Connectivity Fingerprints: From Areal Descriptions to Abstract Spaces

Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease

Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.

Structure, function and connectivity fingerprints of the frontal eye field versus the inferior frontal junction: A comprehensive comparison

The human prefrontal cortex contains two prominent areas, the frontal eye field and the inferior frontal junction, that are crucially involved in the orchestrating functions of attention, working memory and cognitive control. Motivated by comparative evidence in non-human primates, we review the human neuroimaging literature, suggesting that the functions of these regions can be clearly dissociated. We found remarkable differences in how these regions relate to sensory domains and visual topography, top-down and bottom-up spatial attention, spatial versus non-spatial (i.e., feature- and object-based) attention and working memory and, finally, the multiple-demand system. Functional magnetic resonance imaging (fMRI) studies using multivariate pattern analysis reveal the selectivity of the frontal eye field and inferior frontal junction to spatial and non-spatial information, respectively. The analysis of functional and effective connectivity provides evidence of the modulation of the activity in downstream visual areas from the frontal eye field and inferior frontal junction and sheds light on their reciprocal influences. We therefore suggest that future studies should aim at disentangling more explicitly the role of these regions in the control of spatial and non-spatial selection. We propose that the analysis of the structural and functional connectivity (i.e., the connectivity fingerprints) of the frontal eye field and inferior frontal junction may be used to further characterize their involvement in a spatial ('where') and a non-spatial ('what') network, respectively, highlighting segregated brain networks that allow biasing visual selection and working memory performance to support goal-driven behaviour.

Activity flow underlying abnormalities in brain activations and cognition in schizophrenia

Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.

Functional Connectivity in Dorsolateral Frontal Cortex: Intracranial Electroencephalogram Study

Motivation: Mechanisms underlying the variation in the appearance of electroencephalogram (EEG) over human head are not well characterized. We hypothesized that spatial variation of the EEG, being ultimately linked to variations in cortical neurobiology, was dependent on cortical connectivity patterns. Specifically, we explored the relationship of resting-state functional connectivity derived from intracranial EEG (iEEG) data in seven (N = 7) human epilepsy patients with the intrinsic dynamic variability of the local iEEG. We asked whether primary and association brain areas over the lateral frontal lobe-due to their sharply different connectivity patterns-were thus dissociable in "EEG space." Methods: Functional connectivity between pairs of subdural grid electrodes was averaged to yield an electrode connectivity (EC) whose time-average yielded mean electrode connectivity (mEC), compared with that electrode's time-averaged sample entropy (SE; mean electrode sample entropy, mESE). Results: We found that mEC and mESE were generally in inverse proportion to each other. Extreme values of mEC and mESE occurred over the Rolandic region and were part of a more general rostrocaudal gradient observed in all patients, with larger (smaller) values of mEC (mESE) occurring anteriorly. Conclusions: Brain networks influence brain dynamics. Over the lateral frontal lobe, mEC and mESE demonstrate a rostrocaudal topography, consistent with current notions regarding the structural and functional parcellation of the human frontal lobe. Our findings distinguish the frontal association cortex from primary sensorimotor cortex, effectively "diagnosing" Rolandic iEEG independent of the classical mu rhythm associated with the latter brain region. Impact statement Electroencephalographic rhythms (electroencephalogram [EEG]) exhibit well-recognized spatial variation over the brain surface. How such variation pertains to the biology of the cortex is poorly understood. Here we identify a novel relationship between sample entropy of the local EEG and the connectivity of that local cortical region to the rest of the brain. Due to the differing connectivities of primary and association motor areas, our methods identify new differences in the EEG arising from those respective brain areas. Our work demonstrates that aspects of brain dynamics (i.e., EEG entropy) may be understood in terms of brain architecture (i.e., functional connectivity) and vice versa.

Frontal circuit specialisations for decision making

There is widespread consensus that distributed circuits across prefrontal and anterior cingulate cortex (PFC/ACC) are critical for reward-based decision making. The circuit specialisations of these areas in primates were likely shaped by their foraging niche, in which decision making is typically sequential, attention-guided and temporally extended. Here, I argue that in humans and other primates, PFC/ACC circuits are functionally specialised in two ways. First, microcircuits found across PFC/ACC are highly recurrent in nature and have synaptic properties that support persistent activity across temporally extended cognitive tasks. These properties provide the basis of a computational account of time-varying neural activity within PFC/ACC as a decision is being made. Second, the macrocircuit connections (to other brain areas) differ between distinct PFC/ACC cytoarchitectonic subregions. This variation in macrocircuit connections explains why PFC/ACC subregions make unique contributions to reward-based decision tasks and how these contributions are shaped by attention. They predict dissociable neural representations to emerge in orbitofrontal, anterior cingulate and dorsolateral prefrontal cortex during sequential attention-guided choice, as recently confirmed in neurophysiological recordings.

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

Background: Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual level. Methods: Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles. Recently, it has been proposed that the use of geodesic distance is a more accurate way of comparing FCs, one which reflects the underlying non-Euclidean geometry of the data. Computing geodesic distance requires FCs to be positive-definite and hence invertible matrices. As this requirement depends on the functional magnetic resonance imaging scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required. Results: In the present work, we show that regularization is not only an algebraic operation for making FCs invertible, but also that an optimal magnitude of regularization leads to systematically higher fingerprints. We also show evidence that optimal regularization is data set-dependent and varies as a function of condition, parcellation, scanning length, and the number of frames used to compute the FCs. Discussion: We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each data set to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects.

