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Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even "at rest," the brain's different functional networks spontaneously fluctuate in their activity level; each network's spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks; one ideally wants a network model that explicitly allows overlap, for example, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging sampling rate. We identify multiple "temporal functional modes," including several that subdivide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.

Several studies have demonstrated age-related regional differences in the magnitude of the BOLD signal using task-based fMRI. It has been suggested that functional changes reflect either compensatory or de-differentiation mechanisms, both of which assume response to a specific stimulus. Here, we have tested whether ageing affects both task-based and resting brain function, and the extent to which functional changes are mediated by reductions in grey matter (GM) volume. Two groups, of 22 healthy younger and 22 older volunteers, underwent an imaging protocol involving structural and functional MRI, both during a memory task and at rest. The two groups had similar socio-demographical characteristics and cognitive performance. Image analysis revealed both structural and functional differences. Increased BOLD signal in older relative to younger volunteers was mainly observed in the frontal lobes, both during the task and at rest. Functional changes in the frontal lobes were largely located in brain regions spared from GM loss, and adding GM covariates to the fMRI analysis did not significantly alter the group differences. Our results are consistent with the suggestion that, during normal ageing, the brain responds to neuronal loss by fine-tuning connections between spared neurons. Longitudinal studies will be necessary to fully test this hypothesis., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Localising activity in the human midbrain with conventional functional MRI (fMRI) is challenging because the midbrain nuclei are small and located in an area that is prone to physiological artefacts. Here we present a replicable and automated method to improve the detection and localisation of midbrain fMRI signals. We designed a visual fMRI task that was predicted would activate the superior colliculi (SC) bilaterally. A limited number of coronal slices were scanned, orientated along the long axis of the brainstem, whilst simultaneously recording cardiac and respiratory traces. A novel anatomical registration pathway was used to optimise the localisation of the small midbrain nuclei in stereotactic space. Two additional structural scans were used to improve registration between functional and structural T1-weighted images: an echo-planar image (EPI) that matched the functional data but had whole-brain coverage, and a whole-brain T2-weighted image. This pathway was compared to conventional registration pathways, and was shown to significantly improve midbrain registration. To reduce the physiological artefacts in the functional data, we estimated and removed structured noise using a modified version of a previously described physiological noise model (PNM). Whereas a conventional analysis revealed only unilateral SC activity, the PNM analysis revealed the predicted bilateral activity. We demonstrate that these methods improve the measurement of a biologically plausible fMRI signal. Moreover they could be used to investigate the function of other midbrain nuclei., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Age is one of the most salient aspects in faces and of fundamental cognitive and social relevance. Although face processing has been studied extensively, brain regions responsive to age have yet to be localized. Using evocative face morphs and fMRI, we segregate two areas extending beyond the previously established face-sensitive core network, centered on the inferior temporal sulci and angular gyri bilaterally, both of which process changes of facial age. By means of probabilistic tractography, we compare their patterns of functional activation and structural connectivity. The ventral portion of Wernicke's understudied perpendicular association fasciculus is shown to interconnect the two areas, and activation within these clusters is related to the probability of fiber connectivity between them. In addition, post-hoc age-rating competence is found to be associated with high response magnitudes in the left angular gyrus. Our results provide the first evidence that facial age has a distinct representation pattern in the posterior human brain. We propose that particular face-sensitive nodes interact with additional object-unselective quantification modules to obtain individual estimates of facial age. This brain network processing the age of faces differs from the cortical areas that have previously been linked to less developmental but instantly changeable face aspects. Our probabilistic method of associating activations with connectivity patterns reveals an exemplary link that can be used to further study, assess and quantify structure-function relationships.

