbic and sensory cortical regions. Specifically, in comparison with the HC group, the SP group exhibited increased cortical thickness in bilateral insular, bilateral pregenual anterior cingulate, and bilateral posterior cingulate cortex as well as left visual cortical regions. CONCLUSIONS: Taken together, these structural findings parallel results from initial functional imaging studies that implicate paralimbic and sensory cortical regions in the mediating anatomy of SP symptoms. Further research will be necessary to replicate these findings and to determine their specificity as well as their pathophysiologic significance.

Thinning of the cerebral cortex in aging.

The thickness of the cerebral cortex was measured in 106 non-demented participants ranging in age from 18 to 93 years. For each participant, multiple acquisitions of structural T1-weighted magnetic resonance imaging (MRI) scans were averaged to yield high-resolution, high-contrast data sets. Cortical thickness was estimated as the distance between the gray/white boundary and the outer cortical surface, resulting in a continuous estimate across the cortical mantle. Global thinning was apparent by middle age. Men and women showed a similar degree of global thinning, and did not differ in mean thickness in the younger or older groups. Age-associated differences were widespread but demonstrated a patchwork of regional atrophy and sparing. Examination of subsets of the data from independent samples produced highly similar age-associated patterns of atrophy, suggesting that the specific anatomic patterns within the maps were reliable. Certain results, including prominent atrophy of prefrontal cortex and relative sparing of temporal and parahippocampal cortex, converged with previous findings. Other results were unexpected, such as the finding of prominent atrophy in frontal cortex near primary motor cortex and calcarine cortex near primary visual cortex. These findings demonstrate that cortical thinning occurs by middle age and spans widespread cortical regions that include primary as well as association cortex.

Automatically parcellating the human cerebral cortex.

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.

Focal thinning of the cerebral cortex in multiple sclerosis.

Brain atrophy as determined by quantitative MRI can be used to characterize disease progression in multiple sclerosis. Many studies have addressed white matter (WM) alterations leading to atrophy, while changes of the cerebral cortex have been studied to a lesser extent. In vivo, the cerebral cortex has been difficult to study due to its complex structure and regional variability. Measurement of cerebral cortex thickness at different disease stages may provide new insights into grey matter (GM) pathology. In the present investigation, we evaluated in vivo cortical thickness and its relationship to disability, disease duration, WM T2 hyper-intense and T1 hypo-intense lesion volumes. High-resolution MRI brain scans were obtained in 20 patients with clinically definite multiple sclerosis and 15 age-matched normal subjects. A novel method of automated surface reconstruction yielded measurements of the cortical thickness for each subject's entire brain and computed cross-subject statistics based on the cortical anatomy. Statistical thickness difference maps were generated by performing t-tests between patient and control groups and individual thickness measures were submitted to analyses of variance to investigate the relationship between cortical thickness and clinical variables. The mean overall thickness of the cortical ribbon was reduced in multiple sclerosis patients compared with controls [2.30 mm (SD 0.14) versus 2.48 mm (SD 0.11)], showing a significant main effect of group (controls versus patients). In patients, we found significant main effects for disability, disease duration, T2 and T1 lesion volumes. The visualization of statistical difference maps of the cortical GM thickness on inflated brains across the cortical surface revealed a distinct distribution of significant focal thinning of the cerebral cortex in addition to the diffuse cortical atrophy. Focal cortical thinning in frontal [2.37 mm (SD 0.17) versus 2.73 mm (SD 0.25)] and in temporal [2.65 mm (SD 0.15) versus 2.95 mm (SD 0.11)] brain regions was observed, even early in the course of the disease or in patients with mild disability. Patients with longstanding disease or severe disability, however, presented additionally with focal thinning of the motor cortex area [2.35 mm (SD 0.19) versus 2.74 mm (SD 0.15)]. We conclude that in vivo measurement of cortical thickness is feasible in patients suffering from multiple sclerosis. The data provide new insight into the cortical pathology in multiple sclerosis patients, revealing focal cortical thinning beside an overall reduction of the cortical thickness with disease progression.

Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

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.