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Fast saccades are rapid automatic oculomotor responses to salient and ecologically important visual stimuli such as animals and faces. Discriminating the number of friends, foe, or prey may also have an evolutionary advantage. In this study, participants were asked to saccade rapidly towards the more numerous of two arrays. Participants could discriminate numerosities with high accuracy and great speed, as fast as 190 ms. Intermediate numerosities were more likely to elicit fast saccades than very low or very high numerosities. Reaction-times for vocal responses (collected in a separate experiment) were slower, did not depend on numerical range, and correlated only with the slow not the fast saccades, pointing to different systems. The short saccadic reaction-times we observe are surprising given that discrimination using numerosity estimation is thought to require a relatively complex neural circuit, with several relays of information through the parietal and prefrontal cortex. Our results suggest that fast numerosity-driven saccades may be generated on a single feed-forward pass of information recruiting a primitive system that cuts through the cortical hierarchy and rapidly transforms the numerosity information into a saccade command.

Multimodal evidence suggests that brain regions accumulate information over timescales that vary according to anatomical hierarchy. Thus, these experimentally defined “temporal receptive windows” are longest in cortical regions that are distant from sensory input. Interestingly, spontaneous activity in these regions also plays out over relatively slow timescales (i.e., exhibits slower temporal autocorrelation decay). These findings raise the possibility that hierarchical timescales represent an intrinsic organizing principle of brain function. Here, using resting-state functional MRI, we show that the timescale of ongoing dynamics follows hierarchical spatial gradients throughout human cerebral cortex. These intrinsic timescale gradients give rise to systematic frequency differences among large-scale cortical networks and predict individual-specific features of functional connectivity. Whole-brain coverage permitted us to further investigate the large-scale organization of subcortical dynamics. We show that cortical timescale gradients are topographically mirrored in striatum, thalamus, and cerebellum. Finally, timescales in the hippocampus followed a posterior-to-anterior gradient, corresponding to the longitudinal axis of increasing representational scale. Thus, hierarchical dynamics emerge as a global organizing principle of mammalian brains.

Development and aging of the cerebral cortex show similar topographic organization and are governed by the same genes. It is unclear whether the same is true for subcortical regions, which follow fundamentally different ontogenetic and phylogenetic principles. We tested the hypothesis that genetically governed neurodevelopmental processes can be traced throughout life by assessing to which degree brain regions that develop together continue to change together through life. Analyzing over 6000 longitudinal MRIs of the brain, we used graph theory to identify five clusters of coordinated development, indexed as patterns of correlated volumetric change in brain structures. The clusters tended to follow placement along the cranial axis in embryonic brain development, suggesting continuity from prenatal stages, and correlated with cognition. Across independent longitudinal datasets, we demonstrated that developmental clusters were conserved through life. Twin-based genetic correlations revealed distinct sets of genes governing change in each cluster. Single nucleotide polymorphisms-based analyses of 38127 cross-sectional MRIs showed a similar pattern of genetic volume-volume correlations. In conclusion, coordination of subcortical change adheres to fundamental principles of lifespan continuity and genetic organization.

•We present the Subcortical Atlas of the Rhesus Macaque (SARM).•Digital maps at multiple spatial scales allow for flexible analysis and labeling.•The maps are based on 210 primary regions-of-interest (ROIs) and composites thereof.•SARM facilitates subcortical neuroimaging analysis and experimental planning.•SARM is in the NMT v2 template space and complements the CHARM atlas for the cortex. Digitized neuroanatomical atlases that can be overlaid onto functional data are crucial for localizing brain structures and analyzing functional networks identified by neuroimaging techniques. To aid in functional and structural data analysis, we have created a comprehensive parcellation of the rhesus macaque subcortex using a high-resolution ex vivo structural imaging scan. This anatomical scan and its parcellation were warped to the updated NIMH Macaque Template (NMT v2), an in vivo population template, where the parcellation was refined to produce the Subcortical Atlas of the Rhesus Macaque (SARM) with 210 primary regions-of-interest (ROIs). The subcortical parcellation and nomenclature reflect those of the 4th edition of the Rhesus Monkey Brain in Stereotaxic Coordinates (Paxinos et al., in preparation), rather than proposing yet another novel atlas. The primary ROIs are organized across six spatial hierarchical scales from small, fine-grained ROIs to broader composites of multiple ROIs, making the SARM suitable for analysis at different resolutions and allowing broader labeling of functional signals when more accurate localization is not possible. As an example application of this atlas, we have included a functional localizer for the dorsal lateral geniculate (DLG) nucleus in three macaques using a visual flickering checkerboard stimulus, identifying and quantifying significant fMRI activation in this atlas region. The SARM has been made openly available to the neuroimaging community and can easily be used with common MRI data processing software, such as AFNI, where the atlas has been embedded into the software alongside cortical macaque atlases.

