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Contains fulltext : 196820.pdf (publisher's version ) (Open Access), Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

Item does not contain fulltext, Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

Medical imaging has enormous potential for early disease prediction, but is impeded by the difficulty and expense of acquiring data sets before symptom onset. UK Biobank aims to address this problem directly by acquiring high-quality, consistently acquired imaging data from 100,000 predominantly healthy participants, with health outcomes being tracked over the coming decades. The brain imaging includes structural, diffusion and functional modalities. Along with body and cardiac imaging, genetics, lifestyle measures, biological phenotyping and health records, this imaging is expected to enable discovery of imaging markers of a broad range of diseases at their earliest stages, as well as provide unique insight into disease mechanisms. We describe UK Biobank brain imaging and present results derived from the first 5,000 participants' data release. Although this covers just 5% of the ultimate cohort, it has already yielded a rich range of associations between brain imaging and other measures collected by UK Biobank.

Item does not contain fulltext, The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h(2)=0.53-0.90, p<10(-5)), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application.

The precise delineation of auditory areas in vivo remains problematic. Histological analysis of postmortem tissue indicates that the relation of areal borders to macroanatomical landmarks is variable across subjects. Furthermore, functional parcellation schemes based on measures of, for example, frequency preference (tonotopy) remain controversial. Here, we propose a 7 Tesla magnetic resonance imaging method that enables the anatomical delineation of auditory cortical areas in vivo and in individual brains, through the high-resolution visualization (0.6 x 0.6 x 0.6 mm(3)) of intracortical anatomical contrast related to myelin. The approach combines the acquisition and analysis of images with multiple MR contrasts (T-1, T-2*, and proton density). Compared with previous methods, the proposed solution is feasible at high fields and time efficient, which allows collecting myelin-related and functional images within the same measurement session. Our results show that a data-driven analysis of cortical depth-dependent profiles of anatomical contrast allows identifying a most densely myelinated cortical region on the medial Heschl's gyrus. Analyses of functional responses show that this region includes neuronal populations with typical primary functional properties (single tonotopic gradient and narrow frequency tuning), thus indicating that it may correspond to the human homolog of monkey A1.

Musical notes played at octave intervals (i.e., having the same pitch chroma) are perceived as similar. This well-known perceptual phenomenon lays at the foundation of melody recognition and music perception, yet its neural underpinnings remain largely unknown to date. Using fMRI with high sensitivity and spatial resolution, we examined the contribution of multi-peak spectral tuning to the neural representation of pitch chroma in human auditory cortex in two experiments. In experiment 1, our estimation of population spectral tuning curves from the responses to natural sounds confirmed-with new data-our recent results on the existence of cortical ensemble responses finely tuned to multiple frequencies at one octave distance (Moerel et al., 2013). In experiment 2, we fitted a mathematical model consisting of a pitch chroma and height component to explain the measured fMRI responses to piano notes. This analysis revealed that the octave-tuned populations-but not other cortical populations-harbored a neural representation of musical notes according to their pitch chroma. These results indicate that responses of auditory cortical populations selectively tuned to multiple frequencies at one octave distance predict well the perceptual similarity of musical notes with the same chroma, beyond the physical (frequency) distance of notes.

Columnar arrangements of neurons with similar preference have been suggested as the fundamental processing units of the cerebral cortex. Within these columnar arrangements, feed-forward information enters at middle cortical layers whereas feedback information arrives at superficial and deep layers. This interplay of feed-forward and feedback processing is at the core of perception and behavior. Here we provide in vivo evidence consistent with a columnar organization of the processing of sound frequency in the human auditory cortex. We measure submillimeter functional responses to sound frequency sweeps at high magnetic fields (7 tesla) and show that frequency preference is stable through cortical depth in primary auditory cortex. Furthermore, we demonstrate that-in this highly columnar cortex-task demands sharpen the frequency tuning in superficial cortical layers more than in middle or deep layers. These findings are pivotal to understanding mechanisms of neural information processing and flow during the active perception of sounds.

