The Amsterdam Ultra-high field adult lifespan database (AHEAD): A freely available multimodal 7 Tesla submillimeter magnetic resonance imaging database.

Normative databases allow testing of novel hypotheses without the costly collection of magnetic resonance imaging (MRI) data. Here we present the Amsterdam Ultra-high field adult lifespan database (AHEAD). The AHEAD consists of 105 7 Tesla (T) whole-brain structural MRI scans tailored specifically to imaging of the human subcortex, including both male and female participants and covering the entire adult life span (18-80 yrs). We used these data to create probability maps for the subthalamic nucleus, substantia nigra, internal and external segment of the globus pallidus, and the red nucleus. Data was acquired at a submillimeter resolution using a multi-echo (ME) extension of the second gradient-echo image of the MP2RAGE sequence (MP2RAGEME) sequence, resulting in complete anatomical alignment of quantitative, R1-maps, R2*-maps, T1-maps, T1-weighted images, T2*-maps, and quantitative susceptibility mapping (QSM). Quantitative MRI maps, and derived probability maps of basal ganglia structures are freely available for further analyses.

Functional neuroanatomical review of the ventral tegmental area.

The ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) are assumed to play a key role in dopamine-related functions such as reward-related behaviour, motivation, addiction and motor functioning. Although dopamine-producing midbrain structures are bordering, they show significant differences in structure and function that argue for a distinction when studying the functions of the dopaminergic midbrain, especially by means of neuroimaging. First, unlike the SNc, the VTA is not a nucleus, which makes it difficult to delineate the structure due to lack of clear anatomical borders. Second, there is no consensus in the literature about the anatomical nomenclature to describe the VTA. Third, these factors in combination with limitations in magnetic resonance imaging (MRI) complicate VTA visualization. We suggest that developing an MRI-compatible probabilistic atlas of the VTA will help to overcome these issues. Such an atlas can be used to identify the individual VTA and serve as region-of-interest for functional MRI.

Bias in the brain: a diffusion model analysis of prior probability and potential payoff.

In perceptual decision-making, advance knowledge biases people toward choice alternatives that are more likely to be correct and more likely to be profitable. Accumulation-to-bound models provide two possible explanations for these effects: prior knowledge about the relative attractiveness of the alternatives at hand changes either the starting point of the decision process, or the rate of evidence accumulation. Here, we used model-based functional MRI to investigate whether these effects are similar for different types of prior knowledge, and whether there is a common neural substrate underlying bias in simple perceptual choices. We used two versions of the random-dot motion paradigm in which we manipulated bias by: (1) changing the prior likelihood of occurrence for two alternatives ("prior probability") and (2) assigning a larger reward to one of two alternatives ("potential payoff"). Human subjects performed the task inside and outside a 3T MRI scanner. For each manipulation, bias was quantified by fitting the drift diffusion model to the behavioral data. Individual measurements of bias were then used in the imaging analyses to identify regions involved in biasing choice behavior. Behavioral results showed that subjects tended to make more and faster choices toward the alternative that was most probable or had the largest payoff. This effect was primarily due to a change in the starting point of the accumulation process. Imaging results showed that, at cue level, regions of the frontoparietal network are involved in changing the starting points in both manipulations, suggesting a common mechanism underlying the biasing effects of prior knowledge.

Drift-diffusion modeling reveals that masked faces are preconceived as unfriendly.

During the COVID-19 pandemic, the use of face masks has become a daily routine. Studies have shown that face masks increase the ambiguity of facial expressions which not only affects (the development of) emotion recognition, but also interferes with social interaction and judgement. To disambiguate facial expressions, we rely on perceptual (stimulus-driven) as well as preconceptual (top-down) processes. However, it is unknown which of these two mechanisms accounts for the misinterpretation of masked expressions. To investigate this, we asked participants (N = 136) to decide whether ambiguous (morphed) facial expressions, with or without a mask, were perceived as friendly or unfriendly. To test for the independent effects of perceptual and preconceptual biases we fitted a drift-diffusion model (DDM) to the behavioral data of each participant. Results show that face masks induce a clear loss of information leading to a slight perceptual bias towards friendly choices, but also a clear preconceptual bias towards unfriendly choices for masked faces. These results suggest that, although face masks can increase the perceptual friendliness of faces, people have the prior preconception to interpret masked faces as unfriendly.

Manual delineation approaches for direct imaging of the subcortex.