Puberty is characterized by hormonal, physical and psychological transformation. The human brain undergoes significant changes between childhood and adulthood, but little is known about how puberty influences its structural development. Using a longitudinal sample of 711 magnetic resonance imaging scans from 275 individuals aged 7–20years, we examined how subcortical brain regions change in relation to puberty. Our regions of interest included the amygdala, hippocampus and corpus striatum including the nucleus accumbens (NA), caudate, putamen and globus pallidus (GP). Pubertal development was significantly related to structural volume in all six regions in both sexes. Pubertal development and age had both independent and interactive influences on volume for the amygdala, hippocampus and putamen in both sexes, and the caudate in females. There was an interactive puberty-by-age effect on volume for the NA and GP in both sexes, and the caudate in males. These findings suggest a significant role for puberty in structural brain development. •Subcortical regions continue to develop through puberty in females and males.•The developmental trajectories vary between subcortical regions.•Puberty and age have independent and interactive influences on this development.

•Quantitative recurrent inference machine (qRIM) for accelerated R2* mapping.•Unified forward model for joint reconstruction and R2* mapping from sparse data.•Experiments at 7T with sub-millimeter resolution in a cohort study covering the entire adult life span.•An increasing reduction in the reconstruction error with increasing acceleration factor in subcortical regions.•Subcortical maturation over age preserved up to 9-fold acceleration. Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R2*-mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R2* from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R2*-maps. In contrast, when using the U-Net as network architecture, a negative bias in R2* in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R2*. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R2* in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.

Neuromodulatory nuclei that are part of the ascending arousal system (AAS) play a crucial role in regulating cortical state and optimizing task performance. Pupil diameter, under constant luminance conditions, is increasingly used as an index of activity of these AAS nuclei. Indeed, task-based functional imaging studies in humans have begun to provide evidence of stimulus-driven pupil-AAS coupling. However, whether there is such a tight pupil-AAS coupling during rest is not clear. To address this question, we examined simultaneously acquired resting-state fMRI and pupil-size data from 74 participants, focusing on six AAS nuclei: the locus coeruleus, ventral tegmental area, substantia nigra, dorsal and median raphe nuclei, and cholinergic basal forebrain. Activation in all six AAS nuclei was optimally correlated with pupil size at 0–2 s lags, suggesting that spontaneous pupil changes were almost immediately followed by corresponding BOLD-signal changes in the AAS. These results suggest that spontaneous changes in pupil size that occur during states of rest can be used as a noninvasive general index of activity in AAS nuclei. Importantly, the nature of pupil-AAS coupling during rest appears to be vastly different from the relatively slow canonical hemodynamic response function that has been used to characterize task-related pupil-AAS coupling.

Subcortical structures are a relative neurophysiological ‘terra incognita’ owing to their location within the skull. While perioperative subcortical sensing has been performed for more than 20 years, the neurophysiology of the basal ganglia in the home setting has remained almost unexplored. However, with the recent advent of implantable pulse generators (IPG) that are able to record neural activity, the opportunity to chronically record local field potentials (LFPs) directly from electrodes implanted for deep brain stimulation opens up. This allows for a breakthrough of chronic subcortical sensing into fundamental research and clinical practice. In this review an extensive overview of the current state of subcortical sensing is provided. The widespread potential of chronic subcortical sensing for investigational and clinical use is discussed. Finally, status and future perspectives of the most promising application of chronic subcortical sensing —i.e., adaptive deep brain stimulation (aDBS)— are discussed in the context of movement disorders. The development of aDBS based on both chronic subcortical and cortical sensing has the potential to dramatically change clinical practice and the life of patients with movement disorders. However, several barriers still stand in the way of clinical implementation. Advancements regarding IPG and lead technology, physiomarkers, and aDBS algorithms as well as harnessing artificial intelligence, multimodality and sensing in the naturalistic setting are needed to bring aDBS to clinical practice.

•7 Tesla submillimeter whole-brain dataset.•105 intrinsically aligned quantitative contrasts from a single scan acquisition.•Probabilistic atlases of the basal ganglia based on >1000 manual delineations.•Data freely available for further analyses. 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.

Most fundamental cognitive processes rely on brain networks that include both cortical and subcortical structures. Studying such networks using functional magnetic resonance imaging (fMRI) requires a data acquisition protocol that provides blood-oxygenation-level dependent (BOLD) sensitivity across the entire brain. However, when using standard single echo, echo planar imaging protocols, researchers face a tradeoff between BOLD-sensitivity in cortex and in subcortical areas. Multi echo protocols avoid this tradeoff and can be used to optimize BOLD-sensitivity across the entire brain, at the cost of an increased repetition time. Here, we empirically compare the BOLD-sensitivity of a single echo protocol to a multi echo protocol. Both protocols were designed to meet the specific requirements for studying small, iron rich subcortical structures (including a relatively high spatial resolution and short echo times), while retaining coverage and BOLD-sensitivity in cortical areas. The results indicate that both sequences lead to similar BOLD-sensitivity across the brain at 7 ​T. •Single and multi echo EPI protocols were compared for studying subcortex and cortex.•Both protocols had high spatial resolution, short echo times and whole-brain coverage.•The results show marginal differences in BOLD-sensitivity across the brain.