Search

Cortical areas have traditionally been defined by their distinctive layer cyto- and/or myelo- architecture using postmortem histology. Recent studies have delineated many areas by measuring overall cortical myelin content and its spatial gradients using the T1w/T2w ratio MRI in living primates, including humans. While T1w/T2w studies of areal transitions might benefit from using the layer profile of this myelin-related contrast, a significant confound is Gibbs' ringing artefact, which produces signal fluctuations resembling cortical layers. Here, we address these issues with a novel approach using cortical layer thickness-adjusted T1w/T2w-FLAIR imaging, which effectively cancels out Gibbs' ringing artefacts while enhancing intracortical myelin contrast. Whole-brain MRI measures were mapped onto twelve equivolumetric layers, and layer-specific sharp myeloarchitectonic transitions were identified using spatial gradients resulting in a putative 182 area/subarea partition of the macaque cerebral cortex. The myelin maps exhibit unexpectedly high homology with humans suggesting cortical myelin shares the same developmental program across the species. Comparison with histological Gallyas myelin stains explains over 80% of the variance in the laminar T1w/T2w-FLAIR profiles, substantiating the validity of the method. Altogether, our approach provides a novel, noninvasive means for precision mapping layer myeloarchitecture in the primate cerebral cortex, advancing the pioneering work of classical neuroanatomists., Competing Interests: Declaration of competing interest All the authors declare no competing financial interests.

Background: Brain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy., Methods: A 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice., Results: On the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours., Conclusions: In a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.)., (Copyright © 2024 Massachusetts Medical Society.)

In the human brain, a multiple-demand (MD) network plays a key role in cognitive control, with core components in lateral frontal, dorsomedial frontal and lateral parietal cortex, and multivariate activity patterns that discriminate the contents of many cognitive activities. In prefrontal cortex of the behaving monkey, different cognitive operations are associated with very different patterns of neural activity, while details of a particular stimulus are encoded as small variations on these basic patterns (Sigala et al, 2008). Here, using the advanced fMRI methods of the Human Connectome Project and their 360-region cortical parcellation, we searched for a similar result in MD activation patterns. In each parcel, we compared multivertex patterns for every combination of three tasks (working memory, task-switching, and stop-signal) and two stimulus classes (faces and buildings). Though both task and stimulus category were discriminated in every cortical parcel, the strength of discrimination varied strongly across parcels. The different cognitive operations of the three tasks were strongly discriminated in MD regions. Stimulus categories, in contrast, were most strongly discriminated in a large region of primary and higher visual cortex, and intriguingly, in both parietal and frontal lobe regions adjacent to core MD regions. In the monkey, frontal neurons show a strong pattern of nonlinear mixed selectivity, with activity reflecting specific conjunctions of task events. In our data, however, there was limited evidence for mixed selectivity; throughout the brain, discriminations of task and stimulus combined largely linearly, with a small nonlinear component. In MD regions, human fMRI data recapitulate some but not all aspects of electrophysiological data from nonhuman primates., (© The Author(s) 2024. Published by Oxford University Press.)

Mapping the vascular organization of the brain is of great importance across various domains of basic neuroimaging research, diagnostic radiology, and neurology. However, the intricate task of precisely mapping vasculature across brain regions and cortical layers presents formidable challenges, resulting in a limited understanding of neurometabolic factors influencing the brain's microvasculature. Addressing this gap, our study investigates whole-brain vascular volume using ferumoxytol-weighted laminar-resolution multi-echo gradient-echo imaging in macaque monkeys. We validate the results with published data for vascular densities and compare them with cytoarchitecture, neuron and synaptic densities. The ferumoxytol-induced change in transverse relaxation rate (ΔR 2 *), an indirect proxy measure of cerebral blood volume (CBV), was mapped onto twelve equivolumetric laminar cortical surfaces. Our findings reveal that CBV varies 3-fold across the brain, with the highest vascular volume observed in the inferior colliculus and lowest in the corpus callosum. In the cerebral cortex, CBV is notably high in early primary sensory areas and low in association areas responsible for higher cognitive functions. Classification of CBV into distinct groups unveils extensive replication of translaminar vascular network motifs, suggesting distinct computational energy supply requirements in areas with varying cytoarchitecture types. Regionally, baseline R 2 * and CBV exhibit positive correlations with neuron density and negative correlations with receptor densities. Adjusting image resolution based on the critical sampling frequency of penetrating cortical vessels, allows us to delineate approximately 30% of the arterial-venous vessels. Collectively, these results mark significant methodological and conceptual advancements, contributing to the refinement of cerebrovascular MRI. Furthermore, our study establishes a linkage between neurometabolic factors and the vascular network architecture in the primate brain.

