Biomedical subjects

H J Bockholt

Publications and source records attributed to H J Bockholt.

5 recordsLinked to original sources

PubMed2000

Cerebral cortex: a topographic segmentation method using magnetic resonance imaging.

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.

Cerebral Cortex↗

PubMed2000

Insular cortex abnormalities in schizophrenia: a structural magnetic resonance imaging study of first-episode patients.

The insular cortex is a limbic integration region that is engaged in emotional and cognitive functions. To investigate possible insular cortex abnormalities in schizophrenia, we measured insular gray matter volume and cortical surface size in drug-naive first-episode patients. Magnetic resonance images were used to explore the morphology of the insular cortex of 25 healthy male volunteers, and 25 male schizophrenic patients. Groups were matched for age, sex, height, and parental socio-economic status. Clinical dimension scores were correlated with insular gray matter volume and cortical surface area. Patients had a significant reduction in cortical surface area [patients=2020 (206); controls=2142 (204); F=5.83, df=1, 47; P=0.01] and gray matter volume [patients=8.12 (0.77); controls=8.57 (0.94); F=3.93, df=1,47; P=0.05] in the left insular cortex. Insular gray matter volume and cortical surface size correlated negatively and significantly with the psychotic symptom dimension. Schizophrenic patients show morphological abnormalities in the insular cortex at early stages of the illness. These abnormalities are related to the severity of psychotic symptoms. Further investigations are needed to evaluate the role of the insula in the pathophysiology of schizophrenia.

Adult↗

PubMed2000

A new method for the in vivo volumetric measurement of the human hippocampus with high neuroanatomical accuracy.

Accurate and reproducible in vivo measurement of hippocampal volumes using magnetic resonance (MR) imaging is complicated by the morphological complexity of the structure. Additionally, separation of certain parts of the hippocampus from the adjacent brain structures on MR images is sometimes very difficult. These difficulties have led most investigators to either use arbitrary landmarks or to exclude certain parts of the structure from their measurements. Based on three-dimensional MR data, we have developed a reliable in vivo volumetric measurement of the human hippocampus. In contrast to most of the previously described volumetric MR-based methods, we aimed to sample the entire hippocampal formation using its true anatomical definition. This was accomplished by relying on the capacity of the BRAINS software to simultaneously visualize in multiple planes, to "telegraph" tracings or cursor position from one plane to another, and to simultaneously rely on multispectral data from three different image sets (T1, T2, and tissue classified). The methods for identifying boundaries and measuring the hippocampal volume are described. The method has excellent reliability, sensitivity, and specificity. The method may be of use in studies of structure-function relationships in neuropsychiatric disorders such as schizophrenia, temporal lobe epilepsy, and Alzheimer's disease. Future work will use these measurements as training data for a neural net-based technique to identify the anatomical boundaries automatically.

Adolescent↗

PubMed1995

In vitro thermosensitivity of the midline thalamus.

This study compared the thermosensitivity and spontaneous activity of thalamic midline neurons with those of neurons in areas widely regarded to be involved in thermoregulation (preoptic/anterior hypothalamus and posterior hypothalamus). In vitro single unit recordings were made from neurons within the thalamic midline nuclei, the preoptic/anterior hypothalamus and posterior hypothalamus prior to and during a temperature change 3-7 degrees C above and below 37 degrees C. There were no significant differences in the degree of thermosensitivity or the proportion of thermosensitive neurons in the three areas. In each area examined, the thermosensitive neurons had a spontaneous activity which was significantly greater than that of the temperature-insensitive neurons. The results suggest that structures of the midline thalamus may play a role similar to that of the preoptic/anterior hypothalamus and posterior hypothalamus in the processing of temperature related information.

Action Potentials↗

PubMedDate not supplied

Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection.

PURPOSE: To improve the reliability, accuracy, and computational efficiency of tissue classification with multispectral sequences [T1, T2, and proton density (PD)], we developed an automated method for identifying training classes to be used in a discriminant function analysis. We compared it with a supervised operator-dependent method, evaluating its reliability and validity. We also developed a fuzzy (continuous) classification to correct for partial voluming. METHOD: Images were obtained on a 1.5 T GE Signa MR scanner using three pulse sequences that were co-registered. Training classes for the discriminant analysis were obtained in two ways. The operator-dependent method involved defining circular ROIs containing 5-15 voxels that represented "pure" samples of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), using a total of 150-300 voxels for each tissue type. The automated method involved selecting a large number of samples of brain tissue with sufficiently low variance and randomly placed throughout the brain ("plugs"), partitioning these samples into GM, WM, and CSF, and minimizing the amount of variance within each partition of samples to optimize its "purity." The purity of the plug was estimated by calculating the variance of 8 voxels in all modalities (T1, T2, and PD). We also compared "sharp" (discrete) measurements (which classified tissue only as GM, WM, or CSF) and "fuzzy" (continuous) measurements (which corrected for partial voluming by weighting the classification based on the mixture of tissue types in each voxel). RESULTS: Reliability was compared for the operator-dependent and automated methods as well as for the fuzzy versus sharp classification. The automated sharp classifications consistently had the highest interrater and intrarater reliability. Validity was assessed in three ways: reproducibility of measurements when the same individuals were scanned on multiple occasions, sensitivity of the method to detecting changes associated with aging, and agreement between the automated segmentation values and those produced through expert manual segmentation. The sharp automated classification emerged as slightly superior to the other three methods according to each of these validators. Its reproducibility index (intraclass r) was 0.97, 0.98, and 0.98 for total CSF, total GM, and total WM, respectively. Its correlations with age were 0.54, -0.61, and -0.53, respectively. Its percent agreement with the expert manually segmented tissue for the three tissue types was 93, 90, and 94%, respectively. CONCLUSION: Automated identification of training classes for discriminant analysis was clearly superior to a method that required operator intervention. A sharp (discrete) classification into three tissue types was also slightly superior to one that used "fuzzy" classification to produce continuous measurements to correct for partial voluming. This multispectral automated discriminant analysis method produces a computationally efficient, reliable, and valid method for classifying brain tissue into GM, WM, and CSF. It corrects some of the problems with reliability and computational inefficiency previously observed for operator-dependent approaches to segmentation.

Adult↗