Understanding neural flexibility from a multifaceted definition

Flexibility is a hallmark of human intelligence. Emerging studies have proposed several flexibility measurements at the level of individual regions, to produce a brain map of neural flexibility. However, flexibility is usually inferred from separate components of brain activity (i.e., intrinsic/task-evoked), and different definitions are used. Moreover, recent studies have argued that neural processing may be more than a task-driven and intrinsic dichotomy. Therefore, the understanding to neural flexibility is still incomplete. To address this issue, we propose a multifaceted definition of neural flexibility according to three key features: broad cognitive engagement, distributed connectivity, and adaptive connectome dynamics. For these three features, we first review the advances in computational approaches, their functional relevance, and their potential pitfalls. We then suggest a set of metrics that can help us assign a flexibility rating to each region. Subsequently, we present an emergent probabilistic view for further understanding the functional operation of individual regions in the unified framework of intrinsic and task-driven states. Finally, we highlight several areas related to the multifaceted definition of neural flexibility for future research. This review not only strengthens our understanding of flexible human brain, but also suggests that the measure of neural flexibility could bridge the gap between understanding intrinsic and task-driven brain function dynamics.

Concurrent brain parcellation and connectivity estimation via co-clustering of resting state fMRI data: A novel approach

Connectional topography mapping has been gaining widespread attention in human brain imaging studies. However, existing methods might not effectively utilize the information from neuroimaging data, thus hindering the understanding of the underlying connectional organization in the brain and uncovering the optimal clustering number from the data. In this study, we propose a novel method for the automated construction of inherent functional connectivity topography in a data‐driven manner by leveraging the power of co‐clustering‐based on resting state fMRI (rs‐fMRI) data. We propose the co‐clustering‐based method not only for concurrently parcellating two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also for estimating the connectivity between these subregions from the two brain ROIs. In particular, we first model the connectional topography mapping as a co‐clustering‐based bipartite graph partitioning problem for constructing the inherent functional connectivity topography between the two interconnected brain ROIs. We also adopt an objective criterion, that is, silhouette width index measuring clustering quality, for determining the optimal number of clusters. The proposed method has been validated for mapping thalamocortical connectional topography based on rs‐fMRI data of 57 subjects. Validation results have demonstrated that our method identified the optimal solution with five pairs of mutually connected subregions of the thalamocortical system from the rs‐fMRI data, and could yield more meaningful, interpretable, and homogenous connectional topography than existing methods. The proposed method was further validated by the high symmetry of the mapped connectional topography between two hemispheres.

Systematically disrupted functional gradient of the cortical connectome in generalized epilepsy: Initial discovery and independent sample replication

Genetic generalized epilepsy is a network disorder typically involving distributed areas identified by classical neuroanatomy. However, the finer topological relationships in terms of continuous spatial arrangement between these systems are still ambiguous. Connectome gradients provide the topological representations of human macroscale hierarchy in an abstract low-dimensional space by embedding the functional connectome into a set of axes. Leveraging connectome gradients, we systematically scrutinized abnormalities of functional connectome gradient in patients with genetic generalized epilepsy with tonic-clonic seizure (GGE-GTCS, n = 78) compared to healthy controls (HC, n = 85), and further examined the reproducibility across multiple processing configurations and in an independent validation sample (patients with GGE-GTCS, n = 28; HC, n = 31). Our findings demonstrated an extended principal gradient at different spatial scales, network-level and vertex-level, in patients with GGE-GTCS. We found consistent results across processing parameters and in validation sample. The extended principal gradient revealed the excessive functional segregation between unimodal and transmodal systems associated with duration of epilepsy and age at seizure onset in patients. Furthermore, the connectivity profile of regions with abnormal principal gradients verified the disrupted functional hierarchy revealed by gradients. Together, our findings provided a novel view of functional system hierarchy alterations, which facilitated a continuous spatial arrangement of macroscale networks, to increase our understanding of the functional connectome hierarchy in generalized epilepsy.

A Common Space Approach to Comparative Neuroscience

Comparative neuroscience is entering the era of big data. New high-throughput methods and data-sharing initiatives have resulted in the availability of large, digital data sets containing many types of data from ever more species. Here, we present a framework for exploiting the new possibilities offered. The multimodality of the data allows vertical translations, which are comparisons of different aspects of brain organization within a single species and across scales. Horizontal translations compare particular aspects of brain organization across species, often by building abstract feature spaces. Combining vertical and horizontal translations allows for more sophisticated comparisons, including relating principles of brain organization across species by contrasting horizontal translations, and for making formal predictions of unobtainable data based on observed results in a model species.

Multimodal 3D atlas of the macaque monkey motor and premotor cortex

In the present study we reevaluated the parcellation scheme of the macaque frontal agranular cortex by implementing quantitative cytoarchitectonic and multireceptor analyses, with the purpose to integrate and reconcile the discrepancies between previously published maps of this region. We applied an observer-independent and statistically testable approach to determine the position of cytoarchitectonic borders. Analysis of the regional and laminar distribution patterns of 13 different transmitter receptors confirmed the position of cytoarchitectonically identified borders. Receptor densities were extracted from each area and visualized as its "receptor fingerprint". Hierarchical and principal components analyses were conducted to detect clusters of areas according to the degree of (dis)similarity of their fingerprints. Finally, functional connectivity pattern of each identified area was analyzed with areas of prefrontal, cingulate, somatosensory and lateral parietal cortex and the results were depicted as "connectivity fingerprints" and seed-to-vertex connectivity maps. We identified 16 cyto- and receptor architectonically distinct areas, including novel subdivisions of the primary motor area 4 (i.e. 4a, 4p, 4m) and of premotor areas F4 (i.e. F4s, F4d, F4v), F5 (i.e. F5s, F5d, F5v) and F7 (i.e. F7d, F7i, F7s). Multivariate analyses of receptor fingerprints revealed three clusters, which first segregated the subdivisions of area 4 with F4d and F4s from the remaining premotor areas, then separated ventrolateral from dorsolateral and medial premotor areas. The functional connectivity analysis revealed that medial and dorsolateral premotor and motor areas show stronger functional connectivity with areas involved in visual processing, whereas 4p and ventrolateral premotor areas presented a stronger functional connectivity with areas involved in somatomotor responses. For the first time, we provide a 3D atlas integrating cyto- and multi-receptor architectonic features of the macaque motor and premotor cortex. This atlas constitutes a valuable resource for the analysis of functional experiments carried out with non-human primates, for modeling approaches with realistic synaptic dynamics, as well as to provide insights into how brain functions have developed by changes in the underlying microstructure and encoding strategies during evolution.

Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network

Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.

Structural diverseness of neurons between brain areas and between cases

The cerebral cortex is composed of multiple cortical areas that exert a wide variety of brain functions. Although human brain neurons are genetically and areally mosaic, the three-dimensional structural differences between neurons in different brain areas or between the neurons of different individuals have not been delineated. Here we report a nanometer-scale geometric analysis of brain tissues of the superior temporal gyrus of schizophrenia and control cases. The results of the analysis and a comparison with results for the anterior cingulate cortex indicated that (1) neuron structures are significantly dissimilar between brain areas and that (2) the dissimilarity varies from case to case. The structural diverseness was mainly observed in terms of the neurite curvature that inversely correlates with the diameters of the neurites and spines. The analysis also revealed the geometric differences between the neurons of the schizophrenia and control cases. The schizophrenia cases showed a thin and tortuous neuronal network compared with the controls, suggesting that the neuron structure is associated with the disorder. The area dependency of the neuron structure and its diverseness between individuals should represent the individuality of brain functions.

A comprehensive atlas of white matter tracts in the chimpanzee

Chimpanzees (Pan troglodytes) are, along with bonobos, humans’ closest living relatives. The advent of diffusion MRI tractography in recent years has allowed a resurgence of comparative neuroanatomical studies in humans and other primate species. Here we offer, in comparative perspective, the first chimpanzee white matter atlas, constructed from in vivo chimpanzee diffusion-weighted scans. Comparative white matter atlases provide a useful tool for identifying neuroanatomical differences and similarities between humans and other primate species. Until now, comprehensive fascicular atlases have been created for humans (Homo sapiens), rhesus macaques (Macaca mulatta), and several other nonhuman primate species, but never in a nonhuman ape. Information on chimpanzee neuroanatomy is essential for understanding the anatomical specializations of white matter organization that are unique to the human lineage.

Diffusion MRI tractography reveals the first complete atlas of white matter of the chimpanzee, with the potential to help understand differences between the organization of human and chimpanzee brains.

Cross-species functional alignment reveals evolutionary hierarchy within the connectome

Evolution provides an important window into how cortical organization shapes function and vice versa. The complex mosaic of changes in brain morphology and functional organization that have shaped the mammalian cortex during evolution, complicates attempts to chart cortical differences across species. It limits our ability to fully appreciate how evolution has shaped our brain, especially in systems associated with unique human cognitive capabilities that lack anatomical homologues in other species. Here, we develop a function-based method for cross-species alignment that enables the quantification of homologous regions between humans and rhesus macaques, even when their location is decoupled from anatomical landmarks. Critically, we find cross-species similarity in functional organization reflects a gradient of evolutionary change that decreases from unimodal systems and culminates with the most pronounced changes in posterior regions of the default mode network (angular gyrus, posterior cingulate and middle temporal cortices). Our findings suggest that the establishment of the default mode network, as the apex of a cognitive hierarchy, has changed in a complex manner during human evolution - even within subnetworks.

Toward a connectivity gradient-based framework for reproducible biomarker discovery

Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent efforts to address this challenge have capitalized on dimensionality reduction techniques applied to resting-state fMRI, identifying principal components of intrinsic connectivity which describe smooth transitions across different cortical systems, so called "connectivity gradients". These gradients recapitulate neurocognitively meaningful organizational principles that are present in both human and primate brains, and also appear to differ among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209 × 2) and the Midnight scan club (n = 9), we tested the following key biomarker traits - reliability, reproducibility and predictive validity - of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (resting-state fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional and multivariate gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.

Cortico-Subcortical Functional Connectivity Profiles of Resting-State Networks in Marmosets and Humans

Understanding the similarity of cortico-subcortical networks topologies between humans and nonhuman primate species is critical to study the origin of network alternations underlying human neurologic and neuropsychiatric diseases. The New World common marmoset (Callithrix jacchus) has become popular as a nonhuman primate model for human brain function. Most marmoset connectomic research, however, has exclusively focused on cortical areas, with connectivity to subcortical networks less extensively explored. Here, we aimed to first isolate patterns of subcortical connectivity with cortical resting-state networks in awake marmosets using resting-state fMRI, then to compare these networks with those in humans using connectivity fingerprinting. In this study, we used 5 marmosets (4 males, 1 female). While we could match several marmoset and human resting-state networks based on their functional fingerprints, we also found a few striking differences, for example, strong functional connectivity of the default mode network with the superior colliculus in marmosets that was much weaker in humans. Together, these findings demonstrate that many of the core cortico-subcortical networks in humans are also present in marmosets, but that small, potentially functionally relevant differences exist.SIGNIFICANCE STATEMENT The common marmoset is becoming increasingly popular as an additional preclinical nonhuman primate model for human brain function. Here we compared the functional organization of cortico-subcortical networks in marmosets and humans using ultra-high field fMRI. We isolated the patterns of subcortical connectivity with cortical resting-state networks (RSNs) in awake marmosets using resting-state fMRI and then compared these networks with those in humans using connectivity fingerprinting. While we could match several marmoset and human RSNs based on their functional fingerprints, we also found several striking differences. Together, these findings demonstrate that many of the core cortico-subcortical RSNs in humans are also present in marmosets, but that small, potentially functionally relevant differences exist.

Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles

The frontal pole cortex (FPC) plays key roles in various higher-order functions and is highly developed in non-human primates. An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results. This is important for understanding the functional architecture of the cerebral cortex. Here, combining cross-validation and principal component analysis, we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC (2 males and 6 females) into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4T Bruker system, and then revealed their subregional connections. Furthermore, we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions, and found that the dorsolateral FPC, which contains an extension to the medial FPC, was mainly connected to regions of the default-mode network. The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network. These results enhance our understanding of the anatomy and circuitry of the macaque brain, and contribute to FPC-related clinical research.

Identifiability of structural networks of nonlinear electronic oscillators

The interplay between structure and function is critical in the understanding of complex systems, their dynamics and their behavior. We investigated the interplay between structural and functional networks by means of the differential identifiability framework, which here quantifies the ability of identifying a particular network structure based on (1) the observation of its functional network and (2) the comparison with a prior observation under different initial conditions. We carried out an experiment consisting of the construction of [Formula: see text] different structural networks composed of [Formula: see text] nonlinear electronic circuits and studied the regions where network structures are identifiable. Specifically, we analyzed how differential identifiability is related to the coupling strength between dynamical units (modifying the level of synchronization) and what are the consequences of increasing the amount of noise existing in the functional networks. We observed that differential identifiability reaches its highest value for low to intermediate coupling strengths. Furthermore, it is possible to increase the identifiability parameter by including a principal component analysis in the comparison of functional networks, being especially beneficial for scenarios where noise reaches intermediate levels. Finally, we showed that the regime of the parameter space where differential identifiability is the highest is highly overlapped with the region where structural and functional networks correlate the most.

Divergence of rodent and primate medial frontal cortex functional connectivity

With the medial frontal cortex (MFC) centrally implicated in several major neuropsychiatric disorders, it is critical to understand the extent to which MFC organization is comparable between humans and animals commonly used in preclinical research (namely rodents and nonhuman primates). Although the cytoarchitectonic structure of the rodent MFC has mostly been conserved in humans, it is a long-standing question whether the structural analogies translate to functional analogies. Here, we probed this question using ultra high field fMRI data to compare rat, marmoset, and human MFC functional connectivity. First, we applied hierarchical clustering to intrinsically define the functional boundaries of the MFC in all three species, independent of cytoarchitectonic definitions. Then, we mapped the functional connectivity "fingerprints" of these regions with a number of different brain areas. Because rats do not share cytoarchitectonically defined regions of the lateral frontal cortex (LFC) with primates, the fingerprinting method also afforded the unique ability to compare the rat MFC and marmoset LFC, which have often been suggested to be functional analogs. The results demonstrated remarkably similar intrinsic functional organization of the MFC across the species, but clear differences between rodent and primate MFC whole-brain connectivity. Rat MFC patterns of connectivity showed greatest similarity with premotor regions in the marmoset, rather than dorsolateral prefrontal regions, which are often suggested to be functionally comparable. These results corroborate the viability of the marmoset as a preclinical model of human MFC dysfunction, and suggest divergence of functional connectivity between rats and primates in both the MFC and LFC.

XTRACT - Standardised protocols for automated tractography in the human and macaque brain

We present a new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science.

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

GEFF: Graph embedding for functional fingerprinting

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.

Connectomic consistency: a systematic stability analysis of structural and functional connectivity

OBJECTIVE

Connectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain's functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a comparative understanding on how much variation should be expected in the two modalities, both between people and within a single person across scans.

APPROACH

In this study, we systematically analyzed the consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions and in terms of overall network topology. We present a comprehensive study of consistency in connectomes for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally, in structure and function separately. Within structural connectomes, we compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. In functional connectomes, we compared full, positive, and negative connectivity separately along with thresholding of weak edges. We evaluated consistency using correlation (incorporating information at the level of individual edges) and graph matching accuracy (evaluating connectivity at the level of network topology). We also examined the consistency of connectomes that are generated using different communication schemes.

MAIN RESULTS

Our results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated.

SIGNIFICANCE

Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.

Uncovering differential identifiability in network properties of human brain functional connectomes

The identifiability framework (𝕀f) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation, and communicability, among others. Naturally, one wonders whether uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the 𝕀f framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when 𝕀f is applied (a) on the functional connectomes, and (b) directly on derived network measurements. Results show that improving across-session reliability of functional connectomes (FCs) also improves reliability of derived network measures. We also find that, for specific network properties, application of 𝕀f directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is focused on subject-level inferences, this framework is able to uncover FC fingerprints, which propagate to derived network properties.

