Search

Brain responses to food are thought to reflect food's rewarding value and to fluctuate with dietary restraint. We propose that brain responses to food are dynamic and depend on attentional focus. Food pictures (high-caloric/low-caloric, palatable/unpalatable) were presented during fMRI-scanning, while attentional focus (hedonic/health/neutral) was induced in 52 female participants varying in dietary restraint. The level of brain activity was hardly different between palatable versus unpalatable foods or high-caloric versus low-caloric foods. Activity in several brain regions was higher in hedonic than in health or neutral attentional focus (p < .05, FWE-corrected). Palatability and calorie content could be decoded from multi-voxel activity patterns (p < .05, FDR-corrected). Dietary restraint did not significantly influence brain responses to food. So, level of brain activity in response to food stimuli depends on attentional focus, and may reflect salience, not reward value. Palatability and calorie content are reflected in patterns of brain activity., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interest., (Copyright © 2023. Published by Elsevier Inc.)

Obesity reached pandemic proportions and weight-loss treatments are mostly ineffective. The level of brain activity in the reward circuitry is proposed to be proportionate to the reward value of food stimuli, and stronger in people with obesity. However, empirical evidence is inconsistent. This may be due to the double-sided nature of high caloric palatable foods: at once highly palatable and high in calories (unhealthy). This study hypothesizes that, viewing high caloric palatable foods, a hedonic attentional focus compared to a health and a neutral attentional focus elicits more activity in reward-related brain regions, mostly in people with obesity. Moreover, caloric content and food palatability can be decoded from multivoxel patterns of activity most accurately in people with obesity and in the corresponding attentional focus. During one fMRI-session, attentional focus (hedonic, health, neutral) was manipulated using a one-back task with individually tailored food stimuli in 32 healthy-weight people and 29 people with obesity. Univariate analyses (p < 0.05, FWE-corrected) showed that brain activity was not different for palatable vs. unpalatable foods, nor for high vs. low caloric foods. Instead, this was higher in the hedonic compared to the health and neutral attentional focus. Multivariate analyses (MVPA) (p < 0.05, FDR-corrected) showed that palatability and caloric content could be decoded above chance level, independently of either BMI or attentional focus. Thus, brain activity to visual food stimuli is neither proportionate to the reward value (palatability and/or caloric content), nor significantly moderated by BMI. Instead, it depends on people's attentional focus, and may reflect motivational salience. Furthermore, food palatability and caloric content are represented as patterns of brain activity, independently of BMI and attentional focus. So, food reward value is reflected in patterns, not levels, of brain activity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The nucleus basalis of Meynert (nbM) is the major source of cortical acetylcholine (ACh) and has been related to cognitive processes and to neurological disorders. However, spatially delineating the human nbM in MRI studies remains challenging. Due to the absence of a functional localiser for the human nbM, studies to date have localised it using nearby neuroanatomical landmarks or using probabilistic atlases. To understand the feasibility of MRI of the nbM we set our four goals; our first goal was to review current human nbM region-of-interest (ROI) selection protocols used in MRI studies, which we found have reported highly variable nbM volume estimates. Our next goal was to quantify and discuss the limitations of existing atlas-based volumetry of nbM. We found that the identified ROI volume depends heavily on the atlas used and on the probabilistic threshold set. In addition, we found large disparities even for data/studies using the same atlas and threshold. To test whether spatial resolution contributes to volume variability, as our third goal, we developed a novel nbM mask based on the normalized BigBrain dataset. We found that as long as the spatial resolution of the target data was 1.3 mm isotropic or above, our novel nbM mask offered realistic and stable volume estimates. Finally, as our last goal we tried to discern nbM using publicly available and novel high resolution structural MRI ex vivo MRI datasets. We find that, using an optimised 9.4T quantitative T 2 ⁎ ex vivo dataset, the nbM can be visualised using MRI. We conclude caution is needed when applying the current methods of mapping nbM, especially for high resolution MRI data. Direct imaging of the nbM appears feasible and would eliminate the problems we identify, although further development is required to allow such imaging using standard (f)MRI scanning., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