The growing interest in the human subcortex is accompanied by an increasing number of parcellation procedures to identify deep brain structures in magnetic resonance imaging (MRI) contrasts. Manual procedures continue to form the gold standard for parcellating brain structures and is used for the validation of automated approaches. Performing manual parcellations is a tedious process which requires a systematic and reproducible approach. For this purpose, we created a series of protocols for the anatomical delineation of 21 individual subcortical structures. The intelligibility of the protocols was assessed by calculating Dice similarity coefficients for ten healthy volunteers. In addition, dilated Dice coefficients showed that manual parcellations created using these protocols can provide high-quality training data for automated algorithms. Here, we share the protocols, together with three example MRI datasets and the created manual delineations. The protocols can be applied to create high-quality training data for automated parcellation procedures, as well as for further validation of existing procedures and are shared without restrictions with the research community.

7T functional MRI finds no evidence for distinct functional subregions in the subthalamic nucleus during a speeded decision-making task.

The subthalamic nucleus (STN) is a small, subcortical brain structure. It is a target for deep brain stimulation, an invasive treatment that reduces motor symptoms of Parkinson's disease. Side effects of DBS are commonly explained using the tripartite model of STN organization, which proposes three functionally distinct subregions in the STN specialized in cognitive, limbic, and motor processing. However, evidence for the tripartite model exclusively comes from anatomical studies and functional studies using clinical patients. Here, we provide the first experimental tests of the tripartite model in healthy volunteers using ultra-high field 7 Tesla (T) functional magnetic resonance imaging (fMRI). Thirty-four participants performed a random-dot motion decision-making task with a difficulty manipulation and a choice payoff manipulation aimed to differentially affect cognitive and limbic networks. Moreover, participants responded with their left and right index finger, differentially affecting motor networks. We analysed BOLD signal in three subregions of the STN along the dorsolateral-ventromedial axis, identified using manually delineated high resolution anatomical images and based on a previously published atlas. Using these paradigms, all segments responded equally to the experimental manipulations, and the tasks did not provide evidence for the tripartite model.

A high-resolution multi-shell 3T diffusion magnetic resonance imaging dataset as part of the Amsterdam Ultra-high field adult lifespan database (AHEAD).

In order to further our understanding of brain function and the underlying networks, more advanced diffusion weighted magnetic resonance imaging (DWI MRI) data are essential. Here we present freely available high-resolution multi-shell multi-directional 3 Tesla (T) DWI MRI data as part of the 'Amsterdam Ultra-high field adult lifespan database' (AHEAD). The 3T DWI AHEAD dataset include 1.28mm isotropic whole brain DWI data of 49 healthy adult participants between 18 and 90 years old. The acquired data include DWIs at three non-zero b-values (48 directions, b-value 700 s/mm2; 56 directions, b-value 1000 s/mm2; 64 directions, b-value 1600 s/mm2) including a total of twelve volumes with a b-value of 0 s/mm2 (b0 volumes). In addition, eight b0 volumes with a reversed phase encoding direction were acquired to correct for distortions. To facilitate future use, the DWI data have been denoised, corrected for eddy currents, susceptibility-induced off-resonance field distortions, bias fields, and are skull stripped.

Functional connectivity between cognitive control regions is sensitive to familial risk for ADHD.

Familial risk for attention-deficit hyperactivity disorder (ADHD) has been associated with changes in brain activity related to cognitive control. However, it is not clear whether changes in activation are the primary deficit or whether they are related to impaired communication between regions involved in this ability. We investigated whether (1) functional connectivity between regions involved in cognitive control was affected by familial risk and (2) changes were specific to these regions. Correlational seed analyses were used to investigate temporal covariance between cognitive control and motor regions in two independent samples of typically developing controls, subjects with ADHD and their unaffected siblings. In both samples, correlation coefficients between cognitive control regions were greater for typically developing controls than for subjects with ADHD, with intermediate values for unaffected siblings. Within the motor network, unaffected siblings showed correlations similar to typically developing children. There were no differences in activity between the brain regions involved. These data show that functional connectivity between cognitive control regions is sensitive to familial risk for ADHD. Results suggest that changes in connectivity associated with cognitive control may be suitable as an intermediate phenotype for future studies.

A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data.

