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The human lateral geniculate nucleus (LGN) of the visual thalamus is a key subcortical processing site for visual information analysis. Due to its small size and deep location within the brain, a non-invasive characterization of the LGN and its microstructurally distinct magnocellular (M) and parvocellular (P) subdivisions in humans is challenging. Here, we investigated whether structural quantitative MRI (qMRI) methods that are sensitive to underlying microstructural tissue features enable MR-based mapping of human LGN M and P subdivisions. We employed high-resolution 7 Tesla in-vivo qMRI in N = 27 participants and ultra-high resolution 7 Tesla qMRI of a post-mortem human LGN specimen. We found that a quantitative assessment of the LGN and its subdivisions is possible based on microstructure-informed qMRI contrast alone. In both the in-vivo and post-mortem qMRI data, we identified two components of shorter and longer longitudinal relaxation time (T 1 ) within the LGN that coincided with the known anatomical locations of a dorsal P and a ventral M subdivision, respectively. Through ground-truth histological validation, we further showed that the microstructural MRI contrast within the LGN pertains to cyto- and myeloarchitectonic tissue differences between its subdivisions. These differences were based on cell and myelin density, but not on iron content. Our qMRI-based mapping strategy paves the way for an in-depth understanding of LGN function and microstructure in humans. It further enables investigations into the selective contributions of LGN subdivisions to human behavior in health and disease., (Copyright © 2021. Published by Elsevier Inc.)

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.

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.

The lateral premotor cortex (PM) of the macaque monkey is an anatomically multifaceted area, which serves multiple sensorimotor and cognitive functions. While evidence for the functional organization of human premotor cortex accumulates, much less is known about the underlying anatomical properties of this brain region. We used diffusion tractography and functional magnetic resonance imaging (fMRI) to investigate whether the precentral gyrus in humans can be segregated on the basis of anatomical connectivity and of functional activation in a set of cognitive and motor tasks. Tractographic data suggested a distinction between ventral and dorsal premotor cortex, and furthermore inferior and superior subparcellation of both. Functional MRI data corroborated these four areas, showing that anatomical parcellation predicts the distribution of functional activation and vice versa (preliminary evidence). These results may encourage the application of combining diffusion tractography and fMRI in vivo in order to shed light on the correspondence of brain function and anatomy., (Copyright 2009 Elsevier Inc. All rights reserved.)

Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to geometry and conductivity properties of the different head tissues. We propose a low-resolution conductivity estimation (LRCE) method using simulated annealing optimization on high-resolution finite element models that individually optimizes a realistically shaped four-layer volume conductor with regard to the brain and skull compartment conductivities. As input data, the method needs T1- and PD-weighted magnetic resonance images for an improved modeling of the skull and the cerebrospinal fluid compartment and evoked potential data with high signal-to-noise ratio (SNR). Our simulation studies showed that for EEG data with realistic SNR, the LRCE method was able to simultaneously reconstruct both the brain and the skull conductivity together with the underlying dipole source and provided an improved source analysis result. We have also demonstrated the feasibility and applicability of the new method to simultaneously estimate brain and skull conductivity and a somatosensory source from measured tactile somatosensory-evoked potentials of a human subject. Our results show the viability of an approach that computes its own conductivity values and thus reduces the dependence on assigning values from the literature and likely produces a more robust estimate of current sources. Using the LRCE method, the individually optimized four-compartment volume conductor model can, in a second step, be used for the analysis of clinical or cognitive data acquired from the same subject., (2008 Wiley-Liss, Inc.)

To decipher the evolution of the hominoid brain and its functions, it is essential to conduct comparative studies in primates, including our closest living relatives. However, strong ethical concerns preclude in vivo neuroimaging of great apes. We propose a responsible and multidisciplinary alternative approach that links behavior to brain anatomy in non-human primates from diverse ecological backgrounds. The brains of primates observed in the wild or in captivity are extracted and fixed shortly after natural death, and then studied using advanced MRI neuroimaging and histology to reveal macroand microstructures. By linking detailed neuroanatomy with observed behavior within and across primate species, our approach provides new perspectives on brain evolution. Combined with endocranial brain imprints extracted from computed tomographic scans of the skulls these data provide a framework for decoding evolutionary changes in hominin fossils. This approach is poised to become a key resource for investigating the evolution and functional differentiation of hominoid brains. [ABSTRACT FROM AUTHOR]