Theoretical models suggest that executive functions rely on both domain-general and domain-specific processes. Supporting this view, prior brain imaging studies have revealed that executive activations converge and diverge within broadly characterized brain networks. However, the lack of precise anatomical mappings has impeded our understanding of the interplay between domain-general and domain-specific processes. To address this challenge, we used the high-resolution multimodal magnetic resonance imaging approach of the Human Connectome Project to scan participants performing 3 canonical executive tasks: n-back, rule switching, and stop signal. The results reveal that, at the individual level, different executive activations converge within 9 domain-general territories distributed in frontal, parietal, and temporal cortices. Each task exhibits a unique topography characterized by finely detailed activation gradients within domain-general territory shifted toward adjacent resting-state networks; n-back activations shift toward the default mode, rule switching toward dorsal attention, and stop signal toward cingulo-opercular networks. Importantly, the strongest activations arise at multimodal neurobiological definitions of network borders. Matching results are seen in circumscribed regions of the caudate nucleus, thalamus, and cerebellum. The shifting peaks of local gradients at the intersection of task-specific networks provide a novel mechanistic insight into how partially-specialized networks interact with neighboring domain-general territories to generate distinct executive functions., (© The Author(s) 2024. Published by Oxford University Press.)

The Human Connectome Project (HCP)-style surface-based brain MRI analysis is a powerful technique that allows precise mapping of the cerebral cortex. However, the strength of its surface-based analysis has not yet been tested in the older population that often presents with white matter hyperintensities (WMHs) on T2-weighted (T2w) MRI (hypointensities on T1w MRI). We investigated T1-weighted (T1w) and T2w structural MRI in 43 healthy middle-aged to old participants. Juxtacortical WMHs were often misclassified by the default HCP pipeline as parts of the gray matter in T1w MRI, leading to incorrect estimation of the cortical surfaces and cortical metrics. To revert the adverse effects of juxtacortical WMHs, we incorporated the Brain Intensity AbNormality Classification Algorithm into the HCP pipeline (proposed pipeline). Blinded radiologists performed stereological quality control (QC) and found a decrease in the estimation errors in the proposed pipeline. The superior performance of the proposed pipeline was confirmed using an originally-developed automated surface QC based on a large database. Here we showed the detrimental effects of juxtacortical WMHs for estimating cortical surfaces and related metrics and proposed a possible solution for this problem. The present knowledge and methodology should help researchers identify adequate cortical surface biomarkers for aging and age-related neuropsychiatric disorders., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Tomohisa Okada received a grant from Siemens Healthcare K.K., Japan for a 7 Tesla MR research. The remaining authors declare no conflict of interest., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Several cardiovascular and metabolic indicators, such as cholesterol and blood pressure have been associated with altered neural and cognitive health as well as increased risk of dementia and Alzheimer's disease in later life. In this cross-sectional study, we examined how an aggregate index of cardiovascular and metabolic risk factor measures was associated with correlation-based estimates of resting-state functional connectivity (FC) across a broad adult age-span (36-90+ years) from 930 volunteers in the Human Connectome Project Aging (HCP-A). Increased (i.e., worse) aggregate cardiometabolic scores were associated with reduced FC globally, with especially strong effects in insular, medial frontal, medial parietal, and superior temporal regions. Additionally, at the network-level, FC between core brain networks, such as default-mode and cingulo-opercular, as well as dorsal attention networks, showed strong effects of cardiometabolic risk. These findings highlight the lifespan impact of cardiovascular and metabolic health on whole-brain functional integrity and how these conditions may disrupt higher-order network integrity., Competing Interests: Declaration of Competing Interest The authors report no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text 1,2 or sound 3,4 . Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary 1-7 . Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant-who can no longer speak intelligibly owing to amyotrophic lateral sclerosis-achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI 2 ) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant's attempted speech was decoded  at 62 words per minute, which is 3.4 times as fast as the previous record 8 and begins to approach the speed of natural conversation (160 words per minute 9 ). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak., (© 2023. The Author(s).)