Functional magnetic resonance imaging (fMRI) BOLD signal is commonly localized by using neuroanatomical atlases, which can also serve for region of interest analyses. Yet, the available MRI atlases have serious limitations when it comes to imaging subcortical structures: only 7% of the 455 subcortical nuclei are captured by current atlases. This highlights the general difficulty in mapping smaller nuclei deep in the brain, which can be addressed using ultra-high field 7 Tesla (T) MRI. The ventral tegmental area (VTA) is a subcortical structure that plays a pivotal role in reward processing, learning and memory. Despite the significant interest in this nucleus in cognitive neuroscience, there are currently no available, anatomically precise VTA atlases derived from 7 T MRI data that cover the full region of the VTA. Here, we first provide a protocol for multimodal VTA imaging and delineation. We then provide a data description of a probabilistic VTA atlas based on in vivo 7 T MRI data.

Multi-contrast anatomical subcortical structures parcellation.

The human subcortex is comprised of more than 450 individual nuclei which lie deep in the brain. Due to their small size and close proximity, up until now only 7% have been depicted in standard MRI atlases. Thus, the human subcortex can largely be considered as terra incognita. Here, we present a new open-source parcellation algorithm to automatically map the subcortex. The new algorithm has been tested on 17 prominent subcortical structures based on a large quantitative MRI dataset at 7 Tesla. It has been carefully validated against expert human raters and previous methods, and can easily be extended to other subcortical structures and applied to any quantitative MRI dataset. In sum, we hope this novel parcellation algorithm will facilitate functional and structural neuroimaging research into small subcortical nuclei and help to chart terra incognita.

3 versus 7 Tesla magnetic resonance imaging for parcellations of subcortical brain structures in clinical settings.

7 Tesla (7T) magnetic resonance imaging holds great promise for improved visualization of the human brain for clinical purposes. To assess whether 7T is superior regarding localization procedures of small brain structures, we compared manual parcellations of the red nucleus, subthalamic nucleus, substantia nigra, globus pallidus interna and externa. These parcellations were created on a commonly used clinical anisotropic clinical 3T with an optimized isotropic (o)3T and standard 7T scan. The clinical 3T MRI scans did not allow delineation of an anatomically plausible structure due to its limited spatial resolution. o3T and 7T parcellations were directly compared. We found that 7T outperformed the o3T MRI as reflected by higher Dice scores, which were used as a measurement of interrater agreement for manual parcellations on quantitative susceptibility maps. This increase in agreement was associated with higher contrast to noise ratios for smaller structures, but not for the larger globus pallidus segments. Additionally, control-analyses were performed to account for potential biases in manual parcellations by assessing semi-automatic parcellations. These results showed a higher consistency for structure volumes for 7T compared to optimized 3T which illustrates the importance of the use of isotropic voxels for 3D visualization of the surgical target area. Together these results indicate that 7T outperforms c3T as well as o3T given the constraints of a clinical setting.

7 Tesla MRI Followed by Histological 3D Reconstructions in Whole-Brain Specimens.

Post mortem magnetic resonance imaging (MRI) studies on the human brain are of great interest for the validation of in vivo MRI. It facilitates a link between functional and anatomical information available from MRI in vivo and neuroanatomical knowledge available from histology/immunocytochemistry. However, linking in vivo and post mortem MRI to microscopy techniques poses substantial challenges. Fixation artifacts and tissue deformation of extracted brains, as well as co registration of 2D histology to 3D MRI volumes complicate direct comparison between modalities. Moreover, post mortem brain tissue does not have the same physical properties as in vivo tissue, and therefore MRI approaches need to be adjusted accordingly. Here, we present a pipeline in which whole-brain human post mortem in situ MRI is combined with subsequent tissue processing of the whole human brain, providing a 3-dimensional reconstruction via blockface imaging. To this end, we adapted tissue processing procedures to allow both post mortem MRI and subsequent histological and immunocytochemical processing. For MRI, tissue was packed in a susceptibility matched solution, tailored to fit the dimensions of the MRI coil. Additionally, MRI sequence parameters were adjusted to accommodate T1 and T2∗ shortening, and scan time was extended, thereby benefiting the signal-to-noise-ratio that can be achieved using extensive averaging without motion artifacts. After MRI, the brain was extracted from the skull and subsequently cut while performing optimized blockface imaging, thereby allowing three-dimensional reconstructions. Tissues were processed for Nissl and silver staining, and co-registered with the blockface images. The combination of these techniques allows direct comparisons across modalities.

Size and shape matter: The impact of voxel geometry on the identification of small nuclei.