Vocal flexibility is a hallmark of the human species, most particularly the capacity to speak and sing. This ability is supported in part by the evolution of a direct neural pathway linking the motor cortex to the brainstem nucleus that controls the larynx the primary sound source for communication. Early brain imaging studies demonstrated that larynx motor cortex at the dorsal end of the orofacial division of motor cortex (dLMC) integrated laryngeal and respiratory control, thereby coordinating two major muscular systems that are necessary for vocalization. Neurosurgical studies have since demonstrated the existence of a second larynx motor area at the ventral extent of the orofacial motor division (vLMC) of motor cortex. The vLMC has been presumed to be less relevant to speech motor control, but its functional role remains unknown. We employed a novel ultra-high field (7T) magnetic resonance imaging paradigm that combined singing and whistling simple melodies to localise the larynx motor cortices and test their involvement in respiratory motor control. Surprisingly, whistling activated both 'larynx areas' more strongly than singing despite the reduced involvement of the larynx during whistling. We provide further evidence for the existence of two larynx motor areas in the human brain, and the first evidence that laryngeal-respiratory integration is a shared property of both larynx motor areas. We outline explicit predictions about the descending motor pathways that give these cortical areas access to both the laryngeal and respiratory systems and discuss the implications for the evolution of speech., (Copyright © 2021. Published by Elsevier Inc.)