To date it remains largely unknown how fundamental aspects of natural sounds, such as their spectral content and location in space, are processed in human subcortical structures. Here we exploited the high sensitivity and specificity of high field fMRI (7 Tesla) to examine the human inferior colliculus (IC) and medial geniculate body (MGB). Subcortical responses to natural sounds were well explained by an encoding model of sound processing that represented frequency and location jointly. Frequency tuning was organized in one tonotopic gradient in the IC, whereas two tonotopic maps characterized the MGB reflecting two MGB subdivisions. In contrast, no topographic pattern of preferred location was detected, beyond an overall preference for peripheral (as opposed to central) and contralateral locations. Our findings suggest the functional organization of frequency and location processing in human subcortical auditory structures, and pave the way for studying the subcortical to cortical interaction required to create coherent auditory percepts.

Item does not contain fulltext, The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and

Functional neuroimaging research provides detailed observations of the response patterns that natural sounds (e.g. human voices and speech, animal cries, environmental sounds) evoke in the human brain. The computational and representational mechanisms underlying these observations, however, remain largely unknown. Here we combine high spatial resolution (3 and 7 Tesla) functional magnetic resonance imaging (fMRI) with computational modeling to reveal how natural sounds are represented in the human brain. We compare competing models of sound representations and select the model that most accurately predicts fMRI response patterns to natural sounds. Our results show that the cortical encoding of natural sounds entails the formation of multiple representations of sound spectrograms with different degrees of spectral and temporal resolution. The cortex derives these multi-resolution representations through frequency-specific neural processing channels and through the combined analysis of the spectral and temporal modulations in the spectrogram. Furthermore, our findings suggest that a spectral-temporal resolution trade-off may govern the modulation tuning of neuronal populations throughout the auditory cortex. Specifically, our fMRI results suggest that neuronal populations in posterior/dorsal auditory regions preferably encode coarse spectral information with high temporal precision. Vice-versa, neuronal populations in anterior/ventral auditory regions preferably encode fine-grained spectral information with low temporal precision. We propose that such a multi-resolution analysis may be crucially relevant for flexible and behaviorally-relevant sound processing and may constitute one of the computational underpinnings of functional specialization in auditory cortex.

BACKGROUND: The Whitehall II (WHII) study of British civil servants provides a unique source of longitudinal data to investigate key factors hypothesized to affect brain health and cognitive ageing. This paper introduces the multi-modal magnetic resonance imaging (MRI) protocol and cognitive assessment designed to investigate brain health in a random sample of 800 members of the WHII study. METHODS/DESIGN: A total of 6035 civil servants participated in the WHII Phase 11 clinical examination in 2012-2013. A random sample of these participants was included in a sub-study comprising an MRI brain scan, a detailed clinical and cognitive assessment, and collection of blood and buccal mucosal samples for the characterisation of immune function and associated measures. Data collection for this sub-study started in 2012 and will be completed by 2016. The participants, for whom social and health records have been collected since 1985, were between 60-85 years of age at the time the MRI study started. Here, we describe the pre-specified clinical and cognitive assessment protocols, the state-of-the-art MRI sequences and latest pipelines for analyses of this sub-study. DISCUSSION: The integration of cutting-edge MRI techniques, clinical and cognitive tests in combination with retrospective data on social, behavioural and biological variables during the preceding 25 years from a well-established longitudinal epidemiological study (WHII cohort) will provide a unique opportunity to examine brain structure and function in relation to age-related diseases and the modifiable and non-modifiable factors affecting resilience against and vulnerability to adverse brain changes.

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To date, the functional organization of human auditory subcortical structures can only be inferred from animal models. Here we use high-resolution functional magnetic resonance imaging at ultra-high magnetic fields (7T) to map the organization of spectral responses in the human inferior colliculus, a subcortical structure fundamental for sound processing. We reveal a tonotopic map with a spatial gradient of preferred frequencies approximately oriented from dorsolateral (low frequencies) to ventromedial (high frequencies) locations. Furthermore, we observe a spatial organization of spectral selectivity (tuning) of functional magnetic resonance imaging responses in the human inferior colliculus. Along isofrequency contours, functional magnetic resonance imaging tuning is narrowest in central locations and broadest in the surrounding regions. Finally, by comparing subcortical and cortical auditory areas we show that functional magnetic resonance imaging tuning is narrower in human inferior colliculus than on the cortical surface. Our findings pave the way to noninvasive investigations of sound processing in human subcortical nuclei and for studying the interplay between subcortical and cortical neuronal populations.

