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Despite decades of pursuit, human brain imaging has yet to uncover clear neural correlates of male-female behavioral differences. Given that such behavior does not always align with sex categories, we argue that neuroimaging research may find more success by partitioning subjects along nonbinary gender attributes in addition to sex. We review the handful of studies that have done this, several of which find as good or better association between brain measures and "gender" as they do with "sex." Recent advances in operationalizing "gender" as a multidimensional variable should facilitate such studies, along with discovery-based approaches that mine brain imaging data for gender-associated attributes, independent of sex.

A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.

Encoding higher spatial resolution in simultaneous multi-slice (SMS) EPI is highly dependent on gradient performance, high density receiver coil arrays and pulse sequence optimization. We simulate gradient amplitude and slew rate determination of EPI imaging performance in terms of minimum TE, echo spacing (ES) and spatial resolution. We discuss the effects of image zooming in pulse sequences that have been used for sub-millimeter resolutions and the trade-offs in using partial Fourier and parallel imaging to reduce TE, PSF and ES. Using optimizations for SMS EPI pulse sequences with available gradient and receiver hardware, experimental results in ultra-high resolution (UHR) (0.45-0.5mm isotropic) SMS-EPI fMRI and mapping ocular dominance columns (ODC) in human brain at 0.5 mm isotropic resolution are demonstrated. We discuss promising future directions of UHR fMRI.

Brain aging and associated neurodegeneration constitute a major societal challenge as well as one for the neuroimaging community. A full understanding of the physiological mechanisms underlying neurodegeneration still eludes medical researchers, fuelling the development of in vivo neuroimaging markers. Hence it is increasingly recognized that our understanding of neurodegenerative processes likely will depend upon the available information provided by imaging techniques. At the same time, the imaging techniques are often developed in response to the desire to observe certain physiological processes. In this context, functional MRI (fMRI), which has for decades provided information on neuronal activity, has evolved into a large family of techniques well suited for in vivo observations of brain physiology. Given the rapid technical advances in fMRI in recent years, this review aims to summarize the physiological basis of fMRI observations in healthy aging as well as in age-related neurodegeneration. This review focuses on in-vivo human brain imaging studies in this review and on disease features that can be imaged using fMRI methods. In addition to providing detailed literature summaries, this review also discusses future directions in the study of brain physiology using fMRI in the clinical setting.

Quantitative mapping of water content, especially in the human brain, has the potential to provide important information for the study and diagnosis of diseases associated with a focal or global change in tissue water homeostasis. In the current work, an imaging method for the precise and accurate quantification of tissue water content is presented. The method allows the acquisition of water content maps with voxel dimensions of 1x1x2 mm(3) and full brain coverage in less than 10 min on a standard clinical 1.5 T scanner. The precision was optimised for human brain imaging and possible sources of systematic error were carefully investigated, demonstrating the ability of the method to quantify water content with high accuracy and precision. The approach was validated in phantom experiments and quantitative cerebral water content maps of a group of 10 healthy volunteers were obtained.

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of human brain imaging data, the number of comparisons is so large that this solution breaks down: the local Type II error rate ends up being so large that scientifically meaningful analysis is precluded. Here we propose a novel solution to this problem: allow the Type I error rate to converge to zero along with the Type II error rate. It works because when the Type I error rate per comparison is very small, the accumulation (or global) Type I error rate is also small. This solution is achieved by employing the Likelihood paradigm, which uses likelihood ratios to measure the strength of evidence on a voxel-by-voxel basis. In this paper, we provide theoretical and empirical justification for a likelihood approach to the analysis of human brain imaging data. In addition, we present extensive simulations that show the likelihood approach is viable, leading to ‘cleaner’ looking brain maps and operationally superiority (lower average error rate). Finally, we include a case study on cognitive control related activation in the prefrontal cortex of the human brain.

Open Science is calling for a radical re-thinking of existing scientific practices. Within the neuroimaging community, Open Science practices are taking the form of open data repositories and open lab notebooks. The broad sharing of data that accompanies Open Science, however, raises some difficult ethical and legal issues. With neuroethics as a focusing lens, we explore eight central concerns posed by open data with regard to human brain imaging studies: respect for individuals and communities, concern for marginalized communities, consent, privacy protections, participatory research designs, contextual integrity, fusions of clinical and research goals, and incidental findings. Each consideration assists in bringing nuance to the potential benefits for open data sharing against associated challenges. We combine current understandings with forward-looking solutions to key issues. We conclude by underscoring the need for new policy tools to enhance the potential for responsible open data.