Background: The emerging field of ultra-high field MRI (UHF-MRI, 7 Tesla and higher) provides the opportunity to image human brains at a higher resolution and with higher signal-to-noise ratios compared to the more widely available 1.5 and 3T scanners. Scanning postmortem tissue additionally allows for greatly increased scan times and fewer movement issues leading to improvements in image quality. However, typical postmortem neuroimaging routines involve placing the tissue within plastic bags that leave room for susceptibility artifacts from tissue-air interfaces, inadequate submersion, and leakage issues. To address these challenges in postmortem imaging, a custom-built nonferromagnetic container was developed that allows whole brain hemispheres to be scanned at sub-millimeter resolution within typical head-coils., Method: The custom-built polymethylmethacrylaat container consists of a cylinder with a hemispheric side and a lid with valves on the adjacent side. This shape fits within common MR head-coils and allows whole hemispheres to be submerged and vacuum sealed within it reducing imaging artifacts that would otherwise arise at air-tissue boundaries. Two hemisphere samples were scanned on a Siemens 9.4T Magnetom MRI scanner. High resolution T2* weighted data was obtained with a custom 3D gradient echo (GRE) sequence and diffusion-weighted imaging (DWI) scans were obtained with a 3D kT-dSTEAM sequence along 48 directions., Results: The custom-built container proved to submerge and contain tissue samples effectively and showed no interferences with MR scanning acquisition. The 3D GRE sequence provided high resolution isotropic T2* weighted data at 250 μm which showed a clear visualization of gray and white matter structures. DWI scans allowed for dense reconstruction of structural white matter connections via tractography., Conclusion: Using this custom-built container worked towards achieving high quality MR images of postmortem brain material. This procedure can have advantages over traditional schemes including utilization of a standardized protocol and the reduced likelihood of leakage. This methodology could be adjusted and used to improve typical postmortem imaging routines., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Research investigating neural responses to visual food stimuli has produced inconsistent results. Crucially, high-caloric palatable foods have a double-sided nature - they are often craved but are also considered unhealthy - which may have contributed to the inconsistency in the literature. Taking this double-sided nature into account in the current study, neural responses to individually tailored palatable and unpalatable high caloric food stimuli were measured, while participants' (females with overweight: n = 23) attentional focus was manipulated to be either hedonic or neutral. Notably, results showed that the level of neural activity was not significantly different for palatable than for unpalatable food stimuli. Instead, independent of food palatability, several brain regions (including regions in the mesocorticolimbic system) responded more strongly when attentional focus was hedonic than when neutral (p < 0.05, cluster-based FWE corrected). Multivariate analyses showed that food palatability could be decoded from multi-voxel patterns of neural activity (p < 0.05, FDR corrected), mostly with a hedonic attentional focus. These findings illustrate that the level of neural activity might not be proportionate to the palatability of foods, but that food palatability can be decoded from multi-voxel patterns of neural activity. Moreover, they underline the importance of considering attentional focus when measuring food-related neural responses., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Deep brain stimulation of the subthalamic nucleus (STN) is a widely performed surgical treatment for patients with Parkinson's disease. The goal of the surgery is to place an electrode centered in the motor region of the STN while lowering the effects of electrical stimulation on the non-motor regions. However, distinguishing the motor region from the neighboring associative and limbic areas in individual patients using imaging modalities was until recently difficult to obtain in vivo. Here, using ultra-high field MR imaging, we have performed a dissection of the subdivisions of the STN of individual Parkinson's disease patients. We have acquired 7T diffusion-weighted images of seventeen patients with Parkinson's disease scheduled for deep brain stimulation surgery. Using a structural connectivity-based parcellation protocol, the STN's connections to the motor, limbic, and associative cortical areas were used to map the individual subdivisions of the nucleus. A reproducible patient-specific parcellation of the STN into a posterolateral motor and gradually overlapping central associative area was found in all STNs, taking up on average 55.3% and 55.6% of the total nucleus volume. The limbic area was found in the anteromedial part of the nucleus. Our results suggest that 7T MR imaging may facilitate individualized and highly specific planning of deep brain stimulation surgery of the STN., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Effective connectivity is commonly assessed using blood oxygenation level-dependent (BOLD) signals. In (Havlicek et al., 2015), we presented a novel, physiologically informed dynamic causal model (P-DCM) that extends current generative models. We demonstrated the improvements afforded by P-DCM in terms of the ability to model commonly observed neuronal and vascular transients in single regions. Here, we assess the ability of the novel and previous DCM variants to estimate effective connectivity among a network of five ROIs driven by a visuo-motor task. We demonstrate that connectivity estimates depend sensitively on the DCM used, due to differences in the modeling of hemodynamic response transients; such as the post-stimulus undershoot or adaptation during stimulation. In addition, using a novel DCM for arterial spin labeling (ASL) fMRI that measures BOLD and CBF signals simultaneously, we confirmed our findings (by using the BOLD data alone and in conjunction with CBF). We show that P-DCM provides better estimates of effective connectivity, regardless of whether it is applied to BOLD data alone or to ASL time-series, and that all new aspects of P-DCM (i.e. neuronal, neurovascular, hemodynamic components) constitute an improvement compared to those in the previous DCM variants. In summary, (i) accurate modeling of fMRI response transients is crucial to obtain valid effective connectivity estimates and (ii) any additional hemodynamic data, such as provided by ASL, increases the ability to disambiguate neuronal and vascular effects present in the BOLD signal., (Copyright © 2017 Elsevier Inc. All rights reserved.)

A comprehensive tract-based characterisation of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organisation within the voxel. Recently, a new experimental framework that combines inversion recovery and diffusion MRI, called inversion recovery diffusion tensor imaging (IR-DTI), was introduced and applied in an animal study. IR-DTI provides the ability to assign to each unique fibre population within a voxel a specific value of the longitudinal relaxation time, T1, which is a proxy for myelin content. Here, we apply the IR-DTI approach to the human brain in vivo on 7 healthy subjects for the first time. We demonstrate that the approach is able to measure differential tract properties in crossing fibre areas, reflecting the different myelination of tracts. We also show that tract-specific T1 has less inter-subject variability compared to conventional T1 in areas of crossing fibres, suggesting increased specificity to distinct fibre populations. Finally we show in simulations that changes in myelination selectively affecting one fibre bundle in crossing fibre areas can potentially be detected earlier using IR-DTI., (Copyright © 2016 The Authors Elsevier Inc. Published by Elsevier Inc. All rights reserved.)