A neuronal sign of persistent developmental stuttering is the magnified coactivation of right frontal brain regions during speech production. Whether and how stuttering severity relates to the connection strength of these hyperactive right frontal areas to other brain areas is an open question. Scrutinizing such brain-behaviour and structure-function relationships aims at disentangling suspected underlying neuronal mechanisms of stuttering. Here, we acquired diffusion-weighted and functional images from 31 adults who stutter and 34 matched control participants. Using a newly developed structural connectivity measure, we calculated voxel-wise correlations between connection strength and stuttering severity within tract volumes that originated from functionally hyperactive right frontal regions. Correlation analyses revealed that with increasing speech motor deficits the connection strength increased in the right frontal aslant tract, the right anterior thalamic radiation, and in U-shaped projections underneath the right precentral sulcus. In contrast, with decreasing speech motor deficits connection strength increased in the right uncinate fasciculus. Additional group comparisons of whole-brain white matter skeletons replicated the previously reported reduction of fractional anisotropy in the left and right superior longitudinal fasciculus as well as at the junction of right frontal aslant tract and right superior longitudinal fasciculus in adults who stutter compared to control participants. Overall, our investigation suggests that right fronto-temporal networks play a compensatory role as a fluency enhancing mechanism. In contrast, the increased connection strength within subcortical-cortical pathways may be implied in an overly active global response suppression mechanism in stuttering. Altogether, this combined functional MRI-diffusion tensor imaging study disentangles different networks involved in the neuronal underpinnings of the speech motor deficit in persistent developmental stuttering. [ABSTRACT FROM AUTHOR]

While there is ample evidence on the functional and connectional differentiation of the caudate nucleus (CN), less is known about its potential microstructural subdivisions. However, this latter aspect is critical to the local information processing capabilities of the tissue. We applied diffusion MRI, a non-invasive in vivo method that has great potential for the exploration of the brain structure-behavior relationship, in order to characterize the local fiber structure in gray matter of the CN. We report novel evidence of a functionally meaningful structural tri-partition along the anterior-posterior axis of this region. The connectivity of the CN subregions is in line with connectivity evidence from earlier invasive studies in animal models. In addition, histological validation using polarized light imaging (PLI) confirms these results, corroborating the notion that cortico-subcortico-cortical loops involve microstructurally differentiated regions in the caudate nucleus. Methodologically speaking, the comparison with advanced analysis of diffusion MRI shows that diffusion tensor imaging (DTI) yields a simplified view of the CN fiber architecture which is refined by advanced high angular resolution imaging methods. [ABSTRACT FROM AUTHOR]

Several studies have shown that obesity is associated with changes in human brain function and structure. Since women are more susceptible to obesity than men, it seems plausible that neural correlates may also be different. However, this has not been demonstrated so far. To address this issue, we systematically investigated the brain's white matter (WM) structure in 23 lean to obese women (mean age 25.5 y, std 5.1 y; mean body mass index (BMI) 29.5 kg/m2, std 7.3 kg/m2) and 26 lean to obese men (mean age 27.1 y, std 5.0 y; mean BMI 28.8 kg/m2, std 6.8 kg/m2) with diffusion-weighted magnetic resonance imaging (MRI). There was no significant age (p>0.2) or BMI (p>0.7) difference between female and male participants. Using tract-based spatial statistics, we correlated several diffusion parameters including the apparent diffusion coefficient, fractional anisotropy (FA), as well as axial (λII) and radial diffusivity (λ⊥) with BMI and serum leptin levels. In female and male subjects, the putative axon marker λII was consistently reduced throughout the corpus callosum, particularly in the splenium (r =20.62, p<0.005). This suggests that obesity may be associated with axonal degeneration. Only in women, the putative myelin marker l⊥ significantly increased with increasing BMI (r = 0.57, p<0.005) and serum leptin levels (r = 0.62, p<0.005) predominantly in the genu of the corpus callosum, suggesting additional myelin degeneration. Comparable structural changes were reported for the aging brain, which may point to accelerated aging of WM structure in obese subjects. In conclusion, we demonstrate structural WM changes related to an elevated body weight, but with differences between men and women. Future studies on obesity-related functional and structural brain changes should therefore account for sex-related differences. [ABSTRACT FROM AUTHOR]

The article discusses a study on the application of the combined readout-segmented echo-planar imaging (EPI) and generalized autocalibrating partially parallel acquisitions (GRAPPA) to diffusion tensor imaging (DTI) in humans at 7 Tesla (7T). The study shows that the approach results to improved quality of diffusion-weighted (DW) images. The need to further develop the prototype sequence and image reconstruction program used in the study is tackled. Also tackled is the limitation in data storage capability of the magnetic resonance (MR) scanner used.