Night monkeys (Aotus) are the only genus of monkeys within the Simian lineage that successfully occupy a nocturnal environmental niche. Their behavior is supported by their sensory organs' distinctive morphological features; however, little is known about their evolutionary adaptations in sensory regions of the cerebral cortex. Here, we investigate this question by exploring the cortical organization of night monkeys using high-resolution in-vivo brain MRI and comparative cortical-surface T1w/T2w myeloarchitectonic mapping. Our results show that the night monkey cerebral cortex has a qualitatively similar but quantitatively different pattern of cortical myelin compared to the diurnal macaque and marmoset monkeys. T1w/T2w myelin and its gradient allowed us to parcellate high myelin areas, including the middle temporal complex (MT +) and auditory cortex, and a low-myelin area, Brodmann area 7 (BA7) in the three species, despite species differences in cortical convolutions. Relative to the total cortical-surface area, those of MT + and the auditory cortex are significantly larger in night monkeys than diurnal monkeys, whereas area BA7 occupies a similar fraction of the cortical sheet in all three species. We propose that the selective expansion of sensory areas dedicated to visual motion and auditory processing in night monkeys may reflect cortical adaptations to a nocturnal environment., (© 2022. The Author(s).)

Introduction: Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability., Methods: To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features., Results: The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform., Discussion: Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease., Competing Interests: JJ is an employee of Manifest Technologies and has previously worked for Neumora (formerly BlackThorn Therapeutics) and is a co-inventor on the following patent: AA, JM, and JJ: systems and methods for neuro-behavioral relationships in dimensional geometric embedding (N-BRIDGE), PCT International Application No. PCT/US2119/022110, filed March 13, 2019. AK and AM have previously consulted for Neumora (formerly BlackThorn Therapeutics). CF, JD, and ZT have previously consulted for Neumora (formerly BlackThorn Therapeutics) and consult for Manifest Technologies. MHe and LP are employees of Manifest Technologies. VZ and SS consults for Manifest Technologies. JM and AA consult for and hold equity with Neumora (formerly BlackThorn Therapeutics), Manifest Technologies, and are co-inventors on the following patents: JM, AA, and Martin, WJ: Methods and tools for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject, U.S. Application No. 16/149,903 filed on October 2, 2018, U.S. Application for PCT International Application No. 18/054,009 filed on October 2, 2018. GR consults for and holds equity with Neumora (formerly BlackThorn Therapeutics) and Manifest Technologies. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ji, Demšar, Fonteneau, Tamayo, Pan, Kraljič, Matkovič, Purg, Helmer, Warrington, Winkler, Zerbi, Coalson, Glasser, Harms, Sotiropoulos, Murray, Anticevic and Repovš.)

An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.

T1-weighted divided by T2-weighted (T1w/T2w) myelin maps were initially developed for neuroanatomical analyses such as identifying cortical areas, but they are increasingly used in statistical comparisons across individuals and groups with other variables of interest. Existing T1w/T2w myelin maps contain radiofrequency transmit field (B1+) biases, which may be correlated with these variables of interest, leading to potentially spurious results. Here we propose two empirical methods for correcting these transmit field biases using either explicit measures of the transmit field or alternatively a 'pseudo-transmit' approach that is highly correlated with the transmit field at 3T. We find that the resulting corrected T1w/T2w myelin maps are both better neuroanatomical measures (e.g., for use in cross-species comparisons), and more appropriate for statistical comparisons of relative T1w/T2w differences across individuals and groups (e.g., sex, age, or body-mass-index) within a consistently acquired study at 3T. We recommend that investigators who use the T1w/T2w approach for mapping cortical myelin use these B1+ transmit field corrected myelin maps going forward., (Copyright © 2022. Published by Elsevier Inc.)