Axonal density and diameter are two fundamental properties of brain white matter. Recently, advanced diffusion MRI techniques have made these two parameters accessible in vivo. However, the techniques available to estimate such parameters are still under development. For example, current methods to map axonal diameters capture relative trends over different structures, but consistently over-estimate absolute diameters. Axonal density estimates are more accessible experimentally, but different modeling approaches exist and the impact of the experimental parameters has not been thoroughly quantified, potentially leading to incompatibility of results obtained in different studies using different techniques. Here, we characterise the impact of diffusion time on axonal density and diameter estimates using Monte Carlo simulations and STEAM diffusion MRI at 7 T on 9 healthy volunteers. We show that axonal density and diameter estimates strongly depend on diffusion time, with diameters almost invariably overestimated and density both over and underestimated for some commonly used models. Crucially, we also demonstrate that these biases are reduced when the model accounts for diffusion time dependency in the extra-axonal space. For axonal density estimates, both upward and downward bias in different situations are removed by modeling extra-axonal time-dependence, showing increased accuracy in these estimates. For axonal diameter estimates, we report increased accuracy in ground truth simulations and axonal diameter estimates decreased away from high values given by earlier models and towards known values in the human corpus callosum when modeling extra-axonal time-dependence. Axonal diameter feasibility under both advanced and clinical settings is discussed in the light of the proposed advances., (Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.)

The functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses - such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal process and offer new ways of inferring changes in local neuronal activity and effective connectivity from fMRI., (Copyright © 2015. Published by Elsevier Inc.)

Previous studies have shown that one's brain response to high-calorie food cues can predict long-term weight gain or weight loss. The neural correlates that predict food intake in the short term have, however, hardly been investigated. This study examined which brain regions' activation predicts chocolate intake after participants had been either exposed to real chocolate or to control stimuli during approximately one hour, with interruptions for fMRI measurements. Further we investigated whether the variance in chocolate intake could be better explained by activated brain regions than by self-reported craving. In total, five brain regions correlated with subsequent chocolate intake. The activation of two reward regions (the right caudate and the left frontopolar cortex) correlated positively with intake in the exposure group. The activation of two regions associated with cognitive control (the left dorsolateral and left mid-dorsolateral PFC) correlated negatively with intake in the control group. When the regression analysis was conducted with the exposure and the control group together, an additional region's activation (the right anterior PFC) correlated positively with chocolate intake. In all analyses, the intake variance explained by neural correlates was above and beyond the variance explained by self-reported craving. These results are in line with neuroimaging research showing that brain responses are a better predictor of subsequent intake than self-reported craving. Therefore, our findings might provide for a missing link by associating brain activation, previously shown to predict weight change, with short-term intake., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Multimodal neuroimaging has become a mainstay of basic and cognitive neuroscience in humans and animals, despite challenges to consider when acquiring and combining non-redundant imaging data. Multimodal data integration can yield important insights into brain processes and structures in addition to spatiotemporal resolution complementarity, including: a comprehensive physiological view on brain processes and structures, quantification, generalization and normalization, and availability of biomarkers. In this review, we discuss data acquisition and fusion in multimodal neuroimaging in the context of each of these potential merits. However, limitations - due to differences in the neuronal and structural underpinnings of each method - have to be taken into account when modeling and interpreting multimodal data using generative models. We conclude that when these challenges are adequately met, multimodal data fusion can create substantial added value for neuroscience applications making it an indispensable approach for studying the brain., (Copyright © 2014. Published by Elsevier Inc.)

