Hypothalamus enlargement in mood disorders.

OBJECTIVE: The purpose of this study was to determine, in vivo, whether the hypothalamus volume is reduced in patients with mood disorders. METHODS: The cross-sectional study included 20 unmedicated (MDDu) and 20 medicated patients with major depressive disorder, 21 patients with bipolar disorder, and 23 controls. Twenty of the controls were matched to the MDDu. Seven Tesla, T1-weighted magnetic resonance images were acquired and processed using methods specifically developed for high-precision volumetry of the hypothalamus. RESULTS: An overall group difference was observed for the left hypothalamus volume corrected for intracranial volume. Planned contrasts identified that the left hypothalamus was approximately 5% larger in each patient group compared with the control group. A paired t-test with the 20 matched pairs of MDDu and controls and without correction for covariates confirmed the larger left hypothalamus volume in MDDu. CONCLUSIONS: Contrary to our expectations, the hypothalamus volume was increased in patients with uni- and bipolar affective disorders. The effect was left-sided and independent of medication status or statistical correction for covariates. Supported by emerging evidence that the stress response may be related to structural and functional asymmetry in the brain, our finding suggests a crucial role of the hypothalamus in mood disorders.

Human Cerebellar Sub-millimeter Diffusion Imaging Reveals the Motor and Non-motor Topography of the Dentate Nucleus.

The reciprocal cortico-cerebellar loops that underlie cerebellar contributions to motor and cognitive behavior form one of the largest systems in the primate brain. Work with non-human primates has shown that the dentate nucleus, the major output nucleus of the cerebellum, contains topographically distinct connections to both motor and non-motor regions, yet there is no evidence for how the cerebellar cortex connects to the dentate nuclei in humans. Here we used in-vivo sub-millimeter diffusion imaging to characterize this fundamental component of the cortico-cerebellar loop, and identified a pattern of superior motor and infero-lateral non-motor connectivity strikingly similar to that proposed by animal work. Crucially, we also present first evidence that the dominance for motor connectivity observed in non-human primates may be significantly reduced in man - a finding that is in accordance with the proposed increase in cerebellar contributions to higher cognitive behavior over the course of primate evolution.

Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS).

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both voxel space and diffusion-gradient space. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. POAS preserves the edges of the observed fine and anisotropic structures. It is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving a similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.

EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model.

The major goal of the evaluation in presurgical epilepsy diagnosis for medically intractable patients is the precise reconstruction of the epileptogenic foci, preferably with non-invasive methods. This paper evaluates whether surface electroencephalography (EEG) source analysis based on a 1 mm anisotropic finite element (FE) head model can provide additional guidance for presurgical epilepsy diagnosis and whether it is practically feasible in daily routine. A 1 mm hexahedra FE volume conductor model of the patient's head with special focus on accurately modeling the compartments skull, cerebrospinal fluid (CSF) and the anisotropic conducting brain tissues was constructed using non-linearly co-registered T1-, T2- and diffusion-tensor-magnetic resonance imaging data. The electrodes of intra-cranial EEG (iEEG) measurements were extracted from a co-registered computed tomography image. Goal function scan (GFS), minimum norm least squares (MNLS), standardized low resolution electromagnetic tomography (sLORETA) and spatio-temporal current dipole modeling inverse methods were then applied to the peak of the averaged ictal discharges EEG data. MNLS and sLORETA pointed to a single center of activity. Moving and rotating single dipole fits resulted in an explained variance of more than 97%. The non-invasive EEG source analysis methods localized at the border of the lesion and at the border of the iEEG electrodes which mainly received ictal discharges. Source orientation was towards the epileptogenic tissue. For the reconstructed superficial source, brain conductivity anisotropy and the lesion conductivity had only a minor influence, whereas a correct modeling of the highly conducting CSF compartment and the anisotropic skull was found to be important. The proposed FE forward modeling approach strongly simplifies meshing and reduces run-time (37 ms for one forward computation in the model with 3.1 million unknowns), corroborating the practical feasibility of the approach.

Mathematical methods for diffusion MRI processing.