According to the latest Braak staging of Alzheimer's disease (AD), tau pathology occurs earliest in the brain in the locus coeruleus (LC) of the brainstem, then propagates to the transentorhinal cortex (TEC), and later to other neocortical regions. Recent animal and in vivo human brain imaging research also support the trans-axonal propagation of tau pathology. In addition, neurochemical studies link norepinephrine to behavioral symptoms in AD. It is thus critical to examine the integrity of the LC-TEC pathway in studying the early development of the disease, but there has been limited work in this direction. By leveraging the high-resolution and multi-shell diffusion MRI data from the Human Connectome Project (HCP), in this work we develop a novel method for the reconstruction of the LC-TEC pathway in a cohort of 40 HCP subjects carefully selected based on rigorous quality control of the residual distortion artifacts in the brainstem. A probabilistic atlas of the LC-TEC pathway of both hemispheres is then developed in the MNI152 space and distributed publicly on the NITRC website. To apply our atlas on clinical imaging data, we develop an automated approach to calculate the medial core of the LC-TEC pathway for localized analysis of connectivity changes. In a cohort of 138 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we demonstrate the detection of the decreased fiber integrity in the LC-TEC pathways with increasing disease severity.

The first phase of the Human Connectome Project pioneered advances in MRI technology for mapping the macroscopic structural connections of the living human brain through the engineering of a whole-body human MRI scanner equipped with maximum gradient strength of 300 mT/m, the highest ever achieved for human imaging. While this instrument has made important contributions to the understanding of macroscale connectional topology, it has also demonstrated the potential of dedicated high-gradient performance scanners to provide unparalleled in vivo assessment of neural tissue microstructure. Building on the initial groundwork laid by the original Connectome scanner, we have now embarked on an international, multi-site effort to build the next-generation human 3T Connectome scanner (Connectome 2.0) optimized for the study of neural tissue microstructure and connectional anatomy across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current maximum gradient strength from 300 mT/m to 500 mT/m and tripling the maximum slew rate from 200 T/m/s to 600 T/m/s through the design of a one-of-a-kind head gradient coil optimized to minimize peripheral nerve stimulation; (2) developing high-sensitivity multi-channel radiofrequency receive coils for in vivo and ex vivo human brain imaging; (3) incorporating dynamic field monitoring to minimize image distortions and artifacts; (4) developing new pulse sequences to integrate the strongest diffusion encoding and highest spatial resolution ever achieved in the living human brain; and (5) calibrating the measurements obtained from this next-generation instrument through systematic validation of diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales with accompanying diffusion simulations in histology-based micro-geometries. We envision creating the ultimate diffusion MRI instrument capable of capturing the complex multi-scale organization of the living human brain – from the microscopic scale needed to probe cellular geometry, heterogeneity and plasticity, to the mesoscopic scale for quantifying the distinctions in cortical structure and connectivity that define cyto- and myeloarchitectonic boundaries, to improvements in estimates of macroscopic connectivity.

Gradient recalled echo magnetic resonance imaging (GRE-MRI) at ultra-high field holds great promise for new contrast mechanisms and delineation of putative tissue compartments that contribute to the multi-echo GRE-MRI signal may aid structural characterization. Several studies have adopted the three water-pool compartment model to study white matter brain regions, associating individual compartments with myelin, axonal and extracellular water. However, the number and identifiability of GRE-MRI signal compartments has not been fully explored. We undertook this task for human brain imaging data. Multiple echo time GRE-MRI data were acquired in five healthy participants, specific anatomical structures were segmented in each dataset (substantia nigra, caudate, insula, putamen, thalamus, fornix, internal capsule, corpus callosum and cerebrospinal fluid), and the signal fitted with models comprising one to six signal compartments using a complex-valued plane wave formulation. Information criteria and cluster analysis methods were used to ascertain the number of distinct compartments within the signal from each structure and to determine their respective frequency shifts. We identified five principal signal compartments with different relative contributions to each structure's signal. Voxel-based maps of the volume fraction of each of these compartments were generated and demonstrated spatial correlation with brain anatomy.

Diffusion tensor imaging is used to measure the diffusion of water in tissue. The diffusion properties carry information about the relative organization and structure of the underlying tissue. In the case of a single voxel containing both tissue and a fast diffusing component such as free water, a single diffusion tensor is no longer appropriate. A two-tensor free water elimination model has previously been proposed to correct for the case of volume mixing. Here, this model was implemented in a straightforward but novel manner without the use of spatial constraints. The optimal acquisition parameters were investigated through Monte Carlo simulations and human brain imaging studies. At a signal-to-noise ratio of 40 with 64 diffusion-weighted encoding images, the most accurate estimates of fast diffusion signal were obtained with two diffusion-weighted shells (b-value in s/mm^2 x number of directions) of 500×32 and 1500×32. The potential bias in fractional anisotropy induced by this two-compartment model was more than an order of magnitude less than the error of using the single diffusion tensor model in the presence of partial volume effects with free water. This strategy may be useful for characterizing the diffusion of tissues adjacent to cerebral spinal fluid (CSF), tissues affected by edema, and removing artifacts from blurring and ghosting of the CSF signal.

An emerging field of human brain imaging deals with the characterization of the connectome, a comprehensive global description of structural and functional connectivity within the human brain. However, the question of how functional and structural connectivity are related has not been fully answered yet. Here, we used different methods to estimate the connectivity between each voxel of the cerebral cortex based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data in order to obtain observer-independent functional–structural connectomes of the human brain. Probabilistic fiber-tracking and a novel global fiber-tracking technique were used to measure structural connectivity whereas for functional connectivity, full and partial correlations between each voxel pair's fMRI-timecourses were calculated. For every voxel, two vectors consisting of functional and structural connectivity estimates to all other voxels in the cortex were correlated with each other. In this way, voxels structurally and functionally connected to similar regions within the rest of the brain could be identified. Areas forming parts of the ‘default mode network’ (DMN) showed the highest agreement of structure–function connectivity. Bilateral precuneal and inferior parietal regions were found using all applied techniques, whereas the global tracking algorithm additionally revealed bilateral medial prefrontal cortices and early visual areas. There were no significant differences between the results obtained from full and partial correlations. Our data suggests that the DMN is the functional brain network, which uses the most direct structural connections. Thus, the anatomical profile of the brain seems to shape its functional repertoire and the computation of the whole-brain functional–structural connectome appears to be a valuable method to characterize global brain connectivity within and between populations.

Progressing from 3T to 7 T functional MRI enables marked improvements of human brain imaging in vivo. Although direct comparisons demonstrated advantages concerning blood oxygen level dependent (BOLD) signal response and spatial specificity, these mostly focused on single brain regions with rather simple tasks. Considering that physiological noise also increases with higher field strength, it is not entirely clear whether the advantages of 7T translate equally to the entire brain during tasks which elicit more complex neuronal processing. Therefore, we investigated the difference between 3T and 7 T in response to transcutaneous electrical painful and non-painful stimulation in 22 healthy subjects. For painful stimuli vs. baseline, stronger activations were observed at 7 T in several brain regions including the insula and supplementary motor area, but not the secondary somatosensory cortex (p

The reduction of molecular oxygen to water is the final step of oxidative phosphorylation that couples adenosine triphosphate production to the reoxidation of reducing equivalents formed during the oxidation of glucose to carbon dioxide. This coupling makes the cerebral metabolic rate of oxygen consumption (CMRO(2)) an excellent reflection of the metabolic health of the brain. A multi-nuclear magnetic resonance (MR) imaging based method for CMRO(2) mapping is proposed. Oxygen consumption is determined by applying a new three-phase metabolic model for water generation and clearance to the changing 17-oxygen ((17)O) labeled water MR signal measured using quantitative (17)O MR imaging during inhalation of (17)O-enriched oxygen gas. These CMRO(2) data are corrected for the regional brain tissue mass computed from quantitative 23-sodium MR imaging of endogenous tissue sodium ions to derive quantitative results of oxygen consumption in micromoles O(2)/g tissue/minute that agree with literature results reported from positron emission tomography. The proposed technique is demonstrated in the human brain using a 9.4 T MR scanner optimized for human brain imaging.

The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.

Dynamic causal modelling (DCM) is a method for estimating and making inferences about the coupling among small numbers of brain areas, and the influence of experimental manipulations on that coupling [Friston, K.J., Harrison, L., Penny, W., 2003 Dynamic causal modelling. Neuroimage 19, 1273–1302]. Large-scale neural modelling aims to construct neurobiologically grounded computational models with emergent behaviours that inform our understanding of neuronal systems. One such model has been used to simulate region-specific BOLD time-series [Horwitz, B., Friston, K.J., Taylor, J.G., 2000. Neural modeling and functional brain imaging: an overview. Neural Netw. 13, 829–846]. DCM was used to make inferences about effective connectivity using data generated by a model implementing a visual delayed match-to-sample task [Tagamets, M.A., Horwitz, B., 1998. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cereb. Cortex 8, 310–320]. The aim was to explore the validity of inferences made using DCM about the connectivity structure and task-dependent modulatory effects, in a system with a known connectivity structure. We also examined the effects of misspecifying regions of interest. Models with hierarchical connectivity and reciprocal connections were examined using DCM and Bayesian Model Comparison [Penny, W.D., Stephan, K.E., Mechelli, A., Friston, K.J., 2004. Comparing dynamic causal models. Neuroimage 22, 1157–1172]. This approach revealed strong evidence for those models with correctly specified anatomical connectivity. Furthermore, Bayesian model comparison favoured those models when bilinear effects corresponded to their implementation in the neural model. These findings generalised to an extended model with two additional areas and reentrant circuits. The conditional uncertainty of coupling parameter estimates increased in proportion to the number of incorrectly specified regions. These results highlight the role of neural models in establishing the validity of estimation and inference schemes. Specifically, Bayesian model comparison confirms the validity of DCM in relation to a well-characterised and comprehensive neuronal model.

Both electrophysiological research in animals and human brain imaging studies have suggested that, similar to the visual system, separate cortical ventral “what” and dorsal “where” processing streams may also exist in the auditory domain. Recently we have shown enhanced γ-band activity (GBA) over posterior parietal cortex belonging to the putative auditory dorsal pathway during a sound location working memory task. Using a similar methodological approach, the present study assessed whether GBA would be increased over auditory ventral stream areas during an auditory pattern memory task. Whole-head magnetoencephalogram was recorded from N = 12 subjects while they performed a working memory task requiring same–different judgments about pairs of syllables S1 and S2 presented with 0.8-s delays. S1 and S2 could differ either in voice onset time or in formant structure. This was compared with a control task involving the detection of possible spatial displacements in the background sound presented instead of S2. Under the memory condition, induced GBA was enhanced over left inferior frontal/anterior temporal regions during the delay phase and in response to S2 and over prefrontal cortex at the end of the delay period. γ-Band coherence between left frontotemporal and prefrontal sensors was increased throughout the delay period of the memory task. In summary, the memorization of syllables was associated with synchronously oscillating networks both in frontotemporal cortex, supporting a role of these areas as parts of the putative auditory ventral stream, and in prefrontal, possible executive regions. Moreover, corticocortical connectivity was increased between these structures.

Human brain imaging studies have suggested that posterior temporo-parietal regions are involved in auditory spatial processing. We used electroencephalography to investigate the dynamics of temporo-parietal networks during working memory for moving sounds. A delayed matching-to-sample task required a decision on the identity of positions and trajectories of two moving sounds S1 and S2 presented with delays of 927 or 1427 ms. Moving sounds consisted of noise bursts positioned at successive angles to create the impression of one of six possible trajectories at variable spatial positions. Stimuli in the equally difficult control condition were identical to the memory task up to S2, which was replaced by a spatial displacement in the otherwise stationary background sound whose direction had to be detected. Event-related potentials were recorded from 31 scalp electrodes in 15 subjects. Scalp current density estimates allowed to identify the following components. The fronto-central negative variation preceding S2 did not differ between tasks. In contrast, the sustained negative current during the presentation of S1 originating from superior temporal cortex was more pronounced for the memory task, probably reflecting enhanced attention allocation and foreground-background discrimination. Most importantly, the memory task activated current sources over bilateral posterior parietal regions between the middle of S1 and the end of the delay phase. This component was completely absent in the control condition. In summary, the present study disclosed varying degrees of memorization-related, top-down driven influences on the processing of moving sounds at different stages of an auditory network involving temporal and parietal regions.

Human brain imaging studies have found that increases in functional activation in left-frontal cortex during cognitive tasks are often accompanied by similar increases in right-cerebellar regions. The present study used functional magnetic resonance imaging (fMRI) to investigate the distinctive contributions of these regions using a word stem completion task. Stems with many possible completions (MANY condition) were alternately presented with stems that had few possible completions (FEW condition), and subjects were asked to covertly complete each stem with a word and press a response switch for each successful completion. Prominent increases in activation in the MANY, relative to the FEW, condition were observed in the left middle frontal gyrus (Brodmann areas 9/10) and left caudate nucleus. In contrast, portions of the right-cerebellar hemisphere (posterior quadrangular lobule and superior semilunar lobule) and cerebellar vermis exhibited increases in the FEW, relative to the MANY, condition. This double dissociation suggests that the frontal and cerebellar regions make distinctive contributions to cognitive performance, with left-frontal (and striatal) activations reflecting response selection, which increases in difficulty when there are many appropriate responses, and right-cerebellar activation reflecting the search for responses, which increases in difficulty when even a single appropriate response is hard to retrieve.

Brain diffusion templates contain rich information about the microstructure of the brain, and are used as references in spatial normalization or in the development of brain atlases. The accuracy of diffusion templates constructed based on the diffusion tensor (DT) model is limited in regions with complex neuronal micro-architecture. High angular resolution diffusion imaging (HARDI) overcomes limitations of the DT model and is capable of resolving intravoxel heterogeneity. However, when HARDI is combined with multiple-shot sequences to minimize image artifacts, the scan time becomes inappropriate for human brain imaging. In this work, an artifact-free HARDI template of the human brain was developed from low angular resolution multiple-shot diffusion data. The resulting HARDI template was produced in ICBM-152 space based on Turboprop diffusion data, was shown to resolve complex neuronal micro-architecture in regions with intravoxel heterogeneity, and contained fiber orientation information consistent with known human brain anatomy.

Facial identity recognition has been studied mainly with explicit discrimination requirement and faces of social figures in previous human brain imaging studies. We performed a PET activation study with normal volunteers in facial identity recognition tasks using the subject's own face as visual stimulus. Three tasks were designed so that the activation of the visual representation of the face and the effect of sustained attention to the representation could be separately examined: a control-face recognition task (C), a passive own-face recognition task (no explicit discrimination was required) (P), and an active own-face recognition task (explicit discrimination was required) (A). Increased skin conductance responses during recognition of own face were seen in both task P and task A, suggesting the occurrence of psychophysiological changes during recognition of one's own face. The left fusiform gyrus, the right supramarginal gyrus, the left putamen, and the right hypothalamus were activated in tasks P and A compared with task C. The left fusiform gyrus and the right supramarginal gyrus are considered to be involved in the representation of one's own face. The activation in the right supramarginal gyrus may be associated with the representation of one's own face as a part of one's own body. The prefrontal cortices, the right anterior cingulate, the right presupplementary motor area, and the left insula were specifically activated during task A compared with tasks C and P, indicating that these regions may be involved in the sustained attention to the representation of one's own face.

The basic tenet developed by Devlin and Poldrack is to thepoint: by definition functional neuroimaging is an attempt to map‘function’ onto ‘anatomy’. Hence it is critical to cast the functionalresults, albeit indirectly measured, in precise anatomical terms.Furthermore, the authors correctly point out that in many instancesinsufficient care is devoted to this crucial ‘matching’ step betweenfunction and anatomy. The authors make an analysis of thedifferent types of anatomical descriptions presently used for theanalysis of human functional imaging data but the conclusionsreached in the paper are inherently limited by the choice of‘reference frame’ or ‘template’ brain upon which functional dataare projected. Clearly, the authors reject the use of a brain from asingle subject as the ‘template’ (e.g., the Tournoux and Talairachatlas). Moreover, the authors acknowledge that ‘Brodmann areas’are only one of the possible parcellations of human cortex, just asthe Tournoux and Talairach atlas is only a particular referencebrain. Indeed, multiple parcellation schemes are possible based oncytoarchitectonics and myeloarchitectonics.Importantly, however, this micro-architecture is in fact only oneof four criteria for identifying cortical areas. Monkey studies haveclearly shown the limitation of the Brodmann areas and theimportance of defining cortical areas by multiple criteria. Forexample area 18 in the monkey contains several visual retinotopicrepresentations, and these have been definitively recognized asseparate cortical areas (V2, V3). It is our position that, just as in themonkey, it is essential in humans to evolve from Brodmann areasto cortical areas defined by multiple criteria: cyto- and myeloarch-itecture, anatomical connections, the topographic organization(where possible), and functional properties. It may take some time,however, before we have the tools to apply all four criteria to thehuman brain.In the interim, we agree with the authors and the ICBM that theMNI atlas should be considered as the standard anatomical referenceframe, which can be complemented by population-based probabil-istic surface maps such as the PALS atlas (Caret software) andautomatically labeled T1-weighted images.Inaddition,wewishtopointout,however,thatBrodmannareasdonot allow one to draw any conclusions about the homology betweenthehumanandmonkeybrains.Weareconvincedthattheonlywaytoaddress the question of homologies is by introducing fMRI in theawake monkey (Vanduffel et al., 2001, 2002 ) as this technique linkshumanfMRIwithsinglecellstudiesandinvasivestudiesperformedinthe monkey (Orban et al., 2004). Functional imaging in the monkeyhas the additional benefit that regions of interest can be definedanatomically. For example, using monkey fMRI, Nelissen et al.(2005)we were able to show that two cytoarchitectonically definedventral premotor areas (F5c and F5a) differ in functional properties.More recently it has been shown that the latter two regions alsodiffer in anatomical connections (Luppino et al., 2006), whichstrongly suggests that they are indeed different cortical areas. Ofcourse much less is known about the architectonics of the humanbrain, but as more atlases become available (Germann et al., 2005;Eickhoff et al., 2006) they can be used to define anatomical ROIs.Connections with other brain regions is another anatomicalcriterion with which to define cortical areas. It is tempting to thinkthat probabilistic fiber tracking, as mentioned by the authors, mightsupply such an anatomical criterion in humans. Yet currentlyavailable DTI-based tractography methods are beset with severalproblems: (1) none of the current DTI/HARDI methods can easilydistinguish between detailed intra-voxels fiber crossings, and (2)all of the DTI-based tractography algorithms are model-driven andhence not free of assumptions. Thus all these tools still need to bethoroughly validated by monkey studies. The lack of appropriatevalidation in monkeys can lead to unsound conclusions (Sawamuraet al., 2006), underscoring the need to validate the manyassumptions made in human brain imaging by studies in nonhumanprimates, especially before using them in clinical setting.References

The Brain Images of Normal Subjects (BRAINS) Imagebank (http://www.brainsimagebank.ac.uk) is an integrated repository project hosted by the University of Edinburgh and sponsored by the Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) collaborators. BRAINS provide sharing and archiving of detailed normal human brain imaging and relevant phenotypic data already collected in studies of healthy volunteers across the life-course. It particularly focusses on the extremes of age (currently older age, and in future perinatal) where variability is largest, and which are under-represented in existing databanks.BRAINS is a living imagebank where new data will be added when available. Currently BRAINS contains data from 808 healthy volunteers, from 15 to 81 years of age, from 7 projects in 3 centres. Additional completed and ongoing studies of normal individuals from 1st to 10th decades are in preparation and will be included as they become available.BRAINS holds several MRI structural sequences, including T1, T2, T2* and fluid attenuated inversion recovery (FLAIR), available in DICOM (http://dicom.nema.org/); in future Diffusion Tensor Imaging (DTI) will be added where available. Images are linked to a wide range of ‘textual data’, such as age, medical history, physiological measures (e.g. blood pressure), medication use, cognitive ability, and perinatal information for pre/post-natal subjects. The imagebank can be searched to include or exclude ranges of these variables to create better estimates of ‘what is normal’ at different ages.