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

Traumatic brain injury (TBI) frequently produces impairments of attention in humans. These can result in a failure to maintain consistent goal-directed behavior. A predominantly right-lateralized frontoparietal network is often engaged during attentionally demanding tasks. However, lapses of attention have also been associated with increases in activation within the default mode network (DMN). Here, we study TBI patients with sustained attention impairment, defined on the basis of the consistency of their behavioral performance over time. We show that sustained attention impairments in patients are associated with an increase in DMN activation, particularly within the precuneus and posterior cingulate cortex. Furthermore, the interaction of the precuneus with the rest of the DMN at the start of the task, i.e., its functional connectivity, predicts which patients go on to show impairments of attention. Importantly, this predictive information is present before any behavioral evidence of sustained attention impairment, and the relationship is also found in a subgroup of patients without focal brain damage. TBI often results in diffuse axonal injury, which produces cognitive impairment by disconnecting nodes in distributed brain networks. Using diffusion tensor imaging, we demonstrate that structural disconnection within the DMN also correlates with the level of sustained attention. These results show that abnormalities in DMN function are a sensitive marker of impairments of attention and suggest that changes in connectivity within the DMN are central to the development of attentional impairment after TBI.

Traumatic brain injury often results in cognitive impairments that limit recovery. The underlying pathophysiology of these impairments is uncertain, which restricts clinical assessment and management. Here, we use magnetic resonance imaging to test the hypotheses that: (i) traumatic brain injury results in abnormalities of functional connectivity within key cognitive networks; (ii) these changes are correlated with cognitive performance; and (iii) functional connectivity within these networks is influenced by underlying changes in structural connectivity produced by diffuse axonal injury. We studied 20 patients in the chronic phase after traumatic brain injury compared with age-matched controls. Network function was investigated in detail using functional magnetic resonance imaging to analyse both regional brain activation, and the interaction of brain regions within a network (functional connectivity). We studied patients during performance of a simple choice-reaction task and at 'rest'. Since functional connectivity reflects underlying structural connectivity, diffusion tensor imaging was used to quantify axonal injury, and test whether structural damage correlated with functional change. The patient group showed typical impairments in information processing and attention, when compared with age-matched controls. Patients were able to perform the task accurately, but showed slow and variable responses. Brain regions activated by the task were similar between the groups, but patients showed greater deactivation within the default mode network, in keeping with an increased cognitive load. A multivariate analysis of 'resting' state functional magnetic resonance imaging was then used to investigate whether changes in network function were present in the absence of explicit task performance. Overall, default mode network functional connectivity was increased in the patient group. Patients with the highest functional connectivity had the least cognitive impairment. In addition, functional connectivity at rest also predicted patterns of brain activation during later performance of the task. As expected, patients showed widespread white matter damage compared with controls. Lower default mode network functional connectivity was seen in those patients with more evidence of diffuse axonal injury within the adjacent corpus callosum. Taken together, our results demonstrate altered patterns of functional connectivity in cognitive networks following injury. The results support a direct relationship between white matter organization within the brain's structural core, functional connectivity within the default mode network and cognitive function following brain injury. They can be explained by two related changes: a compensatory increase in functional connectivity within the default mode network; and a variable degree of structural disconnection that modulates this change in network function.

Spatial independent components analysis (sICA) has become a widely applied data-driven method for fMRI data, especially for resting-state studies. These sICA approaches are often based on iterative estimation algorithms and there are concerns about accuracy due to noise. Repeatability measures such as ICASSO, RAICAR and ARABICA have been introduced as remedies but information on their effects on estimates is limited. The contribution of this study was to provide more of such information and test if the repeatability analyses are necessary. We compared FastICA-based ordinary and repeatability approaches concerning mixing vector estimates. Comparisons included original FastICA, FSL4 Melodic FastICA and original and modified ICASSO. The effects of bootstrapping and convergence threshold were evaluated. The results show that there is only moderate improvement due to repeatability measures and only in the bootstrapping case. Bootstrapping attenuated power from time courses of resting-state network related ICs at frequencies higher than 0.1 Hz and made subsets of low frequency oscillations more emphasized IC-wise. The convergence threshold did not have a significant role concerning the accuracy of estimates. The performance results suggest that repeatability measures or strict converge criteria might not be needed in sICA analyses of fMRI data. Consequently, the results in existing sICA fMRI literature are probably valid in this sense. A decreased accuracy of original bootstrapping ICASSO was observed and corrected by using centrotype mixing estimates but the results warrant for thorough evaluations of data-driven methods in general. Also, given the fMRI-specific considerations, further development of sICA methods is strongly encouraged., (Copyright © 2010 Elsevier Inc. All rights reserved.)

