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In recent years, bumblebees have become a prominent insect model organism for a variety of biological disciplines, particularly to investigate learning behaviors as well as visual performance. Understanding these behaviors and their underlying neurobiological principles requires a clear understanding of brain anatomy. Furthermore, to be able to compare neuronal branching patterns across individuals, a common framework is required, which has led to the development of 3D standard brain atlases in most of the neurobiological insect model species. Yet, no bumblebee 3D standard brain atlas has been generated. Here we present a brain atlas for the buff-tailed bumblebee Bombus terrestris using micro-computed tomography (micro-CT) scans as a source for the raw data sets, rather than traditional confocal microscopy, to produce the first ever micro-CT-based insect brain atlas. We illustrate the advantages of the micro-CT technique, namely, identical native resolution in the three cardinal planes and 3D structure being better preserved. Our Bombus terrestris brain atlas consists of 30 neuropils reconstructed from ten individual worker bees, with micro-CT allowing us to segment neuropils completely intact, including the lamina, which is a tissue structure often damaged when dissecting for immunolabeling. Our brain atlas can serve as a platform to facilitate future neuroscience studies in bumblebees and illustrates the advantages of micro-CT for specific applications in insect neuroanatomy.

To explore fundamental principles characterizing chemosensory information processing, we have identified antennal-lobe projection neurons in the heliothine moth, including several neuron types not previously described. Generally, odor information is conveyed from the primary olfactory center of the moth brain, the antennal lobe, to higher brain centers via projection neuron axons passing along several parallel pathways, of which the medial, mediolateral, and lateral antennal-lobe tract are considered the classical ones. Recent data have revealed the projections of the individual tracts more in detail demonstrating three main target regions in the protocerebrum; the calyces are innervated mainly by the medial tract, the superior intermediate protocerebrum by the lateral tract exclusively, and the lateral horn by all tracts. In the present study, we have identified, via iontophoretic intracellular staining combined with confocal microscopy, individual projection neurons confined to the tracts mentioned above, plus two additional ones. Further, using the visualization software AMIRA, we reconstructed the stained neurons and registered the models into a standard brain atlas, which allowed us to compare the termination areas of individual projection neurons both across and within distinct tracts. The data demonstrate a morphological diversity of the projection neurons within distinct tracts. Comparison of the output areas of the neurons confined to the three main tracts in the lateral horn showed overlapping terminal regions for the medial and mediolateral tracts; the lateral tract neurons, on the contrary, targeted mostly other output areas in the protocerebrum.

We present a new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science. •A new software package for standardised and automated cross-species tractography.•Homologous white matter bundles in the human and macaque brain.•Human white matter tract atlases generated from large datasets (1000 subjects).•Tractography protocols are standardised, but preserve individual variability.•Generalisability across datasets shown using the HCP and the UK Biobank data.

Despite the widespread use of transcranial magnetic stimulation (TMS), the precise cortical locations underlying the resulting physiological and behavioral effects are still only coarsely known. To date, mapping strategies have relied on projection approaches (often termed “center of gravity” approaches) or maximum electric field value evaluation, and therefore localize the stimulated cortical site only approximately and indirectly. Focusing on the motor cortex, we present and validate a novel method to reliably determine the effectively stimulated cortical site at the individual subject level. The approach combines measurements of motor evoked potentials (MEPs) at different coil positions and orientations with numerical modeling of induced electric fields. We identify sharply bounded cortical areas, around the gyral crowns and rims of the motor hand area, as the origin of MEPs and show that the magnitude of the tangential component and the overall magnitude of the electric field are most relevant for the observed effect. To validate our approach, we identified the coil location and orientation that produces the maximal electric field at the predicted stimulation site, and then experimentally show that this location produces MEPs more efficiently than other tested locations/orientations. Moreover, we used extensive uncertainty and sensitivity analyses to verify the robustness of the method and identify the most critical model parameters. Our generic approach improves the localization of the cortical area effectively stimulated by TMS and may be transferred to other modalities such as language mapping.

