We propose an integral concept for tractography to describe crossing and splitting fibre bundles based on the fibre orientation distribution function (ODF) estimated from high angular resolution diffusion imaging (HARDI). We show that in order to perform accurate probabilistic tractography, one needs to use a fibre ODF estimation and not the diffusion ODF. We use a new fibre ODF estimation obtained from a sharpening deconvolution transform (SDT) of the diffusion ODF reconstructed from q-ball imaging (QBI). This SDT provides new insight into the relationship between the HARDI signal, the diffusion ODF, and the fibre ODF. We demonstrate that the SDT agrees with classical spherical deconvolution and improves the angular resolution of QBI. Another important contribution of this paper is the development of new deterministic and new probabilistic tractography algorithms using the full multidirectional information obtained through use of the fibre ODF. An extensive comparison study is performed on human brain datasets comparing our new deterministic and probabilistic tracking algorithms in complex fibre crossing regions. Finally, as an application of our new probabilistic tracking, we quantify the reconstruction of transcallosal fibres intersecting with the corona radiata and the superior longitudinal fasciculus in a group of eight subjects. Most current diffusion tensor imaging (DTI)-based methods neglect these fibres, which might lead to incorrect interpretations of brain functions.

The human language faculty has been claimed to be grounded in the ability to process hierarchically structured sequences. This human ability goes beyond the capacity to process sequences with simple transitional probabilities of adjacent elements observable in non-human primates. Here we show that the processing of these two sequence types is supported by different areas in the human brain. Processing of local transitions is subserved by the left frontal operculum, a region that is phylogenetically older than Broca's area, which specifically holds responsible the computation of hierarchical dependencies. Tractography data revealing differential structural connectivity signatures for these two brain areas provide additional evidence for a segregation of two areas in the left inferior frontal cortex.

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

Recent findings in neuroscience suggest that adult brain structure changes in response to environmental alterations and skill learning. Whereas much is known about structural changes after intensive practice for several months, little is known about the effects of single practice sessions on macroscopic brain structure and about progressive (dynamic) morphological alterations relative to improved task proficiency during learning for several weeks. Using T1-weighted and diffusion tensor imaging in humans, we demonstrate significant gray matter volume increases in frontal and parietal brain areas following only two sessions of practice in a complex whole-body balancing task. Gray matter volume increase in the prefrontal cortex correlated positively with subject's performance improvements during a 6 week learning period. Furthermore, we found that microstructural changes of fractional anisotropy in corresponding white matter regions followed the same temporal dynamic in relation to task performance. The results make clear how marginal alterations in our ever changing environment affect adult brain structure and elucidate the interrelated reorganization in cortical areas and associated fiber connections in correlation with improvements in task performance.

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

When people make decisions they often face opposing demands for response speed and response accuracy, a process likely mediated by response thresholds. According to the striatal hypothesis, people decrease response thresholds by increasing activation from cortex to striatum, releasing the brain from inhibition. According to the STN hypothesis, people decrease response thresholds by decreasing activation from cortex to subthalamic nucleus (STN); a decrease in STN activity is likewise thought to release the brain from inhibition and result in responses that are fast but error-prone. To test these hypotheses-both of which may be true-we conducted two experiments on perceptual decision making in which we used cues to vary the demands for speed vs. accuracy. In both experiments, behavioral data and mathematical model analyses confirmed that instruction from the cue selectively affected the setting of response thresholds. In the first experiment we used ultra-high-resolution 7T structural MRI to locate the STN precisely. We then used 3T structural MRI and probabilistic tractography to quantify the connectivity between the relevant brain areas. The results showed that participants who flexibly change response thresholds (as quantified by the mathematical model) have strong structural connections between presupplementary motor area and striatum. This result was confirmed in an independent second experiment. In general, these findings show that individual differences in elementary cognitive tasks are partly driven by structural differences in brain connectivity. Specifically, these findings support a cortico-striatal control account of how the brain implements adaptive switches between cautious and risky behavior.basal ganglia | response time model | speed-accuracy tradeoff | structural connectivity | subthalamic nucleus F or many everyday life decisions, people and animals face the dilemma that fast decisions tend to be error-prone, whereas accurate decisions tend to be relatively slow. In other words, the temporal benefits of responding quickly come at a cost of increased error rates, a phenomenon known as the speed-accuracy tradeoff (SAT) (1-6).Even though the SAT is ubiquitous in many areas of decision making, relatively little is known about its neurobiological underpinnings. Currently available empirical data (2, 7, 8) and neurocomputational models both suggest several brain mechanisms that could be responsible for how people switch from cautious behavior that is accurate but slow to risky behavior that is fast but errorprone (1). The work presented here is relevant for two hypotheses about how the brain controls the SAT (Fig. 1). First, the striatal hypothesis posits that an emphasis on speed promotes excitatory input from cortex to striatum; the increased baseline activation of the striatum acts to decrease the inhibitory control that the output nuclei of the basal ganglia exert over the brain, thereby facilitating faster but possibly premature responses (2). Second, the STN hypothesis posits that an emphasis on ...

The ability to learn language is a human trait. In adults and children, brain imaging studies have shown that auditory language activates a bilateral frontotemporal network with a left hemispheric dominance. It is an open question whether these activations represent the complete neural basis for language present at birth. Here we demonstrate that in 2-d-old infants, the language-related neural substrate is fully active in both hemispheres with a preponderance in the right auditory cortex. Functional and structural connectivities within this neural network, however, are immature, with strong connectivities only between the two hemispheres, contrasting with the adult pattern of prevalent intrahemispheric connectivities. Thus, although the brain responds to spoken language already at birth, thereby providing a strong biological basis to acquire language, progressive maturation of intrahemispheric functional connectivity is yet to be established with language exposure as the brain develops

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.DTI ͉ fMRI ͉ hierarchical structure L anguage appears to be a trait specific to humans-at least in its core computational component, that is, grammar. Defining language as a sequence of symbols, Chomsky (1) proposed a hierarchy of grammars as language production mechanisms with increasing generative powers. The lowest-level grammar is finite state grammar (FSG). FSG can be fully specified by transition probabilities between a finite number of states (e.g., words), being not powerful enough to generate structures of natural human languages. Phrase structure grammar (PSG) has more generative power than FSG. A key difference between FSG and PSG is that only PSG can generate the sequence A n B n , where A and B denote symbols and n the number of repetitions. The ability to process the sequence A n B n is crucial for the processing of center-embedded sentences, such as ''The man the boy the dog bit greeted is my friend.'' where subjects (i.e., the man, the boy, and the dog) are A-symbols and the verbs (bit, greeted, and is) are B-symbols. Surprisingly, tests on monkeys (2) and on songbirds (3) showed that whereas songbirds can process A n B n sequences, monkeys cannot. However, even if the birds could correctly discriminate A n B n sequences from A n B m , (4 Ͼ n, m Ͼ0, n m),...