The posterior cingulate cortex (PCC) is a central part of the default mode network (DMN) and part of the structural core of the brain. Although the PCC often shows consistent deactivation when attention is focused on external events, anatomical studies show that the region is not homogeneous, and electrophysiological recordings in nonhuman primates suggest that it is directly involved in some forms of attention. We report a functional magnetic resonance imaging study of an attentionally demanding task (either a zero- or two-back working memory task). Standard subtraction analysis within the PCC shows a relative deactivation as task difficulty increases. In contrast, a dual-regression functional connectivity analysis reveals a clear dissociation between ventral and dorsal parts of the PCC. As task difficulty increases, the ventral PCC shows reduced integration within the DMN and less anticorrelation with the cognitive control network (CCN) activated by the task. The dorsal PCC shows an opposite pattern, with increased DMN integration and more anticorrelation. At rest, the dorsal PCC also shows functional connectivity with both the DMN and attentional networks. As expected, these results provide evidence that the PCC is involved in supporting internally directed thought, as the region is more highly integrated with the DMN at low task demands. In contrast, the task-dependent increases in connectivity between the dorsal PCC and the CCN are consistent with a role for this region in modulating the dynamic interaction between these two networks controlling the efficient allocation of attention.

In recent years, neuroimaging studies have increasingly been acquiring multiple modalities of data and searching for task- or disease-related changes in each modality separately. A major challenge in analysis is to find systematic approaches for fusing these differing data types together to automatically find patterns of related changes across multiple modalities, when they exist. Independent Component Analysis (ICA) is a popular unsupervised learning method that can be used to find the modes of variation in neuroimaging data across a group of subjects. When multimodal data is acquired for the subjects, ICA is typically performed separately on each modality, leading to incompatible decompositions across modalities. Using a modular Bayesian framework, we develop a novel "Linked ICA" model for simultaneously modelling and discovering common features across multiple modalities, which can potentially have completely different units, signal- and contrast-to-noise ratios, voxel counts, spatial smoothnesses and intensity distributions. Furthermore, this general model can be configured to allow tensor ICA or spatially-concatenated ICA decompositions, or a combination of both at the same time. Linked ICA automatically determines the optimal weighting of each modality, and also can detect single-modality structured components when present. This is a fully probabilistic approach, implemented using Variational Bayes. We evaluate the method on simulated multimodal data sets, as well as on a real data set of Alzheimer's patients and age-matched controls that combines two very different types of structural MRI data: morphological data (grey matter density) and diffusion data (fractional anisotropy, mean diffusivity, and tensor mode)., (Copyright © 2010 Elsevier Inc. All rights reserved.)

There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Increasing age and carrying an APOE ε4 allele are well established risk factors for Alzheimer's disease (AD). The earlier age of onset of AD observed in ε4-carriers may reflect an accelerated aging process. We recently reported that APOE genotype modulates brain function decades before the appearance of any cognitive or clinical symptoms. Here we test the hypothesis that APOE influences brain aging by comparing healthy ε4-carriers and non-carriers, using the same imaging protocol in distinct groups of younger and older healthy volunteers. A cross-sectional factorial design was used to examine the effects of age and APOE genotype, and their interaction, on fMRI activation during an encoding memory task. The younger (N=36; age range 20-35; 18 ε4-carriers) and older (35 middle-age/elderly; age range 50-78 years; 15 ε4-carriers) healthy volunteers taking part in the study were cognitively normal. We found a significant interaction between age and ε4-status in the hippocampi, frontal pole, subcortical nuclei, middle temporal gyri and cerebellum, such that aging was associated with decreased activity in e4-carriers and increased activity in non-carriers. Reduced cerebral blood flow was found in the older ε4-carriers relative to older non-carriers despite preserved grey matter volume. Overactivity of brain function in young ε4-carriers is disproportionately reduced with advancing age even before the onset of measurable memory impairment. The APOE genotype determines age-related changes in brain function that may reflect the increased vulnerability of ε4-carriers to late-life pathology or cognitive decline., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Resting state networks (RSNs), as imaged by functional MRI, are distributed maps of areas believed to be involved in the function of the "resting" brain, which appear in both resting and task data. The current dominant view is that such networks are associated with slow (∼0.015Hz), spontaneous fluctuations in the BOLD signal. To date, limited work has investigated the frequency characteristics of RSNs; here we investigate a range of issues relating to their spectral and phase characteristics. Our results indicate that RSNs, although dominated by low frequencies in the raw BOLD signal, are in fact broadband processes that show temporal coherences across a wide frequency spectrum. In addition, we show that RSNs exhibit different levels of phase synchrony at different frequencies. These findings challenge the notion that FMRI resting signals are simple "low frequency" spontaneous signal fluctuations., (Copyright © 2011 Elsevier B.V. All rights reserved.)