In this article, we review recent mathematical models and computational methods for the processing of diffusion Magnetic Resonance Images, including state-of-the-art reconstruction of diffusion models, cerebral white matter connectivity analysis, and segmentation techniques. We focus on Diffusion Tensor Images (DTI) and Q-Ball Images (QBI).

Connectivity-Based Parcellation of Broca's Area.

It is generally agreed that the cerebral cortex can be segregated into structurally and functionally distinct areas. Anatomical subdivision of Broca's area has been achieved using different microanatomical criteria, such as cytoarchitecture and distribution of neuroreceptors. However, brain function also strongly depends upon anatomical connectivity, which therefore forms a sensible criterion for the functio-anatomical segregation of cortical areas. Diffusion-weighted magnetic resonance (MR) imaging offers the opportunity to apply this criterion in the individual living subject. Probabilistic tractographic methods provide excellent means to extract the connectivity signatures from diffusion-weighting MR data sets. The correlations among these signatures may then be used by an automatic clustering method to identify cortical regions with mutually distinct and internally coherent connectivity. We made use of this principle to parcellate Broca's area. As it turned out, 3 subregions are discernible that were identified as putative Brodmann area (BA) 44, BA45, and the deep frontal operculum. These results are discussed in the light of previous evidence from other methods in both human and nonhuman primates. We conclude that plausible results can be achieved by the proposed technique, which cannot be obtained by any other method in vivo. For the first time, there is a possibility to investigate the anatomical subdivision of Broca's area noninvasively in the individual living human subject.

Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: a simulation and visualization study using high-resolution finite element modeling.

To achieve a deeper understanding of the brain, scientists, and clinicians use electroencephalography (EEG) and magnetoencephalography (MEG) inverse methods to reconstruct sources in the cortical sheet of the human brain. The influence of structural and electrical anisotropy in both the skull and the white matter on the EEG and MEG source reconstruction is not well understood. In this paper, we report on a study of the sensitivity to tissue anisotropy of the EEG/MEG forward problem for deep and superficial neocortical sources with differing orientation components in an anatomically accurate model of the human head. The goal of the study was to gain insight into the effect of anisotropy of skull and white matter conductivity through the visualization of field distributions, isopotential surfaces, and return current flow and through statistical error measures. One implicit premise of the study is that factors that affect the accuracy of the forward solution will have at least as strong an influence over solutions to the associated inverse problem. Major findings of the study include (1) anisotropic white matter conductivity causes return currents to flow in directions parallel to the white matter fiber tracts; (2) skull anisotropy has a smearing effect on the forward potential computation; and (3) the deeper a source lies and the more it is surrounded by anisotropic tissue, the larger the influence of this anisotropy on the resulting electric and magnetic fields. Therefore, for the EEG, the presence of tissue anisotropy both for the skull and white matter compartment substantially compromises the forward potential computation and as a consequence, the inverse source reconstruction. In contrast, for the MEG, only the anisotropy of the white matter compartment has a significant effect. Finally, return currents with high amplitudes were found in the highly conducting cerebrospinal fluid compartment, underscoring the need for accurate modeling of this space.

The influence of volume conduction effects on the EEG/MEG reconstruction of the sources of the Early Left Anterior Negativity.

To achieve a deeper understanding of language processing in the human brain, scientists and clinicians use Electroencephalography (EEG) and Magnetoencephalography (MEG) inverse methods to reconstruct sources of Event Related Potentials. There exists a persistent uncertainty regarding the influence of volume conduction effects such as the anisotropy of tissue conductivity of the skull and the white matter layers on the inverse results. In this paper, we will study the sensitivity to anisotropy of the source reconstruction of the Early Left Anterior Negativity (ELAN) component in language processing. For EEG, the presence of tissue anisotropy substantially compromises the restoration ability of an L1-norm current density approach. The centers of activity are strongly shifted along the Sylvian fissure in the anterior direction. In contrast, MEG in combination with the L1 norm approach is able to reconstruct the main features of the ELAN source distribution even in the presence of anisotropic conductivity.