We examine the mechanisms by which the human auditory cortex processes the frequency content of natural sounds. Through mathematical modeling of ultra-high field (7 T) functional magnetic resonance imaging responses to natural sounds, we derive frequency-tuning curves of cortical neuronal populations. With a data-driven analysis, we divide the auditory cortex into five spatially distributed clusters, each characterized by a spectral tuning profile. Beyond neuronal populations with simple single-peaked spectral tuning (grouped into two clusters), we observe that similar to 60% of auditory populations are sensitive to multiple frequency bands. Specifically, we observe sensitivity to multiple frequency bands (1) at exactly one octave distance from each other, (2) at multiple harmonically related frequency intervals, and (3) with no apparent relationship to each other. We propose that beyond the well known cortical tonotopic organization, multipeaked spectral tuning amplifies selected combinations of frequency bands. Such selective amplification might serve to detect behaviorally relevant and complex sound features, aid in segregating auditory scenes, and explain prominent perceptual phenomena such as octave invariance.

Item does not contain fulltext, The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

Ultra high fields (UHF) permit unprecedented explorations of functional organizations and insight into basic neuronal processes. Increases in the signal and contrast to noise ratios have allowed increases in the spatial resolution of T(2)() weighted gradient echo (GE) echo planar imaging (EPI). Furthermore, while the use of T(2) weighted imaging methods at UHF (e.g. spin echo (SE) EPI, gradient and spin echo (GRASE) EPI) can also permit higher resolution images, they in addition allow for increased spatial specificity of functional responses, permitting the in-vivo study of functional organizations down to the columnar level of the cortex. The study of the visual cortex has, thus far, benefitted the most from higher resolution T(2) weighted studies as achieving the required transmit B(1) magnitude at 7T is more challenging in other brain regions, such as the auditory cortex. As such, auditory fMRI studies at UHF have been limited to T(2)* weighted GE sequences. Recent advances in multi-channel RF transmission (e.g. B(1) shimming) have enabled procedures to efficiently address deficiencies in transmit B(1) profiles. However, these techniques, shown to be advantageous in anatomical imaging at UHF, are not generally utilized to facilitate T(2) weighted fMRI studies. Here we investigate the feasibility of applying B(1) shimming to achieve efficient RF transmission in the human auditory cortex. We demonstrate that, with B(1) shimming, functional responses to simple tones and to complex sounds (i.e. voices, speech, animal cries, tools and nature) can be efficiently measured with T(2) weighted SE-EPI in the bilateral human auditory cortex at 7T without exceeding specific absorption rate (SAR) limits.

Whole-brain functional magnetic resonance imaging (fMRI) allows measuring brain dynamics at all brain regions simultaneously and is widely used in research and clinical neuroscience to observe both stimulus-related and spontaneous neural activity. Ultrahigh magnetic fields (7T and above) allow functional imaging with high contrast-to-noise ratios and improved spatial resolution and specificity compared to clinical fields (1.5T and 3T). High-resolution 7T fMRI, however, has been mostly limited to partial brain coverage with previous whole-brain applications sacrificing either the spatial or temporal resolution. Here we present whole-brain high-resolution (1, 1.5 and 2mm isotropic voxels) resting state fMRI at 7T, obtained with parallel imaging technology, without sacrificing temporal resolution or brain coverage, over what is typically achieved at 3T with several fold larger voxel volumes. Using Independent Component Analysis we demonstrate that high resolution images acquired at 7T retain enough sensitivity for the reliable extraction of typical resting state brain networks and illustrate the added value of obtaining both single subject and group maps, using cortex based alignment, of the default-mode network (DMN) with high native resolution. By comparing results between multiple resolutions we show that smaller voxels volumes (1 and 1.5mm isotropic) data result in reduced partial volume effects, permitting separations of detailed spatial features within the DMN patterns as well as a better function to anatomy correspondence.