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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.

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.

•The structural language network is modulated by the specific procesing requirements of one's native language.•Native speakers of a language with complex syntax (German) show stronger connectivity in the intra-hemispheric frontal-parietal/-temporal language network.•Native speakers of a root-based language (Arabic) show stronger connectivity in the temporo-parietal lexical-semantic network and in inter-hemispheric connections. Is the neuroanatomy of the language structural connectome modulated by the life-long experience of speaking a specific language? The current study compared the brain white matter connections of the language and speech production network in a large cohort of 94 native speakers of two very different languages: an Indo-European morphosyntactically complex language (German) and a Semitic root-based language (Arabic). Using high-resolution diffusion-weighted MRI and tractography-based network statistics of the language connectome, we demonstrated that German native speakers exhibited stronger connectivity in an intra-hemispheric frontal to parietal/temporal dorsal language network, known to be associated with complex syntax processing. In comparison, Arabic native speakers showed stronger connectivity in the connections between semantic language regions, including the left temporo-parietal network, and stronger inter-hemispheric connections via the posterior corpus callosum connecting bilateral superior temporal and inferior parietal regions. The current study suggests that the structural language connectome develops and is modulated by environmental factors such as the characteristic processing demands of the native language. [Display omitted]

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.

Neuroimaging and transcranial magnetic stimulation provide insights into the neuronal mechanisms underlying speech disfluencies in chronic persistent stuttering. In the present paper, the goal is not to provide an exhaustive review of existing literature, but rather to highlight robust findings. We, therefore, conducted a meta-analysis of diffusion tensor imaging studies which have recently implicated disrupted white matter connectivity in stuttering. A reduction of fractional anisotropy in persistent stuttering has been reported at several different loci. Our meta-analysis revealed consistent deficits in the left dorsal stream and in the interhemispheric connections between the sensorimotor cortices. In addition, recent fMRI meta-analyses link stuttering to reduced left fronto-parieto-temporal activation while greater fluency is associated with boosted co-activations of right fronto-parieto-temporal areas. However, the physiological foundation of these irregularities is not accessible with MRI. Complementary, transcranial magnetic stimulation (TMS) reveals local excitatory and inhibitory regulation of cortical dynamics. Applied to a speech motor area, TMS revealed reduced speech-planning-related neuronal dynamics at the level of the primary motor cortex in stuttering. Together, this review provides a focused view of the neurobiology of stuttering to date and may guide the rational design of future research. This future needs to account for the perpetual dynamic interactions between auditory, somatosensory, and speech motor circuits that shape fluent speech.

Diffusion MRI (dMRI) measurements are used for inferring the microstructural properties of white matter and to reconstruct fiber pathways. Very often voxels contain complex fiber configurations comprising multiple bundles, rendering the simple diffusion tensor model unsuitable. Multi-compartment models deliver a convenient parameterization of the underlying complex fiber architecture, but pose challenges for fitting and model selection. Spherical deconvolution, in contrast, very economically produces a fiber orientation density function (fODF) without any explicit model assumptions. Since, however, the fODF is represented by spherical harmonics, a direct interpretation of the model parameters is impossible. Based on the fact that the fODF can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle), we offer a solution that seeks to combine the advantages of both approaches: first the fiber configuration is modeled as fODF represented by spherical harmonics and then each of the peaks is parameterized separately in order to characterize the underlying bundle. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second-order statistics of the fiber orientations, from which we derive metrics for the parametric quantification of fiber bundles. We propose meaningful relationships between these measures and the underlying microstructural properties. We focus on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. We compare these metrics to the conventionally used fractional anisotropy (FA) and show how they may help to increase the specificity of the characterization of microstructural properties. While metrics relying on the first moments of the Bingham distributions provide relatively robust results, second-order metrics representing the peak spread are only meaningful, if the SNR is very high and no fiber crossings are present in the voxel.

Human language is supported by a cortical network involving Broca’s area, which comprises Brodmann Areas 44 and 45 (BA44 and BA45). While cytoarchitectonic homolog areas have been identified in nonhuman primates, it remains unknown how these regions evolved to support human language. Here, we use histological data and advanced cortical registration methods to precisely compare the morphology of BA44 and BA45 in humans and chimpanzees. We found a general expansion of Broca’s areas in humans, with the left BA44 enlarging the most, growing anteriorly into a region known to process syntax. Together with recent functional and receptorarchitectural studies, our findings support the conclusion that BA44 evolved from an action-related region to a bipartite system, with a posterior portion supporting action and an anterior portion supporting syntactic processes. Our findings add novel insights to the longstanding debate on the relationship between language and action, and the evolution of Broca’s area.

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.

The flexible and efficient adaptation to dynamic, rapid changes in the auditory environment likely involves generating and updating of internal models. Such models arguably exploit connections between the neocortex and the cerebellum, supporting proactive adaptation. Here, we tested whether temporo-cerebellar disconnection is associated with the processing of sound at short timescales. First, we identify lesion-specific deficits for the encoding of short timescale spectro-temporal non-speech and speech properties in patients with left posterior temporal cortex stroke. Second, using lesion-guided probabilistic tractography in healthy participants, we revealed bidirectional temporo-cerebellar connectivity with cerebellar dentate nuclei and crura I/II. These findings support the view that the encoding and modeling of rapidly modulated auditory spectro-temporal properties can rely on a temporo-cerebellar interface. We discuss these findings in view of the conjecture that proactive adaptation to a dynamic environment via internal models is a generalizable principle.

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide – one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.