Adolescence is characterized by the maturation of cortical microstructure and connectivity supporting complex cognition and behavior. Axonal myelination influences brain connectivity during development by enhancing neural signaling speed and inhibiting plasticity. However, the maturational timing of cortical myelination during human adolescence remains poorly understood. Here, we take advantage of recent advances in high-resolution cortical T1w/T2w mapping methods, including principled correction of B1+ transmit field effects, using data from the Human Connectome Project in Development (HCP-D; N = 628, ages 8-21). We characterize microstructural changes relevant to myelination by estimating age-related differences in T1w/T2w throughout the cerebral neocortex from childhood to early adulthood. We apply Bayesian spline models and clustering analysis to demonstrate graded variation in age-dependent cortical T1w/T2w differences that are correlated with the sensorimotor-association (S-A) axis of cortical organization reported by others. In sensorimotor areas, T1w/T2w ratio measures start at high levels at early ages, increase at a fast pace, and decelerate at later ages (18-21). In intermediate multimodal areas along the S-A axis, T1w/T2w starts at intermediate levels and increases linearly at an intermediate pace. In transmodal/paralimbic association areas, T1w/T2w starts at low levels and increases linearly at the slowest pace. These data provide evidence for graded variation of the T1w/T2w ratio along the S-A axis that may reflect cortical myelination changes during adolescence underlying the development of complex information processing and psychological functioning. We discuss the implications of these results as well as caveats in interpreting magnetic resonance imaging (MRI)-based estimates of myelination. SIGNIFICANCE STATEMENT Myelin is a lipid membrane that is essential to healthy brain function. Myelin wraps axons to increase neural signaling speed, enabling complex neuronal functioning underlying learning and cognition. Here, we characterize the developmental timing of myelination across the cerebral cortex during adolescence using a noninvasive proxy measure, T1w/T2w mapping. Our results provide new evidence demonstrating graded variation across the cortex in the timing of T1w/T2w changes during adolescence, with rapid T1w/T2w increases in lower-order sensory areas and gradual T1w/T2w increases in higher-order association areas. This spatial pattern of microstructural brain development closely parallels the sensorimotor-to-association axis of cortical organization and plasticity during ontogeny., (Copyright © 2022 the authors.)

Recent functional MRI studies identified sensory-biased regions across much of the association cortices and cerebellum. However, their anatomical relationship to multiple-demand (MD) regions, characterized as domain-general due to their coactivation during multiple cognitive demands, remains unclear. For a better anatomical delineation, we used multimodal MRI techniques of the Human Connectome Project to scan subjects performing visual and auditory versions of a working memory (WM) task. The contrast between hard and easy WM showed strong domain generality, with essentially identical patterns of cortical, subcortical, and cerebellar MD activity for visual and auditory materials. In contrast, modality preferences were shown by contrasting easy WM with baseline; most MD regions showed visual preference while immediately adjacent to cortical MD regions, there were interleaved regions of both visual and auditory preference. The results may exemplify a general motif whereby domain-specific regions feed information into and out of an adjacent, integrative MD core., (© The Author(s) 2021. Published by Oxford University Press.)

Localising accurate brain regions needs careful evaluation in each experimental species due to their individual variability. However, the function and connectivity of brain areas is commonly studied using a single-subject cranial landmark-based stereotactic atlas in animal neuroscience. Here, we address this issue in a small primate, the common marmoset, which is increasingly widely used in systems neuroscience. We developed a non-invasive multi-modal neuroimaging-based targeting pipeline, which accounts for intersubject anatomical variability in cranial and cortical landmarks in marmosets. This methodology allowed creation of multi-modal templates (MarmosetRIKEN20) including head CT and brain MR images, embedded in coordinate systems of anterior and posterior commissures (AC-PC) and CIFTI grayordinates. We found that the horizontal plane of the stereotactic coordinate was significantly rotated in pitch relative to the AC-PC coordinate system (10 degrees, frontal downwards), and had a significant bias and uncertainty due to positioning procedures. We also found that many common cranial and brain landmarks (e.g., bregma, intraparietal sulcus) vary in location across subjects and are substantial relative to average marmoset cortical area dimensions. Combining the neuroimaging-based targeting pipeline with robot-guided surgery enabled proof-of-concept targeting of deep brain structures with an accuracy of 0.2 mm. Altogether, our findings demonstrate substantial intersubject variability in marmoset brain and cranial landmarks, implying that subject-specific neuroimaging-based localization is needed for precision targeting in marmosets. The population-based templates and atlases in grayordinates, created for the first time in marmoset monkeys, should help bridging between macroscale and microscale analyses., Competing Interests: Declaration of Competing Interest Stephen Frey is employed by Rogue Research Inc. All the other authors declare no competing financial interests., (Copyright © 2022. Published by Elsevier Inc.)

