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Abstract The human cerebral cortex is estimated to comprise 200–300 distinct functional regions per hemisphere. Identification of the precise anatomical location of an individual's unique set of functional regions is a challenge for neuroscience that has broad scientific and clinical utility. Recent studies have demonstrated the existence of four interleaved regions in lateral frontal cortex (LFC) that are part of broader visual attention and auditory attention networks (Michalka et al., 2015; Noyce et al., 2017; Tobyne et al., 2017). Due to a large degree of inter-subject anatomical variability, identification of these regions depends critically on within-subject analyses. Here, we demonstrate that, for both sexes, an individual's unique pattern of resting-state functional connectivity can accurately identify their specific pattern of visual- and auditory-selective working memory and attention task activation in lateral frontal cortex (LFC) using "connectome fingerprinting." Building on prior techniques (Saygin et al., 2011; Osher et al., 2016; Tavor et al., 2016; Smittenaar et al., 2017; Wang et al., 2017; Parker Jones et al., 2017), we demonstrate here that connectome fingerprint predictions are far more accurate than group-average predictions and match the accuracy of within-subject task-based functional localization, while requiring less data. These findings are robust across brain parcellations and are improved with penalized regression methods. Because resting-state data can be easily and rapidly collected, these results have broad implications for both clinical and research investigations of frontal lobe function. Our findings also provide a set of recommendations for future research. Graphical abstract Image 1 Highlights • Multiple visual- and auditory-biased regions exist in lateral frontal cortex (LFC). • 'Connectome Fingerprinting' (CF) attempts to identify brain regions in individuals. • CF uses resting-state functional connectivity to predict task activation patterns. • CF accurately predicts location of sensory modality-selective regions in LFC. • Our findings also present 'best practice' recommendations for performing CF. [ABSTRACT FROM AUTHOR]

Abstract: The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings. [Copyright &y& Elsevier]

Abstract: Transcranial direct current stimulation (tDCS) can up- and down-regulate cortical excitability depending on current direction, however our abilities to measure brain-tissue effects of the stimulation and its after-effects have been limited so far. We used regional cerebral blood flow (rCBF), a surrogate measure of brain activity, to examine regional brain-tissue and brain-network effects during and after tDCS. We varied the polarity (anodal and cathodal) as well as the current strength (0.8 to 2.0mA) of the stimulation. Fourteen healthy subjects were randomized into receiving either anodal or cathodal stimulation (two subjects received both, one week apart) while undergoing Arterial Spin Labeling (ASL) in the MRI scanner with an alternating off–on sampling paradigm. The stimulating, MRI-compatible electrode was placed over the right motor region and the reference electrode over the contralateral supra-orbital region. SPM5 was used to process and extract the rCBF data using a 10mm spherical volume of interest (VOI) placed in the motor cortex directly underneath the stimulating scalp electrode. Anodal stimulation induced a large increase (17.1%) in rCBF during stimulation, which returned to baseline after the current was turned off, but exhibited an increase in rCBF again in the post-stimulation period. Cathodal stimulation induced a smaller increase (5.6%) during stimulation, a significant decrease compared to baseline (−6.5%) after cessation, and a continued decrease in the post-stimulation period. These changes in rCBF were all significant when compared to the pre-stimulation baseline or to a control region. Furthermore, for anodal stimulation, there was a significant correlation between current strength and the increase in rCBF in the on-period relative to the pre-stimulation baseline. The differential rCBF after-effects of anodal (increase in resting state rCBF) and cathodal (decrease in resting state rCBF) tDCS support findings of behavioral and cognitive after-effects after cathodal and anodal tDCS. We also show that tDCS not only modulates activity in the brain region directly underlying the stimulating electrode but also in a network of brain regions that are functionally related to the stimulated area. Our results indicate that ASL may be an excellent tool to investigate the effects of tDCS and its stimulation parameters on brain activity. [Copyright &y& Elsevier]