Digital diffusion tensor imaging (DTI) templates of the adult human brain are commonly used in neuroimaging research, and their characteristics influence the accuracy of the application. However, a systematic evaluation of the characteristics and performance of standardized and study-specific DTI templates has not been conducted. The purpose of this work was to compare eight available standardized DTI templates to each other (ICBM81, ENIGMA, FMRIB58, SRI24, IIT2, NTU-DSI-122-DTI, IIT v.3.0, Eve), as well as to study-specific templates, in terms of template characteristics (image sharpness, ability to identify small brain structures, artifacts, mean values, noise properties) and performance in spatial normalization and detection of small inter-group FA differences. The IIT v.3.0 template was shown to combine a number of desirable characteristics: includes full-tensor information, is population-based, has high image sharpness, shows no visible artifacts, has low noise levels, has diffusion tensor properties and spatial features representative of data from the average individual adult brain. Furthermore, the IIT v.3.0 template was shown to allow higher inter-subject DTI spatial normalization accuracy, and detection of smaller inter-group FA differences, compared to all other templates, including study-specific templates. These findings were consistent when evaluating the templates in younger as well as older adult cohorts. [Display omitted]

To accurately quantify the radioactivity concentration measured by PET, emission data need to be corrected for photon attenuation; however, the MRI signal cannot easily be converted into attenuation values, making attenuation correction (AC) in PET/MRI challenging. In order to further improve the current vendor-implemented MR-AC methods for absolute quantification, a number of prototype methods have been proposed in the literature. These can be categorized into three types: template/atlas-based, segmentation-based, and reconstruction-based. These proposed methods in general demonstrated improvements compared to vendor-implemented AC, and many studies report deviations in PET uptake after AC of only a few percent from a gold standard CT-AC. Using a unified quantitative evaluation with identical metrics, subject cohort, and common CT-based reference, the aims of this study were to evaluate a selection of novel methods proposed in the literature, and identify the ones suitable for clinical use. In total, 11 AC methods were evaluated: two vendor-implemented (MR-ACDIXON and MR-ACUTE), five based on template/atlas information (MR-ACSEGBONE (Koesters et al., 2016), MR-ACONTARIO (Anazodo et al., 2014), MR-ACBOSTON (Izquierdo-Garcia et al., 2014), MR-ACUCL (Burgos et al., 2014), and MR-ACMAXPROB (Merida et al., 2015)), one based on simultaneous reconstruction of attenuation and emission (MR-ACMLAA (Benoit et al., 2015)), and three based on image-segmentation (MR-ACMUNICH (Cabello et al., 2015), MR-ACCAR-RiDR (Juttukonda et al., 2015), and MR-ACRESOLUTE (Ladefoged et al., 2015)). We selected 359 subjects who were scanned using one of the following radiotracers: [18F]FDG (210), [11C]PiB (51), and [18F]florbetapir (98). The comparison to AC with a gold standard CT was performed both globally and regionally, with a special focus on robustness and outlier analysis. The average performance in PET tracer uptake was within ±5% of CT for all of the proposed methods, with the average±SD global percentage bias in PET FDG uptake for each method being: MR-ACDIXON (−11.3±3.5)%, MR-ACUTE (−5.7±2.0)%, MR-ACONTARIO (−4.3±3.6)%, MR-ACMUNICH (3.7±2.1)%, MR-ACMLAA (−1.9±2.6)%, MR-ACSEGBONE (−1.7±3.6)%, MR-ACUCL (0.8±1.2)%, MR-ACCAR-RiDR (−0.4±1.9)%, MR-ACMAXPROB (−0.4±1.6)%, MR-ACBOSTON (−0.3±1.8)%, and MR-ACRESOLUTE (0.3±1.7)%, ordered by average bias. The overall best performing methods (MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically) showed regional average errors within ±3% of PET with CT-AC in all regions of the brain with FDG, and the same four methods, as well as MR-ACCAR-RiDR, showed that for 95% of the patients, 95% of brain voxels had an uptake that deviated by less than 15% from the reference. Comparable performance was obtained with PiB and florbetapir. All of the proposed novel methods have an average global performance within likely acceptable limits (±5% of CT-based reference), and the main difference among the methods was found in the robustness, outlier analysis, and clinical feasibility. Overall, the best performing methods were MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically. These methods all minimized the number of outliers, standard deviation, and average global and local error. The methods MR-ACMUNICH and MR-ACCAR-RiDR were both within acceptable quantitative limits, so these methods should be considered if processing time is a factor. The method MR-ACSEGBONE also demonstrates promising results, and performs well within the likely acceptable quantitative limits. For clinical routine scans where processing time can be a key factor, this vendor-provided solution currently outperforms most methods. With the performance of the methods presented here, it may be concluded that the challenge of improving the accuracy of MR-AC in adult brains with normal anatomy has been solved to a quantitatively acceptable degree, which is smaller than the quantification reproducibility in PET imaging.