How, and to what extent do size and shape of a voxel measured with magnetic resonance imaging (MRI) affect the ability to visualize small brain nuclei? Despite general consensus that voxel geometry affects volumetric properties of regions of interest, particularly those of small brain nuclei, no quantitative data on the influence of voxel size and shape on labeling accuracy is available. Using simulations, we investigated the selective influence of voxel geometry by reconstructing simulated ellipsoid structures with voxels varying in shape and size. For each reconstructed ellipsoid, we calculated differences in volume and similarity between the labeled volume and the predefined dimensions of the ellipsoid. Probability functions were derived from one or two individual raters and a simulated ground truth for reference. As expected, larger voxels (i.e., coarser resolution) and increasing anisotropy results in increased deviations of both volume and shape measures, which is of particular relevance for small brain structures. Our findings clearly illustrate the anatomical inaccuracies introduced by the application of large and/or anisotropic voxels. To ensure deviations occur within the acceptable range (Dice coefficient scores; DCS > 0.75, corresponding to < 57% volume deviation), the volume of isotropic voxels should not exceed 5% of the total volume of the region of interest. When high accuracy is required (DCS > 0.90, corresponding to a < 19% volume deviation), the volumes of isotropic voxels should not exceed 0.08%, of the total volume. Finally, when large anisotropic factors (>3) are used, and the ellipsoid is orthogonal to the slice axes, having its long axis in the imaging plane, the voxel volume should not exceed 0.005% of the total volume. This allows sufficient compensation of anisotropy effects, in order to reach accuracy in the acceptable range (DCS > 0.75, corresponding to >57% volume deviation).

Dopamine transporter genotype conveys familial risk of attention-deficit/hyperactivity disorder through striatal activation.

OBJECTIVE: The dopamine transporter (DAT1) gene has been implicated in attention-deficit/hyperactivity disorder (ADHD), although the mechanism by which it exerts its effects remains unknown. The polymorphism associated with ADHD has been shown to affect expression of the transporter in vitro and in vivo. Dopamine transporters are predominantly expressed in the striatum, but also in the cerebellar vermis. Stimulant medication is often effective in ADHD and is believed to exert its effects by blocking dopamine transporters in the striatum. We set out to investigate the effect of the DAT1 genotype in ADHD in a small, preliminary study. We hypothesized that the DAT1 genotype would affect brain activation patterns in a manner similar to that of stimulant medication, with the lesser expressing allele mirroring its effects. METHOD: We investigated DAT1 gene effects on brain activation patterns in an all-male sample of sibling pairs discordant for ADHD (n = 20) and controls (n = 9). All of the subjects participated in a functional magnetic resonance imaging session using a go/no-go paradigm and provided a DNA sample for analysis. RESULTS: DAT1 genotype affected activation in the striatum and cerebellar vermis. The genotype interacted with familial risk of ADHD in the striatum but not the vermis. CONCLUSIONS: These preliminary results suggest that the DAT1 gene effects in the striatum are involved in translating the genetic risk of ADHD into a neurobiological substrate. As such, this study represents a first step in elucidating the neurobiological mechanisms underlying genetic influences in ADHD. Furthermore, these results may contribute to long-term possibilities for the development of new treatments: If the DAT1 genotype has differential effects on striatal activation, then it may be useful as a surrogate endpoint in individualized treatments targeting genotype/functional magnetic resonance imaging activation profiles.

Familial vulnerability to ADHD affects activity in the cerebellum in addition to the prefrontal systems.

OBJECTIVE: Familial vulnerability to attention-deficit/hyperactivity disorder (ADHD) has been shown to be related to atypical prefrontal activity during cognitive control tasks. However, ADHD is associated with deficits in the cerebellum as well as deficits in frontostriatal circuitry and associated cognitive control. In this study, we investigated whether cerebellar systems are sensitive to familial risk for ADHD in addition to frontostriatal circuitry. METHOD: We used an event-related, rapid mixed-trial functional magnetic resonance imaging design. The paradigm was a variation on a go/no-go task, with expected (go) and unexpected (no-go) events at expected and unexpected times. A total of 36 male children and adolescents completed the study, including 12 sibling pairs discordant for ADHD and 12 matched controls. RESULTS: Children and adolescents with ADHD were less accurate on unexpected events than control subjects. Performance by unaffected siblings was intermediate, between that of children and adolescents with ADHD and controls. Functional neuroimaging results showed dissociation between activation in the cerebellum and anterior cingulate cortex: Activity in the anterior cingulate cortex was decreased for subjects with ADHD and their unaffected siblings compared with controls for manipulations of stimulus type (no-go trials), but not timing. In contrast, cerebellar activity was decreased for subjects with ADHD and their unaffected siblings for manipulations of timing, but not stimulus type. CONCLUSIONS: These findings suggest that activity in both the prefrontal cortex and cerebellum is sensitive to familial vulnerability to ADHD. Unaffected siblings of individuals with ADHD show deficits similar to affected probands in prefrontal areas for unexpected events and in cerebellum for events atunexpected times.

Increased biohydrogen yields, volatile fatty acid production and substrate utilisation rates via the electrodialysis of a continually fed sucrose fermenter.

Electrodialysis (ED) removed volatile fatty acids (VFAs) from a continually-fed, hydrogen-producing fermenter. Simultaneously, electrochemical removal and adsorption removed gaseous H2 and CO2, respectively. Removing VFAs via ED in this novel process increased H2 yields by a factor of 3.75 from 0.24molH2mol-1hexose to 0.90molH2mol-1hexose. VFA production and substrate utilisation rates were consistent with the hypothesis that end product inhibition arrests H2 production. The methodology facilitated the recovery of 37g of VFAs, and 30L H2 that was more than 99% pure, both of which are valuable, energy dense chemicals. Typically, short hydraulic and solid retention times, and depressed pH levels are used to suppress methanogenesis, but this limits H2 production. To produce H2 from real world, low grade biomass containing complex carbohydrates, longer hydraulic retention times (HRTs) are required. The proposed system increased H2 yields via increased substrate utilisation over longer HRTs.

Maximising biohydrogen yields via continuous electrochemical hydrogen removal and carbon dioxide scrubbing.

The use of electrochemical hydrogen removal (EHR) together with carbon dioxide removal (CDR) was demonstrated for the first time using a continuous hydrogen producing fermenter. CDR alone was found to increase hydrogen yields from 0.07molH2molhexose to 0.72molH2molhexose. When CDR was combined with EHR, hydrogen yields increased further to 1.79molH2molhexose. The pattern of carbohydrate utilisation and volatile fatty acid (VFA) production are consistent with the hypothesis that increased yields are the result of relieving end product inhibition and inhibition of microbial hydrogen consumption. In situ removal of hydrogen and carbon dioxide as demonstrated here not only increase hydrogen yield but also produces a relatively pure product gas and unlike other approaches can be used to enhance conventional, mesophilic, CSTR type fermentation of low grade/high solids biomass.

Cortico-subthalamic connection predicts individual differences in value-driven choice bias.

It has been suggested that a connection between the STN and value-sensitive areas of the prefrontal cortex might mediate value-based actions in perceptual decision making. In this study, we first seek to quantify a structural connection between the STN and a cortical region that was associated with mechanisms underlying bias in choice behavior (vmPFC). Next, we tested whether individual differences in the probabilistic tract-strength of this connection were predictive for individual differences in the magnitude of bias in a perceptual decision-making task. Probabilistic tractography was used to measure the tract-strength between the STN and the vmPFC. Bias was quantified using an accumulation-to-bound model where a shift in the starting point of the accumulation of sensory evidence causes faster and more choices for an alternative that is more likely or more valuable. Results show that vmPFC is structurally connected with the STN and that the strength of this connection is predictive for choice bias towards an alternative that is more valuable, but not for choice bias towards an alternative that is more likely. These findings confirm the involvement of the cortico-subthalamic circuit in mechanisms underlying value-based actions in perceptual decision making.

Are accuracy and reaction time affected via different processes?

A recent study by van Ede et al. (2012) shows that the accuracy and reaction time in humans of tactile perceptual decisions are affected by an attentional cue via distinct cognitive and neural processes. These results are controversial as they undermine the notion that accuracy and reaction time are influenced by the same latent process that underlie the decision process. Typically, accumulation-to-bound models (like the drift diffusion model) can explain variability in both accuracy and reaction time by a change of a single parameter. To elaborate the findings of van Ede et al., we fitted the drift diffusion model to their behavioral data. Results show that both changes in accuracy and reaction time can be partly explained by an increase in the accumulation of sensory evidence (drift rate). In addition, a change in non-decision time is necessary to account for reaction time changes as well. These results provide a subtle explanation of how the underlying dynamics of the decision process might give rise to differences in both the speed and accuracy of perceptual tactile decisions. Furthermore, our analyses highlight the importance of applying a model-based approach, as the observed changes in the model parameters might be ecologically more valid, since they have an intuitive relationship with the neuronal processes underlying perceptual decision making.