The functions of the resting state networks (RSNs) revealed by functional MRI remain unclear, but it has seemed possible that networks emerge in parallel with the development of related cognitive functions. We tested the alternative hypothesis: that the full repertoire of resting state dynamics emerges during the period of rapid neural growth before the normal time of birth at term (around 40 wk of gestation). We used a series of independent analytical techniques to map in detail the development of different networks in 70 infants born between 29 and 43 wk of postmenstrual age (PMA). We characterized and charted the development of RSNs from recognizable but often fragmentary elements at 30 wk of PMA to full facsimiles of adult patterns at term. Visual, auditory, somatosensory, motor, default mode, frontoparietal, and executive control networks developed at different rates; however, by term, complete networks were present, several of which were integrated with thalamic activity. These results place the emergence of RSNs largely during the period of rapid neural growth in the third trimester of gestation, suggesting that they are formed before the acquisition of cognitive competencies in later childhood.

Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: (1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, (2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and (3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or gray matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder.

Symptoms of cognitive impairment during smoking withdrawal can be ameliorated by nicotine replacement. To define brain mechanisms contributing to this therapeutic effect, we conducted a functional connectivity analysis of resting-state fMRI in 17 abstinent smokers following nicotine replacement in a double-blind, placebo-controlled, crossover design. We found that individual differences in cognitive withdrawal symptom improvements after nicotine replacement were associated with increased inverse coupling between executive control and default mode brain networks. Furthermore, improvements in withdrawal symptoms were negatively correlated with altered functional connectivity within the default mode network, and with connectivity between the executive control network and regions implicated in reward processing. These findings demonstrate that nicotine administration in abstinent smokers modulates dynamic interactions between large-scale cognitive brain networks in the resting state. We specifically highlight the role of midline and prefrontal network regions in the neurocognitive response to nicotine pharmacotherapy and suggest that altered functional connectivity patterns of these networks reflect their engagement in reward and salience processing during smoking withdrawal. Individual differences in resting brain functional connectivity may predict therapeutic outcomes in nicotine addiction and other conditions associated with cognitive impairments., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data 'explosion'. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.

The cerebellum processes information from functionally diverse regions of the cerebral cortex. Cerebellar input and output nuclei have connections with prefrontal, parietal, and sensory cortex as well as motor and premotor cortex. However, the topography of the connections between the cerebellar and cerebral cortices remains largely unmapped, as it is relatively unamenable to anatomical methods. We used resting-state functional magnetic resonance imaging to define subregions within the cerebellar cortex based on their functional connectivity with the cerebral cortex. We mapped resting-state functional connectivity voxel-wise across the cerebellar cortex, for cerebral-cortical masks covering prefrontal, motor, somatosensory, posterior parietal, visual, and auditory cortices. We found that the cerebellum can be divided into at least 2 zones: 1) a primary sensorimotor zone (Lobules V, VI, and VIII), which contains overlapping functional connectivity maps for domain-specific motor, somatosensory, visual, and auditory cortices; and 2) a supramodal zone (Lobules VIIa, Crus I, and II), which contains overlapping functional connectivity maps for prefrontal and posterior-parietal cortex. The cortical connectivity of the supramodal zone was driven by regions of frontal and parietal cortex which are not directly involved in sensory or motor processing, including dorsolateral prefrontal cortex and the frontal pole, and the inferior parietal lobule.