Brain atrophy occurs in aging even in the absence of dementia, but it is unclear to what extent this is due to undetected preclinical Alzheimer disease. Here we examine a cross-sectional cohort (ages 18-88) free from confounding influence of preclinical Alzheimer disease, as determined by amyloid PET scans and three years of clinical evaluation post-imaging. We determine the regional strength of age-related atrophy using linear modeling of brain volumes and cortical thicknesses with age. Age-related atrophy was seen in nearly all regions, with greatest effects in the temporal lobe and subcortical regions. When modeling age with the estimated derivative of smoothed aging curves, we found that the temporal lobe declined linearly with age, subcortical regions declined faster at later ages, and frontal regions declined slower at later ages than during midlife. This age-derivative pattern was distinct from the linear measure of age-related atrophy and significantly associated with a measure of myelin. Atrophy did not detectably differ from a preclinical Alzheimer disease cohort when age ranges were matched., (Copyright © 2021. Published by Elsevier Inc.)

Social interaction is thought to provide a selection pressure for human intelligence, yet little is known about its neurobiological basis and evolution throughout the primate lineage. Recent advances in neuroimaging have enabled whole brain investigation of brain structure, function, and connectivity in humans and non-human primates (NHPs), leading to a nascent field of comparative connectomics. However, linking social behavior to brain organization across the primates remains challenging. Here, we review the current understanding of the macroscale neural mechanisms of social behaviors from the viewpoint of system neuroscience. We first demonstrate an association between the number of cortical neurons and the size of social groups across primates, suggesting a link between neural information-processing capacity and social capabilities. Moreover, by capitalizing on recent advances in species-harmonized functional MRI, we demonstrate that portions of the mirror neuron system and default-mode networks, which are thought to be important for representation of the other's actions and sense of self, respectively, exhibit similarities in functional organization in macaque monkeys and humans, suggesting possible homologies. With respect to these two networks, we describe recent developments in the neurobiology of social perception, joint attention, personality and social complexity. Together, the Human Connectome Project (HCP)-style comparative neuroimaging, hyperscanning, behavioral, and other multi-modal investigations are expected to yield important insights into the evolutionary foundations of human social behavior., (Copyright © 2021. Published by Elsevier Inc.)

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest in relation to this manuscript., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Research into the human connectome (i.e., all connections in the human brain) with the use of resting state functional MRI has rapidly increased in popularity in recent years, especially with the growing availability of large-scale neuroimaging datasets. The goal of this review article is to describe innovations in functional connectome representations that have come about in the past 8 years, since the 2013 NeuroImage special issue on 'Mapping the Connectome'. In the period, research has shifted from group-level brain parcellations towards the characterization of the individualized connectome and of relationships between individual connectomic differences and behavioral/clinical variation. Achieving subject-specific accuracy in parcel boundaries while retaining cross-subject correspondence is challenging, and a variety of different approaches are being developed to meet this challenge, including improved alignment, improved noise reduction, and robust group-to-subject mapping approaches. Beyond the interest in the individualized connectome, new representations of the data are being studied to complement the traditional parcellated connectome representation (i.e., pairwise connections between distinct brain regions), such as methods that capture overlapping and smoothly varying patterns of connectivity ('gradients'). These different connectome representations offer complimentary insights into the inherent functional organization of the brain, but challenges for functional connectome research remain. Interpretability will be improved by future research towards gaining insights into the neural mechanisms underlying connectome observations obtained from functional MRI. Validation studies comparing different connectome representations are also needed to build consensus and confidence to proceed with clinical trials that may produce meaningful clinical translation of connectome insights., (Copyright © 2021. Published by Elsevier Inc.)