In contrast to simple structures in animal vocal behavior, hierarchical structures such as center-embedded sentences manifest the core computational faculty of human language. Previous artificial grammar learning studies found that the left pars opercularis (LPO) subserves the processing of hierarchical structures. However, it is not clear whether this area is activated by the structural complexity per se or by the increased memory load entailed in processing hierarchical structures. To dissociate the effect of structural complexity from the effect of memory cost, we conducted a functional magnetic resonance imaging study of German sentence processing with a 2-way factorial design tapping structural complexity (with/without hierarchical structure, i.e., center-embedding of clauses) and working memory load (long/short distance between syntactically dependent elements; i.e., subject nouns and their respective verbs). Functional imaging data revealed that the processes for structure and memory operate separately but co-operatively in the left inferior frontal gyrus; activities in the LPO increased as a function of structural complexity. whereas activities in the left inferior frontal sulcus (LIFS) were modulated by the distance over which the syntactic information had to be transferred. Diffusion tensor imaging showed that these 2 regions were interconnected through white matter fibers. Moreover, functional coupling between the 2 regions was found to increase during the processing of complex, hierarchically structured sentences. These results suggest a neuroanatomical segregation of syntax-related aspects represented in the LPO from memory-related aspects reflected in the LIFS, which are, however, highly interconnected functionally and anatomically. [ABSTRACT FROM AUTHOR]

Abstract: The human brain forms a complex neural network with a connectional architecture that is still far from being known in full detail, even at the macroscopic level. The advent of diffusion MR imaging has enabled the exploration of the structural properties of white matter in vivo. In this article we propose a new forward model that maps the microscopic geometry of nervous tissue onto the water diffusion process and further onto the measured MR signals. Our spherical deconvolution approach completely parameterizes the fiber orientation density by a finite mixture of Bingham distributions. In addition, we define the term anatomical connectivity, taking the underlying image modality into account. This neurophysiological metric may represent the proportion of the nerve fibers originating in the source area which intersect a given target region. The specified inverse problem is solved by Bayesian statistics. Posterior probability maps denote the probability that the connectivity value exceeds a chosen threshold, conditional upon the noisy observations. These maps allow us to draw inferences about the structural organization of the cerebral cortex. Moreover, we will demonstrate the proposed approach with diffusion-weighted data sets featuring high angular resolution. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: We have recently introduced a novel MRI methodology, so-called super resolution track-density imaging (TDI), which produces high-quality white matter images, with high spatial resolution and exquisite anatomical contrast not available from other MRI modalities. This method achieves super resolution by utilising the long-range information contained in the diffusion MRI fibre tracks. In this study, we validate the super resolution property of the TDI method by using in vivo diffusion MRI data acquired at ultra-high magnetic field strength (7T), and in silico diffusion MRI data from a well-characterised numerical phantom. Furthermore, an alternative version of the TDI technique is described, which mitigates the track length weighting of the TDI map intensity. For the in vivo data, high-resolution diffusion images were down-sampled to simulate low-resolution data, for which the high-resolution images serve as a gold standard. For the in silico data, the gold standard is given by the known simulated structures of the numerical phantom. Both the in vivo and in silico data show that the structures that could be identified in the TDI maps only after using super resolution were consistent with the corresponding structures identified in the reference maps. This supports the claim that the structures identified by the super resolution step are accurate, thus providing further evidence for the important potential role of the super resolution TDI methodology in neuroscience. [Copyright &y& Elsevier]

• Novel acquisition and reconstruction strategy to increase the signal to noise ratio (SNR) and contrast of diffusion MRI. • Statistical data modelling for image reconstruction with reduced noise bias. • Substantial SNR gain for only moderate increase in acquisition time. Post-mortem diffusion MRI (dMRI) enables acquisitions of structural imaging data with otherwise unreachable resolutions - at the expense of longer scanning times. These data are typically acquired using highly segmented image acquisition strategies, thereby resulting in an incomplete signal decay before the MRI encoding continues. Especially in dMRI, with low signal intensities and lengthy contrast encoding, such temporal inefficiency translates into reduced image quality and longer scanning times. This study introduces Multi Echo (ME) acquisitions to dMRI on a human MRI system - a time-efficient approach, which increases SNR (Signal-to-Noise Ratio) and reduces noise bias for dMRI images. The benefit of the introduced ME-dMRI method was validated using numerical Monte Carlo simulations and showcased on a post-mortem brain of a wild chimpanzee. The proposed Maximum Likelihood Estimation echo combination results in an optimal SNR without detectable signal bias. The combined strategy comes at a small price in scanning time (here 30% additional) and leads to a substantial SNR increase (here white matter: ~ 1.6x, equivalent to 2.6 averages, grey matter: ~ 1.9x, equivalent to 3.6 averages) and a general reduction of the noise bias. [ABSTRACT FROM AUTHOR]