Stopping an action in response to an unexpected event requires both that the event is attended to, and that the action is inhibited. Previous neuroimaging investigations of stopping have failed to adequately separate these cognitive elements. Here we used a version of the widely used Stop Signal Task that controls for the attentional capture of stop signals. This allowed us to fractionate the contributions of frontal regions, including the right inferior frontal gyrus and medial frontal cortex, to attentional capture, response inhibition, and error processing. A ventral attentional system, including the right inferior frontal gyrus, has been shown to respond to unexpected stimuli. In line with this evidence, we reasoned that lateral frontal regions support attentional capture, whereas medial frontal regions, including the presupplementary motor area (pre-SMA), actually inhibit the ongoing action. We tested this hypothesis by contrasting the brain networks associated with the presentation of unexpected stimuli against those associated with outright stopping. Functional MRI images were obtained in 26 healthy volunteers. Successful stopping was associated with activation of the right inferior frontal gyrus, as well as the pre-SMA. However, only activation of the pre-SMA differentiated stopping from a high-level baseline that controlled for attentional capture. As expected, unsuccessful attempts at stopping activated the anterior cingulate cortex. In keeping with work in nonhuman primates these findings demonstrate that successful motor inhibition is specifically associated with pre-SMA activation.

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon&#95;1000/.

Functional MRI (fMRI) has not previously been used systematically to investigate brain function in preterm infants. We here describe statistically robust and reproducible fMRI results in this challenging subject group using a programmable somatosensory stimulus synchronized with MR image acquisition which induced well-localized positive blood oxygen level dependent (BOLD) responses contralateral to the side of the stimulation in: 11 preterm infants (median post menstrual age 33 weeks and 4 days, range 29+1 to 35+3); 6 control infants born at term gestational age; and 18 infants born preterm (median gestational age at birth 30 weeks and 5 days, range 25+4 to 36+0) but studied at term corrected gestational age. Bilateral signals were identified in 8 of the ex-preterm infants at term age. Anatomical confirmation of appropriate activations was provided with diffusion tensor imaging (DTI) based tractography which identified connecting pathways from the regions of activation through the ipsilateral corticospinal tracts and posterior limb of the internal capsule. These results demonstrate that it is possible to reliably identify positive BOLD signals in the infant brain and that fMRI techniques can also be applied in the study of preterm infants., (Crown Copyright (c) 2009. Published by Elsevier Inc. All rights reserved.)

The default-mode network, a coherent resting-state brain network, is thought to characterize basal neural activity. Aberrant default-mode connectivity has been reported in a host of neurological and psychiatric illnesses and in persons at genetic risk for such illnesses. Whereas the neurophysiologic mechanisms that regulate default-mode connectivity are unclear, there is growing evidence that genetic factors play a role. In this report, we estimate the importance of genetic effects on the default-mode network by examining covariation patterns in functional connectivity among 333 individuals from 29 randomly selected extended pedigrees. Heritability for default-mode functional connectivity was 0.424 +/- 0.17 (P = 0.0046). Although neuroanatomic variation in this network was also heritable, the genetic factors that influence default-mode functional connectivity and gray-matter density seem to be distinct, suggesting that unique genes influence the structure and function of the network. In contrast, significant genetic correlations between regions within the network provide evidence that the same genetic factors contribute to variation in functional connectivity throughout the default mode. Specifically, the left parahippocampal region was genetically correlated with all other network regions. In addition, the posterior cingulate/precuneus region, medial prefrontal cortex, and right cerebellum seem to form a subnetwork. Default-mode functional connectivity is influenced by genetic factors that cannot be attributed to anatomic variation or a single region within the network. By establishing the heritability of default-mode functional connectivity, this experiment provides the obligatory evidence required before these measures can be considered as endophenotypes for psychiatric or neurological illnesses or to identify genes influencing intrinsic brain function.