Reading music and words: The anatomical connectivity of musicians' visual cortex

Musical score reading and word reading have much in common, from their historical origins to their cognitive foundations and neural correlates. In the ventral occipitotemporal cortex (VOT), the specialization of the so-called Visual Word Form Area for word reading has been linked to its privileged structural connectivity to distant language regions. Here we investigated how anatomical connectivity relates to the segregation of regions specialized for musical notation or words in the VOT. In a cohort of professional musicians and non-musicians, we used probabilistic tractography combined with task-related functional MRI to identify the connections of individually defined word- and music-selective left VOT regions. Despite their close proximity, these regions differed significantly in their structural connectivity, irrespective of musical expertise. The music-selective region was significantly more connected to posterior lateral temporal regions than the word-selective region, which, conversely, was significantly more connected to anterior ventral temporal cortex. Furthermore, musical expertise had a double impact on the connectivity of the music region. First, music tracts were significantly larger in musicians than in non-musicians, associated with marginally higher connectivity to perisylvian music-related areas. Second, the spatial similarity between music and word tracts was significantly increased in musicians, consistently with the increased overlap of language and music functional activations in musicians, as compared to non-musicians. These results support the view that, for music as for words, very specific anatomical connections influence the specialization of distinct VOT areas, and that reciprocally those connections are selectively enhanced by the expertise for word or music reading.

Towards HCP-Style macaque connectomes: 24-Channel 3T multi-array coil, MRI sequences and preprocessing

Macaque monkeys are an important animal model where invasive investigations can lead to a better understanding of the cortical organization of primates including humans. However, the tools and methods for noninvasive image acquisition (e.g. MRI RF coils and pulse sequence protocols) and image data preprocessing have lagged behind those developed for humans. To resolve the structural and functional characteristics of the smaller macaque brain, high spatial, temporal, and angular resolutions combined with high signal-to-noise ratio are required to ensure good image quality. To address these challenges, we developed a macaque 24-channel receive coil for 3-T MRI with parallel imaging capabilities. This coil enables adaptation of the Human Connectome Project (HCP) image acquisition protocols to the in-vivo macaque brain. In addition, we adapted HCP preprocessing methods to the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI). The coil provides the necessary high signal-to-noise ratio and high efficiency in data acquisition, allowing four- and five-fold accelerations for dMRI and fMRI. Automated FreeSurfer segmentation of cortex, reconstruction of cortical surface, removal of artefacts and nuisance signals in fMRI, and distortion correction of dMRI all performed well, and the overall quality of basic neurobiological measures was comparable with those for the HCP. Analyses of functional connectivity in fMRI revealed high sensitivity as compared with those from publicly shared datasets. Tractography-based connectivity estimates correlated with tracer connectivity similarly to that achieved using ex-vivo dMRI. The resulting HCP-style in vivo macaque MRI data show considerable promise for analyzing cortical architecture and functional and structural connectivity using advanced methods that have previously only been available in studies of the human brain.

Magnetoencephalography in Cognitive Neuroscience: A Primer

Magnetoencephalography (MEG) is an invaluable tool to study the dynamics and connectivity of large-scale brain activity and their interactions with the body and the environment in functional and dysfunctional body and brain states. This primer introduces the basic concepts of MEG, discusses its strengths and limitations in comparison to other brain imaging techniques, showcases interesting applications, and projects exciting current trends into the near future, in a way that might more fully exploit the unique capabilities of MEG.

Human Lateral Frontal Pole Contributes to Control over Emotional Approach-Avoidance Actions

Regulation of emotional behavior is essential for human social interactions. Recent work has exposed its cognitive complexity, as well as its unexpected reliance on portions of the anterior PFC (aPFC) also involved in exploration, relational reasoning, and counterfactual choice, rather than on dorsolateral and medial prefrontal areas involved in several forms of cognitive control. This study anatomically qualifies the contribution of aPFC territories to the regulation of prepotent approach-avoidance action tendencies elicited by emotional faces, and explores a possible structural pathway through which this emotional action regulation might be implemented. We provide converging evidence from task-based fMRI, diffusion-weighted imaging, and functional connectivity fingerprints for a novel neural element in emotional regulation. Task-based fMRI in human male participants (N = 40) performing an emotional approach-avoidance task identified aPFC territories involved in the regulation of action tendencies elicited by emotional faces. Connectivity fingerprints, based on diffusion-weighted imaging and resting-state connectivity, localized those task-defined frontal regions to the lateral frontal pole (FPl), an anatomically defined portion of the aPFC that lacks a homologous counterpart in macaque brains. Probabilistic tractography indicated that 10%-20% of interindividual variation in emotional regulation abilities is accounted for by the strength of structural connectivity between FPl and amygdala. Evidence from an independent replication sample (N = 50; 10 females) further substantiated this result. These findings provide novel neuroanatomical evidence for incorporating FPl in models of control over human action tendencies elicited by emotional faces.SIGNIFICANCE STATEMENT Successful regulation of emotional behaviors is a prerequisite for successful participation in human society, as is evidenced by the social isolation and loss of occupational opportunities often encountered by people suffering from emotion regulation disorders, such as social-anxiety disorder and psychopathy. Knowledge about the precise cortical regions and connections supporting this control is crucial for understanding both the nature of computations needed to successfully traverse the space of possible actions in social situations, and the potential interventions that might result in efficient treatment of social-emotional disorders. This study provides evidence for a precise cortical region (lateral frontal pole) and a structural pathway (the ventral amygdalofugal bundle) through which a cognitively complex form of emotional action regulation might be implemented in the human brain.