Reconstructing the macroscopic human cortical connectome by Diffusion Weighted Imaging (DWI) is a challenging research topic that has recently gained a lot of attention. In the present work, we investigate the effects of intra-voxel fiber direction modeling and tractography algorithm on derived structural network indices (e.g. density, small-worldness and global efficiency). The investigation is centered on three semi-independent distinctions within the large set of available diffusion models and tractography methods: i) single fiber direction versus multiple directions in the intra-voxel diffusion model, ii) deterministic versus probabilistic tractography and iii) local versus global measure-of-fit of the reconstructed fiber trajectories. The effect of algorithm and parameter choice has two components. First, there is the large effect of tractography algorithm and parameters on global network density, which is known to strongly affect graph indices. Second, and more importantly, there are remaining effects on graph indices which range in the tens of percent even when global density is controlled for. This is crucial for the sensitivity of any human structural network study and for the validity of study comparisons. We then investigate the effect of the choice of tractography algorithm on sensitivity and specificity of the resulting connections with a connectome dissection quality control (QC) approach. In this approach, evaluation of Tract Specific Density Coefficients (TSDCs) measures sensitivity while careful inspection of tractography path results assesses specificity. We use this to discuss interactions in the combined effects of these methods and implications for future studies., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Twenty years ago, the discovery of the blood oxygen level dependent (BOLD) contrast and invention of functional magnetic resonance imaging (MRI) not only allowed for enhanced analyses of regional brain activity, but also laid the foundation for novel approaches to studying effective connectivity, which is essential for mechanistically interpretable accounts of neuronal systems. Dynamic causal modeling (DCM) and Granger causality (G-causality) modeling have since become the most frequently used techniques for inferring effective connectivity from fMRI data. In this paper, we provide a short historical overview of these approaches, describing milestones of their development from our subjective perspectives., (Copyright © 2012 Elsevier Inc. All rights reserved.)

The premise of cognitive therapy is that one can overcome the irresistible temptation of highly palatable foods by actively restructuring the way one thinks about food. Testing this idea, participants in the present study were instructed to passively view foods, up-regulate food palatability thoughts, apply cognitive reappraisal (e.g., thinking about health consequences), or suppress food palatability thoughts and cravings. We examined whether these strategies affect self-reported food craving and mesocorticolimbic activity as assessed by functional magnetic resonance imaging. It was hypothesized that cognitive reappraisal would most effectively inhibit the mesocorticolimbic activity and associated food craving as compared to suppression. In addition, it was hypothesized that suppression would lead to more prefrontal cortex activity, reflecting the use of more control resources, as compared to cognitive reappraisal. Self-report results indicated that up-regulation increased food craving compared to the other two conditions, but that there was no difference in craving between the suppression and cognitive reappraisal strategy. Corroborating self-report results, the neuroimaging results showed that up-regulation increased activity in important regions of the mesocorticolimbic circuitry, including the ventral tegmental area, ventral striatum, operculum, posterior insular gyrus, medial orbitofrontal cortex and ventromedial prefrontal cortex. Contrary to our hypothesis, suppression more effectively decreased activity in the core of the mesocorticolimbic circuitry (i.e., ventral tegmental area and ventral striatum) compared to cognitive reappraisal. Overall, the results support the contention that appetitive motivation can be modulated by the application of short-term cognitive control strategies., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Imagination is a key function for many human activities, such as reminiscing, learning, or planning. Unravelling its neuro-biological basis is paramount to grasp the essence of our thoughts. Previous neuroimaging studies have identified brain regions subserving the visualisation of "what?" (e.g. faces or objects) and "where?" (e.g. spatial layout) content of mental images. However, the functional role of a common set of involved regions - the frontal regions - and their interplay with the "what" and "where" regions, has remained largely unspecified. This study combines functional MRI and electroencephalography to examine the full-brain network that underlies the visual imagery of complex scenes and to investigate the spectro-temporal properties of its nodes, especially of the frontal cortex. Our results indicate that frontal regions integrate the "what" and "where" content of our thoughts into one visually imagined scene. We link early synchronisation of anterior theta and beta oscillations to regional activation of right and central frontal cortices, reflecting retrieval and integration of information. These frontal regions orchestrate remote occipital-temporal regions (including calcarine sulcus and parahippocampal gyrus) that encode the detailed representations of the objects, and parietal "where" regions that encode the spatial layout into forming one coherent mental picture. Specifically the mesial superior frontal gyrus appears to have a principal integrative role, as its activity during the visualisation of the scene predicts subsequent performance on the imagery task., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Functional magnetic resonance imaging (fMRI) is increasingly used to study functional connectivity in large-scale brain networks that support cognitive and perceptual processes. We face serious conceptual, statistical and data analysis challenges when addressing the combinatorial explosion of possible interactions within high-dimensional fMRI data. Moreover, we need to know, and account for, the physiological mechanisms underlying our signals. We argue here that (i) model selection procedures for connectivity should include consideration of more than just a few brain structures, (ii) temporal precedence - and causality concepts based on it - are essential in dynamic models of connectivity and (iii) undoing the effect of hemodynamics on fMRI data (by deconvolution) can be an important tool. However, it is crucially dependent upon assumptions that need to be verified., (Copyright © 2009 Elsevier Inc. All rights reserved.)