Recent research has characterized the anatomical connectivity of the cortico-cerebellar system - a large and important fibre system in the primate brain. Within this system, there are reciprocal projections between the prefrontal cortex and Crus II of the cerebellar cortex, which both play important roles in the acquisition and execution of cognitive skills. Here, we propose that this system also plays a particular role in sustaining skilled cognitive performance in patients with Relapsing-Remitting Multiple Sclerosis (RRMS), in whom advancing neuropathology causes increasingly inefficient information processing. We scanned RRMS patients and closely matched healthy subjects while they performed the Paced Auditory Serial Addition Test (PASAT), a demanding test of information processing speed, and a control task. This enabled us to localize differences between conditions that change as a function of group (group-by-condition interactions). Hemodynamic activity in some patient populations with CNS pathology are not well understood and may be atypical, so we avoided analysis strategies that rely exclusively on models of hemodynamic activity derived from the healthy brain, using instead an approach that combined a 'model-free' analysis technique (Tensor Independent Component Analysis, TICA) that was relatively free of such assumptions, with a post-hoc 'model-based' approach (General Linear Model, GLM). Our results showed group-by-condition interactions in cerebellar cortical Crus II. We suggest that this area may have in role maintaining performance in working memory tasks by compensating for inefficient data transfer associated with white matter lesions in MS.

Understanding functional plasticity in memory networks associated with temporal lobe epilepsy (TLE) is central to predicting memory decline following surgery. However, the extent of functional reorganization within memory networks remains unclear. In this preliminary study, we used novel analysis methods assessing network-level changes across the brain during memory task performance in patients with TLE to test the hypothesis that hippocampal functions may not readily shift between hemispheres, but instead may show altered intra-hemispheric organization with unilateral damage. In addition, we wished to relate functional differences to structural changes along specific fibre pathways associated with memory function. Nine pre-operative patients with intractable left TLE and 10 healthy controls underwent functional MRI during complex scene encoding. Diffusion tensor imaging was additionally performed in the same patients. In our study, we found no evidence of inter-hemispheric shifts in memory-related activity in TLE using standard general linear model analysis. However, tensor independent component analysis revealed significant reductions in functional connectivity between bilateral MTL, occipital and left orbitofrontal regions among others in left TLE. This altered orbitofrontal activity was directly related to measures of fornix tract coherence in patients (P < 0.05). Our results suggest that specific fibre pathways, potentially affected by MTL neurodegeneration, may play a central role in functional plasticity in TLE and highlight the importance of network-based analysis approaches. Relative to standard model-based methods, novel objective functional connectivity analyses may offer improved sensitivity to subtle changes in the distribution of memory functions relevant for surgical planning in TLE., (2009 Wiley-Liss, Inc.)

Background: The default-mode network (DMN) is a functional network with increasing relevance for psychiatric research, characterized by increased activation at rest and decreased activation during task performance. The degree of DMN deactivation during a cognitively demanding task depends on its difficulty. However, the relation of hemodynamic responses in the resting phase after a preceding cognitive challenge remains relatively unexplored. We test the hypothesis that the degree of activation of the DMN following cognitive challenge is influenced by the cognitive load of a preceding working-memory task., Methodology/principal Findings: Twenty-five healthy subjects were investigated with functional MRI at 3 Tesla while performing a working-memory task with embedded short resting phases. Data were decomposed into statistically independent spatio-temporal components using Tensor Independent Component Analysis (TICA). The DMN was selected using a template-matching procedure. The spatial map contained rest-related activations in the medial frontal cortex, ventral anterior and posterior cingulate cortex. The time course of the DMN revealed increased activation at rest after 1-back and 2-back blocks compared to the activation after a 0-back block., Conclusion/significance: We present evidence that a cognitively challenging working-memory task is followed by greater activation of the DMN than a simple letter-matching task. This might be interpreted as a functional correlate of self-evaluation and reflection of the preceding task or as relocation of cerebral resources representing recovery from high cognitive demands. This finding is highly relevant for neuroimaging studies which include resting phases in cognitive tasks as stable baseline conditions. Further studies investigating the DMN should take possible interactions of tasks and subsequent resting phases into account.