Cross-species cortical alignment identifies different types of anatomical reorganization in the primate temporal lobe

Evolutionary adaptations of temporo-parietal cortex are considered to be a critical specialization of the human brain. Cortical adaptations, however, can affect different aspects of brain architecture, including local expansion of the cortical sheet or changes in connectivity between cortical areas. We distinguish different types of changes in brain architecture using a computational neuroanatomy approach. We investigate the extent to which between-species alignment, based on cortical myelin, can predict changes in connectivity patterns across macaque, chimpanzee, and human. We show that expansion and relocation of brain areas can predict terminations of several white matter tracts in temporo-parietal cortex, including the middle and superior longitudinal fasciculus, but not the arcuate fasciculus. This demonstrates that the arcuate fasciculus underwent additional evolutionary modifications affecting the temporal lobe connectivity pattern. This approach can flexibly be extended to include other features of cortical organization and other species, allowing direct tests of comparative hypotheses of brain organization.

Multiscale Structure-Function Gradients in the Neonatal Connectome

The adult functional connectome is well characterized by a macroscale spatial gradient of connectivity traversing from unimodal toward higher-order transmodal cortices that recapitulates known principles of hierarchical organization and myelination patterns. Despite an emerging literature assessing connectome properties in neonates, the presence of connectome gradients and particularly their correspondence to microstructure remains largely unknown. We derived connectome gradients using unsupervised techniques applied to functional connectivity data from 40 term-born neonates. A series of cortex-wide analysis examined associations to magnetic resonance imaging-derived morphological parameters (cortical thickness, sulcal depth, curvature), measures of tissue microstructure (intracortical T1w/T2w intensity, superficial white matter diffusion parameters), and subcortico-cortical functional connectivity. Our findings indicate that the primary neonatal connectome gradient runs between sensorimotor and visual anchors and captures specific associations to cortical and superficial white matter microstructure as well as thalamo-cortical connectivity. A second gradient indicated an anterior-to-posterior asymmetry in macroscale connectivity alongside an immature differentiation between unimodal and transmodal areas, indicating a connectome-level circuitry en route to an adult-like organization. Our findings reveal an important coordination of structural and functional interactions in the neonatal connectome across spatial scales. Observed associations were replicable across individual neonates, suggesting consistency and generalizability.

Discovering the Computational Relevance of Brain Network Organization

Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking empirical and simulated brain network organization with cognitive information processing. Building on these advances, we offer a new framework for understanding the role of connectivity in cognition: network coding (encoding/decoding) models. These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations in empirical neural data. The success of these models supports the possibility that localized neural functions mechanistically emerge (are computed) from distributed activity flow processes that are specified primarily by connectivity patterns.

Individualized Cortical Parcellation Based on Diffusion MRI Tractography

The spatial topological properties of cortical regions vary across individuals. Connectivity-based functional and anatomical cortical mapping in individuals will facilitate research on structure-function relationships. However, individual-specific cortical topographic properties derived from anatomical connectivity are less explored than those based on functional connectivity. We aimed to develop a novel individualized anatomical connectivity-based parcellation framework and investigate individual differences in spatial topographic features of cortical regions using diffusion magnetic resonance imaging (dMRI) tractography. Using a high-quality, repeated-session dMRI dataset (42 subjects, 2 sessions per subject), cortical parcels were derived through in vivo anatomical connectivity-based parcellation. These individual-specific parcels demonstrated good within-individual reproducibility and reflected interindividual differences in anatomical brain organization. Connectivity in these individual-specific parcels was significantly more homogeneous than that based on the group atlas. We found that the position, size, and topography of these anatomical parcels were highly variable across individuals and demonstrated nonredundant information about individual differences. Finally, we found that intersubject variability in anatomical connectivity was correlated with the diversity of anatomical connectivity patterns. Overall, we identified cortical parcels that show homogeneous anatomical connectivity patterns. These parcels displayed marked intersubject spatial variability, which may be used in future functional studies to reveal structure-function relationships in the human brain.

Dichotomous organization of amygdala/temporal-prefrontal bundles in both humans and monkeys

The interactions of anterior temporal structures, and especially the amygdala, with the prefrontal cortex are pivotal to learning, decision-making, and socio-emotional regulation. A clear anatomical description of the organization and dissociation of fiber bundles linking anterior temporal cortex/amygdala and prefrontal cortex in humans is still lacking. Using diffusion imaging techniques, we reconstructed fiber bundles between these anatomical regions in human and macaque brains. First, by studying macaques, we assessed which aspects of connectivity known from tracer studies could be identified with diffusion imaging. Second, by comparing diffusion imaging results in humans and macaques, we estimated the patterns of fibers coursing between human amygdala and prefrontal cortex and compared them with those in the monkey. In posterior prefrontal cortex, we observed a prominent and well-preserved bifurcation of bundles into primarily two fiber systems-an amygdalofugal path and an uncinate path-in both species. This dissociation fades away in more rostral prefrontal regions.

Intrinsic Functional Connectivity is Organized as Three Interdependent Gradients

The intrinsic functional architecture of the brain supports moment-to-moment maintenance of an internal model of the world. We hypothesized and found three interdependent architectural gradients underlying the organization of intrinsic functional connectivity within the human cerebral cortex. We used resting state fMRI data from two samples of healthy young adults (N's = 280 and 270) to generate functional connectivity maps of 109 seeds culled from published research, estimated their pairwise similarities, and multidimensionally scaled the resulting similarity matrix. We discovered an optimal three-dimensional solution, accounting for 98% of the variance within the similarity matrix. The three dimensions corresponded to three gradients, which spatially correlate with two functional features (external vs. internal sources of information; content representation vs. attentional modulation) and one structural feature (anatomically central vs. peripheral) of the brain. Remapping the three dimensions into coordinate space revealed that the connectivity maps were organized in a circumplex structure, indicating that the organization of intrinsic connectivity is jointly guided by graded changes along all three dimensions. Our findings emphasize coordination between multiple, continuous functional and anatomical gradients, and are consistent with the emerging predictive coding perspective.

Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex

Regions of human medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) are part of the default network (DN), and additionally are implicated in diverse cognitive functions ranging from autobiographical memory to subjective valuation. Our ability to interpret the apparent co-localization of task-related effects with DN-regions is constrained by a limited understanding of the individual-level heterogeneity in mPFC/PCC functional organization. Here we used cortical surface-based meta-analysis to identify a parcel in human PCC that was more strongly associated with the DN than with valuation effects. We then used resting-state fMRI data and a data-driven network analysis algorithm, spectral partitioning, to partition mPFC and PCC into "DN" and "non-DN" subdivisions in individual participants (n = 100 from the Human Connectome Project). The spectral partitioning algorithm identified individual-level cortical subdivisions that varied markedly across individuals, especially in mPFC, and were reliable across test/retest datasets. Our results point toward new strategies for assessing whether distinct cognitive functions engage common or distinct mPFC subregions at the individual level.

The impact of ischemic stroke on connectivity gradients

The functional organization of the brain can be represented as a low-dimensional space that reflects its macroscale hierarchy. The dimensions of this space, described as connectivity gradients, capture the similarity of areas' connections along a continuous space. Studying how pathological perturbations with known effects on functional connectivity affect these connectivity gradients provides support for their biological relevance. Previous work has shown that localized lesions cause widespread functional connectivity alterations in structurally intact areas, affecting a network of interconnected regions. By using acute stroke as a model of the effects of focal lesions on the connectome, we apply the connectivity gradient framework to depict how functional reorganization occurs throughout the brain, unrestricted by traditional definitions of functional network boundaries. We define a three-dimensional connectivity space template based on functional connectivity data from healthy controls. By projecting lesion locations into this space, we demonstrate that ischemic strokes result in dimension-specific alterations in functional connectivity over the first week after symptom onset. Specifically, changes in functional connectivity were captured along connectivity Gradients 1 and 3. The degree of functional connectivity change was associated with the distance from the lesion along these connectivity gradients (a measure of functional similarity) regardless of the anatomical distance from the lesion. Together, these results provide support for the biological validity of connectivity gradients and suggest a novel framework to characterize connectivity alterations after stroke.

The human connectome from an evolutionary perspective

The connectome describes the comprehensive set of neuronal connections of a species' central nervous system. Identifying the network characteristics of the human macroscale connectome and comparing these features with connectomes of other species provides insight into the evolution of human brain connectivity and its role in brain function. Several network properties of the human connectome are conserved across species, with emerging evidence also indicating potential human-specific adaptations of connectome topology. This review describes the human macroscale structural and functional connectome, focusing on common themes of brain wiring in the animal kingdom and network adaptations that may underlie human brain function. Evidence is drawn from comparative studies across a wide range of animal species, and from research comparing human brain wiring with that of non-human primates. Approaching the human connectome from a comparative perspective paves the way for network-level insights into the evolution of human brain structure and function.

The function of connectomes in encoding sensory stimuli

The enormous number of neurons and the massive sum of connecting fibers linking them make the neural processes of encoding sensory signals extraordinarily complex, and this challenge is far from being elucidated. Simply stated, for the present paper, the question is - how does the brain encode complex images? Our proposal argues that modulation of strengths of functional relationships between firing neurons in relation to an input results in the formation of stimulus-salient functional connectomes. This type of connection/coupling strength is computed by performing cross correlograms (CCG) of spike trains between simultaneously firing cells. Significantly, the strength is dependent upon stimuli characteristics, inferring that cells may join or leave particular ensembles, thus creating signature emergent connectomes for different images, thereby, allowing their discrimination. We observed in an ensemble that functionally connected cells exhibited synergistic excitatory activity, increased coherence, and augmented gamma oscillations within a window-of-opportunity contrasting with unconnected neighboring neuronal companions. We suggest that investigating and revealing such stimulus-salient emergent connectomes is a realistic and promising pursuit toward answering how the brain processes complex images.

Interspecies Differences in the Connectivity of Ventral Striatal Components Between Humans and Macaques

Although the evolutionarily conserved functions of the ventral striatal components have been used as a priori knowledge for further study, whether these functions are conserved between species remains unclear. In particular, whether macroscopic connectivity supports this given the disproportionate volumetric differences between species in the brain regions that project to the ventral striatum, including the prefrontal and limbic areas, has not been established In this study, the human and macaque striatum was first tractographically parcellated to define the ventral striatum and its two subregions, the nucleus accumbens (Acb)-like and the neurochemically unique domains of the Acb and putamen (NUDAPs)-like divisions. Our results revealed a similar topographical distribution of the connectivity-based ventral striatal components in the two primate brains. Successively, a set of targets was extracted to construct a connectivity fingerprint to characterize these parcellation results, enabling cross-species comparisons. Our results indicated that the connectivity fingerprints of the ventral striatum-like divisions were dissimilar in the two species. We localized this difference to specific targets to analyze possible interspecies functional modifications. Our results also revealed interspecies-convergent connectivity ratio fingerprints of the target group to these two ventral striatum-like subregions. This convergence may suggest synchronous connectional changes of these ventral striatal components during primate evolution.