This is the final paper in a Comments and Controversies series dedicated to "The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution". We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener-Akaike-Granger-Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Visual imagery--similar to visual perception--activates feature-specific and category-specific visual areas. This is frequently observed in experiments where the instruction is to imagine stimuli that have been shown immediately before the imagery task. Hence, feature-specific activation could be related to the short-term memory retrieval of previously presented sensory information. Here, we investigated mental imagery of stimuli that subjects had not seen before, eliminating the effects of short-term memory. We recorded brain activation using fMRI while subjects performed a behaviourally controlled guided imagery task in predefined retinotopic coordinates to optimize sensitivity in early visual areas. Whole brain analyses revealed activation in a parieto-frontal network and lateral-occipital cortex. Region of interest (ROI) based analyses showed activation in left hMT/V5+. Granger causality mapping taking left hMT/V5+ as source revealed an imagery-specific directed influence from the left inferior parietal lobule (IPL). Interestingly, we observed a negative BOLD response in V1-3 during imagery, modulated by the retinotopic location of the imagined motion trace. Our results indicate that rule-based motion imagery can activate higher-order visual areas involved in motion perception, with a role for top-down directed influences originating in IPL. Lower-order visual areas (V1, V2 and V3) were down-regulated during this type of imagery, possibly reflecting inhibition to avoid visual input from interfering with the imagery construction. This suggests that the activation in early visual areas observed in previous studies might be related to short- or long-term memory retrieval of specific sensory experiences.

The optic chiasm with its complex fiber micro-structure is a challenge for diffusion tensor models and tractography methods. Likewise, it is an ideal candidate for evaluation of diffusion tensor imaging tractography approaches in resolving inter-regional connectivity because the macroscopic connectivity of the optic chiasm is well known. Here, high-resolution (156 microm in-plane) diffusion tensor imaging of the human optic chiasm was performed ex vivo at ultra-high field (9.4 T). Estimated diffusion tensors at this high resolution were able to capture complex fiber configurations such as sharp curves, and convergence and divergence of tracts, but were unable to resolve directions at sites of crossing fibers. Despite the complex microstructure of the fiber paths through the optic chiasm, all known connections could be tracked by a line propagation algorithm. However, fibers crossing from the optic nerve to the contralateral tract were heavily underrepresented, whereas ipsilateral nerve-to-tract connections, as well as tract-to-tract connections, were overrepresented, and erroneous nerve-to-nerve connections were tracked. The effects of spatial resolution and the varying degrees of partial volume averaging of complex fiber architecture on the performance of these methods could be investigated. Errors made by the tractography algorithm at high resolution were shown to increase at lower resolutions closer to those used in vivo. This study shows that increases in resolution, made possible by higher field strengths, improve the accuracy of DTI-based tractography. More generally, post-mortem investigation of fixed tissue samples with diffusion imaging at high field strengths is important in the evaluation of MR-based diffusion models and tractography algorithms.

We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity.