Mammillary body and hypothalamic volumes in mood disorders.

We have previously reported an in vivo enlargement of the left hypothalamus in mood disorders using 7 T magnetic resonance imaging. The aim of this follow-up study was to find out whether the hypothalamic volume difference may be located in the mammillary bodies (MB) rather than being widespread across the hypothalamus. We developed and evaluated a detailed segmentation algorithm that allowed a reliable segmentation of the MBs, and applied it to 20 unmedicated (MDDu) and 20 medicated patients with major depressive disorder, 21 medicated patients with bipolar disorder, and 23 controls. 20 out of 23 healthy controls were matched to the MDDu. We tested for group differences in MB and hypothalamus without MB (HTh) volumes using analyses of covariance. Associations between both volumes of interest were analysed using bivariate and partial correlations. In contrast to postmortem findings, we found no statistically significant differences of the MB volumes between the study groups. Left HTh volumes differed significantly across the study groups after correction for intracranial volume (ICV) and for ICV and sex. Our result of an HTh enlargement in mood disorders was confirmed by a paired t-test between the matched pairs of MDDu and healthy controls using the native MB and HTh volumes. In the whole sample, MB volumes correlated significantly with the ipsilateral HTh volumes. Our results indicate a structural relationship between both volumes, and that our previous in vivo finding of a hypothalamus enlargement does not extend to the MB, but is limited to the HTh. The enlargement is more likely related to the dysregulation of the HPA axis than to cognitive dysfunctions accompanying mood disorders.

A semi-automated algorithm for hypothalamus volumetry in 3 Tesla magnetic resonance images.

The hypothalamus, a small diencephalic gray matter structure, is part of the limbic system. Volumetric changes of this structure occur in psychiatric diseases, therefore there is increasing interest in precise volumetry. Based on our detailed volumetry algorithm for 7 Tesla magnetic resonance imaging (MRI), we developed a method for 3 Tesla MRI, adopting anatomical landmarks and work in triplanar view. We overlaid T1-weighted MR images with gray matter-tissue probability maps to combine anatomical information with tissue class segmentation. Then, we outlined regions of interest (ROIs) that covered potential hypothalamus voxels. Within these ROIs, seed growing technique helped define the hypothalamic volume using gray matter probabilities from the tissue probability maps. This yielded a semi-automated method with short processing times of 20-40 min per hypothalamus. In the MRIs of ten subjects, reliabilities were determined as intraclass correlations (ICC) and volume overlaps in percent. Three raters achieved very good intra-rater reliabilities (ICC 0.82-0.97) and good inter-rater reliabilities (ICC 0.78 and 0.82). Overlaps of intra- and inter-rater runs were very good (≥ 89.7%). We present a fast, semi-automated method for in vivo hypothalamus volumetry in 3 Tesla MRI.

Intensity standardisation of 7T MR images for intensity-based segmentation of the human hypothalamus.

The high spatial resolution of 7T MRI enables us to identify subtle volume changes in brain structures, providing potential biomarkers of mental disorders. Most volumetric approaches require that similar intensity values represent similar tissue types across different persons. By applying colour-coding to T1-weighted MP2RAGE images, we found that the high measurement accuracy achieved by high-resolution imaging may be compromised by inter-individual variations in the image intensity. To address this issue, we analysed the performance of five intensity standardisation techniques in high-resolution T1-weighted MP2RAGE images. Twenty images with extreme intensities in the GM and WM were standardised to a representative reference image. We performed a multi-level evaluation with a focus on the hypothalamic region-analysing the intensity histograms as well as the actual MR images, and requiring that the correlation between the whole-brain tissue volumes and subject age be preserved during standardisation. The results were compared with T1 maps. Linear standardisation using subcortical ROIs of GM and WM provided good results for all evaluation criteria: it improved the histogram alignment within the ROIs and the average image intensity within the ROIs and the whole-brain GM and WM areas. This method reduced the inter-individual intensity variation of the hypothalamic boundary by more than half, outperforming all other methods, and kept the original correlation between the GM volume and subject age intact. Mixed results were obtained for the other four methods, which sometimes came at the expense of unwarranted changes in the age-related pattern of the GM volume. The mapping of the T1 relaxation time with the MP2RAGE sequence is advertised as being especially robust to bias field inhomogeneity. We found little evidence that substantiated the T1 map's theoretical superiority over the T1-weighted images regarding the inter-individual image intensity homogeneity.

Development and evaluation of an algorithm for the computer-assisted segmentation of the human hypothalamus on 7-Tesla magnetic resonance images.

Post mortem studies have shown volume changes of the hypothalamus in psychiatric patients. With 7T magnetic resonance imaging this effect can now be investigated in vivo in detail. To benefit from the sub-millimeter resolution requires an improved segmentation procedure. The traditional anatomical landmarks of the hypothalamus were refined using 7T T1-weighted magnetic resonance images. A detailed segmentation algorithm (unilateral hypothalamus) was developed for colour-coded, histogram-matched images, and evaluated in a sample of 10 subjects. Test-retest and inter-rater reliabilities were estimated in terms of intraclass-correlation coefficients (ICC) and Dice's coefficient (DC). The computer-assisted segmentation algorithm ensured test-retest reliabilities of ICC≥.97 (DC≥96.8) and inter-rater reliabilities of ICC≥.94 (DC = 95.2). There were no significant volume differences between the segmentation runs, raters, and hemispheres. The estimated volumes of the hypothalamus lie within the range of previous histological and neuroimaging results. We present a computer-assisted algorithm for the manual segmentation of the human hypothalamus using T1-weighted 7T magnetic resonance imaging. Providing very high test-retest and inter-rater reliabilities, it outperforms former procedures established at 1.5T and 3T magnetic resonance images and thus can serve as a gold standard for future automated procedures.

Diffusion imaging-based subdivision of the human hypothalamus: a magnetic resonance study with clinical implications.

The hypothalamus and its subdivisions are involved in many neuropsychiatric conditions such as affective disorders, schizophrenia, or narcolepsy, but parcellations of hypothalamic subnuclei have hitherto been feasible only with histological techniques in postmortem brains. In an attempt to map subdivisions of the hypothalamus in vivo, we analyzed the directionality information from high-resolution diffusion-weighted magnetic resonance images of healthy volunteers. We acquired T1-weighted and diffusion-weighted scans in ten healthy subjects at 3 T. In the T1-weighted images, we manually delineated an individual mask of the hypothalamus in each subject and computed in the co-registered diffusion-weighted images the similarity of the principal diffusion direction for each pair of mask voxels. By clustering the similarity matrix into three regions with a k-means algorithm, we obtained an anatomically coherent arrangement of subdivisions across hemispheres and subjects. In each hypothalamus mask, we found an anterior region with dorsoventral principal diffusion direction, a posteromedial region with rostro-caudal direction, and a lateral region with mediolateral direction. A comparative analysis with microstructural hypothalamus parcellations from the literature reveals that each of these regions corresponds to a specific group of hypothalamic subnuclei as defined in postmortem brains. This is to our best knowledge the first in vivo study that attempts a delineation of hypothalamic subdivisions by clustering diffusion-weighted magnetic resonance imaging data. When applied in a larger sample of neuropsychiatric patients, a structural analysis of hypothalamic subnuclei should contribute to a better understanding of the pathogenesis of neuropsychiatric conditions such as affective disorders.

Structural studies of the hypothalamus and its nuclei in mood disorders.

A large body of evidence indicates that the hypothalamus is involved in pathogenetic mechanisms of mood disorders. It has been suggested that functional abnormalities of the hypothalamus are associated with structural hypothalamic changes. Structural neuroimaging allows in vivo investigation of the hypothalamus that may shed light on the underlying pathogenetic mechanisms of unipolar and bipolar disorder. Clearly, the detection of subtle structural cerebral changes depends on the limitations of the neuroimaging technique used. Making a comprehensive database search, we reviewed the literature on hypothalamic macrostructure in affective disorders, addressing the specific question of what structural magnetic resonance imaging might be expected to show. Studies with convincing methodology, although rare, suggest a global volume decrease in the hypothalamus in affective disorders, a decrease which is not shown by the two specific nuclei investigated, the paraventricular and supraoptic nuclei. We discuss the implications of these findings and provide directions for future research.

Imagery of voluntary movement of fingers, toes, and tongue activates corresponding body-part-specific motor representations.

We investigate whether imagery of voluntary movements of different body parts activates somatotopical sections of the human motor cortices. We used functional magnetic resonance imaging to detect the cortical activity when 7 healthy subjects imagine performing repetitive (0.5-Hz) flexion/extension movements of the right fingers or right toes, or horizontal movements of the tongue. We also collected functional images when the subjects actually executed these movements and used these data to define somatotopical representations in the motor areas. In this study, we relate the functional activation maps to cytoarchitectural population maps of areas 4a, 4p, and 6 in the same standard anatomical space. The important novel findings are 1). that imagery of hand movements specifically activates the hand sections of the contralateral primary motor cortex (area 4a) and the contralateral dorsal premotor cortex (area 6) and a hand representation located in the caudal cingulate motor area and the most ventral part of the supplementary motor area; 2). that when imagining making foot movements, the foot zones of the posterior part of the contralateral supplementary motor area (area 6) and the contralateral primary motor cortex (area 4a) are active; and 3). that imagery of tongue movements activates the tongue region of the primary motor cortex and the premotor cortex bilaterally (areas 4a, 4p, and 6). These results demonstrate that imagery of action engages the somatotopically organized sections of the primary motor cortex in a systematic manner as well as activating some body-part-specific representations in the nonprimary motor areas. Thus the content of the mental motor image, in this case the body part, is reflected in the pattern of motor cortical activation.