Abstract: During Pavlovian conditioning the expression of a conditioned response is typically taken as evidence that an association between a conditioned stimulus (CS) and an unconditioned stimulus (UCS) has been formed. However, learning-related changes in the unconditioned response (UCR) produced by a predictable UCS can also develop. Learning-related reductions in UCR magnitude are often referred to as UCR diminution. In the present study, we examined UCR diminution in the functional magnetic resonance imaging (fMRI) signal by pairing supra- and sub-threshold CS presentations with a UCS. UCR diminution was observed within several brain regions associated with fear learning and memory including the insula, inferior parietal lobe, ventromedial prefrontal cortex (PFC), dorsomedial PFC, and dorsolateral PFC. CS perception appeared to mediate UCR diminution within the ventromedial PFC and posterior cingulate cortex. UCRs within these regions were larger when the UCS followed an unperceived compared to a perceived CS. UCS expectancies appeared to modulate UCRs within the dorsomedial PFC, dorsolateral PFC, insula, and inferior parietal lobe. Activity within these regions showed an inverse relationship with participants'' UCS expectancies, such that as UCS expectancy increased UCR magnitude decreased. In addition, activity within the dorsomedial PFC, dorsolateral PFC, and insula showed a linear relationship with unconditioned skin conductance response (SCR) expression. These findings demonstrate UCR diminution within the fMRI signal, and suggest that UCS expectancies modulate prefrontal cortex responses to aversive stimuli. In turn, prefrontal cortex activity appears to modulate the expression of unconditioned SCRs. [Copyright &y& Elsevier]

The high evolutionary value of learning when to respond to threats or when to inhibit previously learned associations after changing threat contingencies is reflected in dedicated networks in the animal and human brain. Recent evidence further suggests that adaptive learning may be dependent on the dynamic interaction of meta-stable functional brain networks. However, it is still unclear which functional brain networks compete with each other to facilitate associative learning and how changes in threat contingencies affect this competition. The aim of this study was to assess the dynamic competition between large-scale networks related to associative learning in the human brain by combining a repeated differential conditioning and extinction paradigm with independent component analysis of functional magnetic resonance imaging data. The results (i) identify three task-related networks involved in initial and sustained conditioning as well as extinction, and demonstrate that (ii) the two main networks that underlie sustained conditioning and extinction are anti-correlated with each other and (iii) the dynamic competition between these two networks is modulated in response to changes in associative contingencies. These findings provide novel evidence for the view that dynamic competition between large-scale functional networks differentiates fear conditioning from extinction learning in the healthy brain and suggest that dysfunctional network dynamics might contribute to learning-related neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

Abstract: Connectome genetics attempts to discover how genetic factors affect brain connectivity. Here we review a variety of genetic analysis methods—such as genome-wide association studies (GWAS), linkage and candidate gene studies—that have been fruitfully adapted to imaging data to implicate specific variants in the genome for brain-related traits. Studies that emphasized the genetic influences on brain connectivity. Some of these analyses of brain integrity and connectivity using diffusion MRI, and others have mapped genetic effects on functional networks using resting state functional MRI. Connectome-wide genome-wide scans have also been conducted, and we review the multivariate methods required to handle the extremely high dimension of the genomic and network data. We also review some consortium efforts, such as ENIGMA, that offer the power to detect robust common genetic associations using phenotypic harmonization procedures and meta-analysis. Current work on connectome genetics is advancing on many fronts and promises to shed light on how disease risk genes affect the brain. It is already discovering new genetic loci and even entire genetic networks that affect brain organization and connectivity. [Copyright &y& Elsevier]

Resting state fMRI (rs-fMRI) provides imaging biomarkers of task-independent brain function that can be associated with clinical variables or modulated by interventions such as behavioral training or pharmacological manipulations. These biomarkers include time-averaged regional brain function as manifested by regional cerebral blood flow (CBF) measured using arterial spin labeled (ASL) perfusion MRI and correlated temporal fluctuations of function across brain networks with either ASL or blood oxygenation level dependent (BOLD) fMRI. Resting-state studies are typically carried out using just one of several prescribed state conditions such as eyes closed (EC), eyes open (EO), or visual fixation on a cross-hair (FIX), which may affect the reliability and specificity of rs-fMRI. In this study, we collected test-retest ASL MRI data during 4 resting-state task conditions: EC, EO, FIX and PVT (low-frequency psychomotor vigilance task), and examined the effects of these task conditions on reliability and reproducibility as well as trait specificity of regional brain function. We also acquired resting-state BOLD fMRI under FIX and compared the network connectivity reliabilities between the four ASL conditions and the BOLD FIX condition. For resting-state ASL data, EC provided the highest CBF reliability, reproducibility, trait specificity, and network connectivity reliability, followed by EO, while FIX was lowest on all of these measures. PVT demonstrated lower CBF reliability, reproducibility and trait specificity than EO and EC. Overall network connectivity reliability was comparable between ASL and BOLD. Our findings confirm ASL CBF as a reliable, stable, and consistent measure of resting-state regional brain function and support the use of EC or EO over FIX and PVT as the resting-state condition. [ABSTRACT FROM AUTHOR]