Functional magnetic resonance imaging (MRI) data are usually registered into standard anatomical space. However, standard atlases, such as LPBA40, the Harvard–Oxford atlas, FreeSurfer, and the Jülich cytoarchitectonic maps all lack important detailed information about small subcortical structures like the substantia nigra and subthalamic nucleus. Here we introduce a new subcortical probabilistic atlas based on ultra-high resolution in-vivo anatomical imaging from 7T MRI. The atlas includes six important but elusive subcortical nuclei: the striatum, the globus pallidus internal and external segment (GPi/e), the subthalamic nucleus, the substantia nigra, and the red nucleus. With a sample of 30 young subjects and carefully cross-validated delineation protocols, our atlas is able to capture the anatomical variability within healthy populations for each of the included structures at an unprecedented level of detail. All the generated probabilistic atlases are registered to MNI standard space and are publicly available. •Current brain atlases lack detailed information about subcortical brain structures.•Most atlases are based on low field-strength MRI.•Here, probabilistic atlas maps of 6 important subcortical nuclei using ultra-high resolution 7T MRI are presented.•This atlas is freely available.

Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we automatically created a multi-subject atlas of SWM diffusion-based bundles of the whole brain. For each subject, the complete cortico-cortical tractogram is first split into sub-tractograms connecting pairs of gyri. Then intra-subject shape-based fiber clustering performs compression of each sub-tractogram into a set of bundles. Proceeding further with shape-based clustering provides a match of the bundles across subjects. Bundles found in most of the subjects are instantiated in the atlas. To increase robustness, this procedure was performed with two independent groups of subjects, in order to discard bundles without match across the two independent atlases. Finally, the resulting intersection atlas was projected on a third independent group of subjects in order to filter out bundles without reproducible and reliable projection. The final multi-subject diffusion-based U-fiber atlas is composed of 100 bundles in total, 50 per hemisphere, from which 35 are common to both hemispheres. •We propose an hybrid method for the study of the reproducibility of superficial white matter bundles of the whole brain, using diffusion-weighted imaging.•The method combines cortical parcellation and fiber clustering in order to determine reproducible well-defined bundles across subjects.•A multi-subject atlas of 100 reproducible bundles is finally created, from which 35 are common to both hemispheres.

Noninvasive functional neuroimaging of the human brain can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms are available, and have been widely evaluated in the context of localizing task‐evoked activities. However, no consensus yet exists on the optimum algorithm for resting‐state data. Here, we evaluated the performance of six commonly‐used source reconstruction algorithms based on minimum‐norm and beamforming estimates. Using human resting‐state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor‐level data. Next, we proposed a data‐driven approach to reduce the atlas from the Human Connectome Project's multi‐modal parcellation of the human cortex based on metrics such as MEG signal‐to‐noise‐ratio and resting‐state functional connectivity gradients. This procedure produced a reduced cortical atlas with 230 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners. Our results show that there is no “one size fits all” algorithm, and make recommendations on the appropriate algorithms depending on the data and aimed analyses. Our comprehensive comparisons and recommendations can serve as a guide for choosing appropriate methodologies in future studies of resting‐state MEG. Magnetoencephalography (MEG) measures extracranial magnetic fields, requiring source reconstruction to make neuroanatomical inferences from these signals. Here, we evaluated the performance of six commonly‐used source reconstruction algorithms based on minimum‐norm and beamforming estimates in the context of human resting‐state MEG. Our results show that, there is no “one size fits all” algorithm, and make recommendations on the appropriate algorithms depending on the data and aimed analyses.

Although numerous neuroimaging studies have depicted neural alterations in individuals with obsessive-compulsive disorder (OCD), a psychiatric disorder characterized by intrusive cognitions and repetitive behaviors, the molecular mechanisms connecting brain structural changes and gene expression remain poorly understood. This study combined the Allen Human Brain Atlas dataset with neuroimaging data from the Meta-Analysis (ENIGMA) consortium and independent cohorts. Later, partial least squares regression and enrichment analysis were performed to probe the correlation between transcription and cortical thickness variation among adults with OCD. The cortical map of case-control differences in cortical thickness was spatially correlated with cortical expression of a weighted combination of genes enriched for neurobiologically relevant ontology terms preferentially expressed across different cell types and cortical layers. These genes were specifically expressed in brain tissue, spanning all cortical developmental stages. Protein-protein interaction analysis revealed that these genes coded a network of proteins encompassing various highly interactive hubs. The study findings bridge the gap between neural structure and transcriptome data in OCD, fostering an integrative understanding of the potential biological mechanisms.