Corticocortical Systems Underlying High-Order Motor Control

Cortical networks are characterized by the origin, destination, and reciprocity of their connections, as well as by the diameter, conduction velocity, and synaptic efficacy of their axons. The network formed by parietal and frontal areas lies at the core of cognitive-motor control because the outflow of parietofrontal signaling is conveyed to the subcortical centers and spinal cord through different parallel pathways, whose orchestration determines, not only when and how movements will be generated, but also the nature of forthcoming actions. Despite intensive studies over the last 50 years, the role of corticocortical connections in motor control and the principles whereby selected cortical networks are recruited by different task demands remain elusive. Furthermore, the synaptic integration of different cortical signals, their modulation by transthalamic loops, and the effects of conduction delays remain challenging questions that must be tackled to understand the dynamical aspects of parietofrontal operations. In this article, we evaluate results from nonhuman primate and selected rodent experiments to offer a viewpoint on how corticocortical systems contribute to learning and producing skilled actions. Addressing this subject is not only of scientific interest but also essential for interpreting the devastating consequences for motor control of lesions at different nodes of this integrated circuit. In humans, the study of corticocortical motor networks is currently based on MRI-related methods, such as resting-state connectivity and diffusion tract-tracing, which both need to be contrasted with histological studies in nonhuman primates.

Predicting an individual's dorsal attention network activity from functional connectivity fingerprints

The cortical dorsal attention network (DAN) is a set of parietal and frontal regions that support a wide variety of attentionally demanding tasks. Whereas attentional deployment reliably drives DAN activity across subjects, there is a large degree of variation in the activation pattern in individual subjects. We hypothesize that a subject's own idiosyncratic pattern of cortical DAN activity can be predicted from that subject's own unique pattern of functional connectivity. By modeling task activation as a function of whole brain connectivity patterns, we are able to define the connectivity fingerprints for the frontal and parietal DAN, and use it to predict a subject's characteristic DAN activation pattern with high accuracy. These predictions outperform the standard group-average benchmark and predict a subject's own activation pattern above and beyond predictions from another subject's connectivity pattern. Thus an individual's distinctive connectivity pattern accounts for substantial variance in DAN functional responses. Last, we show that the set of connections that predict cortical DAN responses, the frontal and parietal DAN connectivity fingerprints, is predominantly composed of other coactive regions, including regions outside of the DAN including occipital and temporal visual cortices. These connectivity fingerprints represent defining computational characteristics of the DAN, delineating which voxels are or are not capable of exerting top-down attentional bias to other regions of the brain. NEW & NOTEWORTHY The dorsal attention network (DAN) is a set of regions in frontoparietal cortex that reliably activate during attentional tasks. We designed computational models that predict the degree of an individual's DAN activation using their resting-state connectivity pattern alone. This uncovered the connectivity fingerprints of the DAN, which define it so well that we can predict how a voxel will respond to an attentional task given only its pattern of connectivity, with outstanding accuracy.

Microstructural and functional gradients are increasingly dissociated in transmodal cortices

While the role of cortical microstructure in organising neural function is well established, it remains unclear how structural constraints can give rise to more flexible elements of cognition. While nonhuman primate research has demonstrated a close structure-function correspondence, the relationship between microstructure and function remains poorly understood in humans, in part because of the reliance on post mortem analyses, which cannot be directly related to functional data. To overcome this barrier, we developed a novel approach to model the similarity of microstructural profiles sampled in the direction of cortical columns. Our approach was initially formulated based on an ultra-high-resolution 3D histological reconstruction of an entire human brain and then translated to myelin-sensitive magnetic resonance imaging (MRI) data in a large cohort of healthy adults. This novel method identified a system-level gradient of microstructural differentiation traversing from primary sensory to limbic regions that followed shifts in laminar differentiation and cytoarchitectural complexity. Importantly, while microstructural and functional gradients described a similar hierarchy, they became increasingly dissociated in transmodal default mode and fronto-parietal networks. Meta-analytic decoding of these topographic dissociations highlighted involvement in higher-level aspects of cognition, such as cognitive control and social cognition. Our findings demonstrate a relative decoupling of macroscale functional from microstructural gradients in transmodal regions, which likely contributes to the flexible role these regions play in human cognition.

Concurrent analysis of white matter bundles and grey matter networks in the chimpanzee

Understanding the phylogeny of the human brain requires an appreciation of brain organization of our closest animal relatives. Neuroimaging tools such as magnetic resonance imaging (MRI) allow us to study whole-brain organization in species which can otherwise not be studied. Here, we used diffusion MRI to reconstruct the connections of the cortical hemispheres of the chimpanzee. This allowed us to perform an exploratory analysis of the grey matter structures of the chimpanzee cerebral cortex and their underlying white matter connectivity profiles. We identified a number of networks that strongly resemble those found in other primates, including the corticospinal system, limbic connections through the cingulum bundle and fornix, and occipital-temporal and temporal-frontal systems. Notably, chimpanzee temporal cortex showed a strong resemblance to that of the human brain, providing some insight into the specialization of the two species' shared lineage.

Network convergence zones in the anterior midcingulate cortex

Situated medially and centrally in the brain, the anterior midcingulate cortex (aMCC) is a nexus of control. This specialized neocortical brain region participates in large-scale brain networks underlying attention, motor, and limbic processes. The functional diversity and proximity of cognitive and affective subdivisions within this region are its distinguishing features, rendering it an effective site for integration across domains. Here we review comparative neuroanatomic, meta-analytic, and connectomic analyses contributing to the emerging picture of the aMCC as comprising functionally diverse, flexible network nodes involved in multiple regulatory behaviors. We further present data providing evidence for an organizing gradient along the anterior and midcingulate cortex and explore the implications of these findings for understanding the functional role of the anterior midcingulate within this spectrum. We conclude by highlighting open questions and proposing future directions for investigations into the functional role of this important network convergence zone.