ConnectomeDB is a database for housing and disseminating data about human brain structure, function, and connectivity, along with associated behavioral and demographic data. It is the main archive and dissemination platform for data collected under the WU-Minn consortium Human Connectome Project. Additional connectome-style study data is and will be made available in the database under current and future projects, including the Connectome Coordination Facility. The database currently includes multiple modalities of magnetic resonance imaging (MRI) and magnetoencephalograpy (MEG) data along with associated behavioral data. MRI modalities include structural, task, resting state and diffusion. MEG modalities include resting state and task. Imaging data includes unprocessed, minimally preprocessed and analysis data. Imaging data and much of the behavioral data are publicly available, subject to acceptance of data use terms, while access to some sensitive behavioral data is restricted to qualified investigators under a more stringent set of terms. ConnectomeDB is the public side of the WU-Minn HCP database platform. As such, it is geared towards public distribution, with a web-based user interface designed to guide users to the optimal set of data for their needs and a robust backend mechanism based on the commercial Aspera fasp service to enable high speed downloads. HCP data is also available via direct shipment of hard drives and Amazon S3. [ABSTRACT FROM AUTHOR]

In functional magnetic resonance imaging (fMRI), the hemodynamic response function (HRF) reflects regulation of regional cerebral blood flow in response to neuronal activation. The HRF varies significantly between individuals. This study investigated the genetic contribution to individual variation in HRF using fMRI data from 125 monozygotic (MZ) and 149 dizygotic (DZ) twin pairs. The resemblance in amplitude, latency, and duration of the HRF in six regions in the frontal and parietal lobes was compared between MZ and DZ twin pairs. Heritability was estimated using an ACE (Additive genetic, Common environmental, and unique Environmental factors) model. The genetic influence on the temporal profile and amplitude of HRF was moderate to strong (24%–51%). The HRF may be used in the genetic analysis of diseases with a cerebrovascular etiology. [ABSTRACT FROM AUTHOR]

Abstract: The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels. [Copyright &y& Elsevier]

Abstract: The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013. [Copyright &y& Elsevier]

Abstract: The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. [Copyright &y& Elsevier]

Abstract: In patients presenting with cerebral ischemic injury, the outcome of injured brain tissue quantified as decreased apparent diffusion coefficient (ADC) may depend on associated alterations in cerebral blood perfusion (CBP). This study proposes a non-biased method to quantify associations between ADC and CBP in newborns with global or focal cerebral ischemia. The study population consisted of nine neonates (age: 0 to 3days) presenting with clinical and imaging evidence of ischemia (seven with global hypoxic ischemia, and two with focal arterial ischemic stroke) with decreased ADC. Six newborns without diffusion abnormalities on magnetic resonance (MR) imaging served as a comparative cohort (age: 0days to 4weeks). All patients underwent MR imaging including diffusion weighted imaging (DWI) to determine ADC and axial arterial spin labeling (ASL) to determine CBP. An algorithm was developed that uses the B0 volume from the DWI raw data as a reference, co-registers the ADC and ASL-CBP data to the B0, generates mask filters, and finally performs a statistical analysis to automatically select regions of interest (ROIs) with ADC or ASL-CBP values that deviate significantly from the rest of the brain. If ROIs are identified in this analysis, the algorithm then evaluates correlation based on ROI location and volume. A significant correlation was found between decreased ADC and elevated ASL-CBP with regions of elevated ASL-CBP typically larger than the corresponding ADC abnormality. The association between decreased diffusivity and increased ASL-CBP suggests that, for this cohort, cerebral ischemia is associated with hyperperfusion. [Copyright &y& Elsevier]

Abstract: Structural analysis of MRI data on the cortical surface usually focuses on cortical thickness. Cortical surface area, when considered, has been measured only over gross regions or approached indirectly via comparisons with a standard brain. Here we demonstrate that direct measurement and comparison of the surface area of the cerebral cortex at a fine scale is possible using mass conservative interpolation methods. We present a framework for analyses of the cortical surface area, as well as for any other measurement distributed across the cortex that is areal by nature. The method consists of the construction of a mesh representation of the cortex, registration to a common coordinate system and, crucially, interpolation using a pycnophylactic method. Statistical analysis of surface area is done with power-transformed data to address lognormality, and inference is done with permutation methods. We introduce the concept of facewise analysis, discuss its interpretation and potential applications. [Copyright &y& Elsevier]

Abstract: Volume-based registration (VBR) is the predominant method used in human neuroimaging to compensate for individual variability. However, surface-based registration (SBR) techniques have an inherent advantage over VBR because they respect the topology of the convoluted cortical sheet. There is evidence that existing SBR methods indeed confer a registration advantage over affine VBR. Landmark-SBR constrains registration using explicit landmarks to represent corresponding geographical locations on individual and atlas surfaces. The need for manual landmark identification has been an impediment to the widespread adoption of Landmark-SBR. To circumvent this obstacle, we have implemented and evaluated an automated landmark identification (ALI) algorithm for registration to the human PALS-B12 atlas. We compared ALI performance with that from two trained human raters and one expert anatomical rater (ENR). We employed both quantitative and qualitative quality assurance metrics, including a biologically meaningful analysis of hemispheric asymmetry. ALI performed well across all quality assurance tests, indicating that it yields robust and largely accurate results that require only modest manual correction (<10min per subject). ALI largely circumvents human error and bias and enables high throughput analysis of large neuroimaging datasets for inter-subject registration to an atlas. [Copyright &y& Elsevier]

Abstract: The recently proposed track-density imaging (TDI) technique was introduced as a means to achieve super-resolution using diffusion MRI. This technique is able to increase the spatial resolution of the reconstructed images beyond the acquired MRI resolution by incorporating information from whole-brain fibre-tracking results. It not only achieves super-resolution, but also provides very high anatomical contrast with a new MRI contrast mechanism. However, the anatomical information-content of this novel contrast mechanism has not yet been assessed. In this work, we perform such a study using diffusion MRI of ex vivo mouse brains acquired at 16.4T, to compare the results of the super-resolution TDI technique with histological staining (myelin and Nissl stains) in the same brains. Furthermore, a modified version of the directionally-encoded colour TDI map using short-tracks is introduced, which reduces the TDI intensity dynamic range, and therefore enhances the directionality colour-contrast. Good agreement was observed between structures visualised in the super-resolution TDI maps and in the histological sections, supporting the anatomical information-content of the images generated using the TDI technique. The results therefore show that the TDI methodology does provide meaningful and rich anatomical contrast, in addition to achieving super-resolution. Furthermore, this study is the first to show the application of TDI to mouse brain imaging: the high-resolution, high-quality images demonstrate the useful complementary information that can be achieved using super-resolution TDI. [Copyright &y& Elsevier]

Abstract: Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies. [Copyright &y& Elsevier]

Abstract: Understanding the evolutionary forces that produced the human brain is a central problem in neuroscience and human biology. Comparisons across primate species show that both brain volume and gyrification (the degree of folding in the cerebral cortex) have progressively increased during primate evolution and there is a strong positive correlation between these two traits across primate species. The human brain is exceptional among primates in both total volume and gyrification, and therefore understanding the genetic mechanisms influencing variation in these traits will improve our understanding of a landmark feature of our species. Here we show that individual variation in gyrification is significantly heritable in both humans and an Old World monkey (baboons, Papio hamadryas). Furthermore, contrary to expectations based on the positive phenotypic correlation across species, the genetic correlation between cerebral volume and gyrification within both humans and baboons is estimated as negative. These results suggest that the positive relationship between cerebral volume and cortical folding across species cannot be explained by one set of selective pressures or genetic changes. Our data suggest that one set of selective pressures favored the progressive increase in brain volume documented in the primate fossil record, and that a second independent selective process, possibly related to parturition and neonatal brain size, may have favored brains with progressively greater cortical folding. Without a second separate selective pressure, natural selection favoring increased brain volume would be expected to produce less folded, more lissencephalic brains. These results provide initial evidence for the heritability of gyrification, and possibly a new perspective on the evolutionary mechanisms underlying long-term changes in the nonhuman primate and human brain. [Copyright &y& Elsevier]

Abstract: A major challenge in functional neuroimaging is to cope with individual variability in cortical structure and function. Most analyses of cortical function compensate for variability using affine or low-dimensional nonlinear volume-based registration (VBR) of individual subjects to an atlas, which does not explicitly take into account the geometry of cortical convolutions. A promising alternative is to use surface-based registration (SBR), which capitalizes on explicit surface representations of cortical folding patterns in individual subjects. In this study, we directly compare results from SBR and affine VBR in a study of working memory in healthy controls and patients with schizophrenia (SCZ). Each subject''s structural scan was used for cortical surface reconstruction using the SureFit method. fMRI data were mapped directly onto individual cortical surface models, and each hemisphere was registered to the population-average PALS-B12 atlas using landmark-constrained SBR. The precision with which cortical sulci were aligned was much greater for SBR than VBR. SBR produced superior alignment precision across the entire cortex, and this benefit was greater in patients with schizophrenia. We demonstrate that spatial smoothing on the surface provides better resolution and signal preservation than a comparable degree of smoothing in the volume domain. Lastly, the statistical power of functional activation in the working memory task was greater for SBR than for VBR. These results indicate that SBR provides significant advantages over affine VBR when analyzing cortical fMRI activations. Furthermore, these improvements can be even greater in disorders that have associated structural abnormalities. [Copyright &y& Elsevier]