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

The effect of echoplanar imaging (EPI) acoustic background noise on blood oxygenation level dependent (BOLD) activations was investigated. Two EPI pulse sequences were compared: (i) conventional EPI with a pulsating sound component of typically 8-10 Hz, which is a potent physiological stimulus, and (ii) the more recently developed continuous-sound EPI, which is perceived as less distractive despite equivalent peak sound pressure levels. Sixteen healthy subjects performed an established demanding visual n-back working memory task. Using an exploratory data analysis technique (tensorial probabilistic independent component analysis; tensor-PICA), we studied the inter-session/within-subject response variability introduced by continuous-sound versus conventional EPI acoustic background noise in addition to temporal and spatial signal characteristics. The analysis revealed a task-related component associated with the established higher-level working memory and motor feedback response network, which exhibited a significant 19% increase in its average effect size for the continuous-sound as opposed to conventional EPI. Stimulus-related lower-level activations, such as primary visual areas, were not modified. EPI acoustic background noise influences much more than the auditory system per se. This analysis provides additional evidence for an enhancement of task-related, extra-auditory BOLD activations by continuous-sound EPI due to less distractive acoustic background gradient noise.

Convergent data from various scientific approaches strongly implicate cerebellar systems in nonmotor functions. The functional anatomy of these systems has been pieced together from disparate sources, such as animal studies, lesion studies in humans, and structural and functional imaging studies in humans. To better define this distinct functional anatomy, in the current study we delineate the role of the cerebellum in several nonmotor systems simultaneously and in the same subjects using resting state functional connectivity MRI. Independent component analysis was applied to resting state data from two independent datasets to identify common cerebellar contributions to several previously identified intrinsic connectivity networks (ICNs) involved in executive control, episodic memory/self-reflection, salience detection, and sensorimotor function. We found distinct cerebellar contributions to each of these ICNs. The neocerebellum participates in (1) the right and left executive control networks (especially crus I and II), (2) the salience network (lobule VI), and (3) the default-mode network (lobule IX). Little to no overlap was detected between these cerebellar regions and the sensorimotor cerebellum (lobules V-VI). Clusters were also located in pontine and dentate nuclei, prominent points of convergence for cerebellar input and output, respectively. The results suggest that the most phylogenetically recent part of the cerebellum, particularly crus I and II, make contributions to parallel cortico-cerebellar loops involved in executive control, salience detection, and episodic memory/self-reflection. The largest portions of the neocerebellum take part in the executive control network implicated in higher cognitive functions such as working memory.

Purpose: To investigate the possible effect of the APOE epsilon4 allele on age-related regional volume loss within the corpus callosum (CC) in healthy epsilon4 allele carriers compared with noncarriers., Materials and Methods: A total of 211 subjects, ages 27 to 83 years, 51 epsilon4 carriers and 160 noncarriers underwent T1-weighted MRI scan. All subjects had normal MRI scan and performed within normal range on a neuropsychological battery of tests. CC was segmented into seven functionally relevant regions using a previously published probabilistic map of the CC connectivity. We measured the volumes of the CC and its subregions. We used a regression model (with volumes as dependent and age as independent variables) and compared the slopes between carriers and noncarriers using an analysis of covariance model. We also carried out voxel-based-morphometry analysis to investigate the possible effect of the APOE epsilon4 gene on the gray matter., Results: We found that the volume of the CC and all subregions decreased with increasing age in both groups. The slope was steeper in the APOE epsilon4 carriers compared withthe noncarriers particularly in the prefrontal region (P = 0.02). No gray matter differences were observed between the two groups., Conclusion: APOE epsilon4 polymorphism is associated with accelerated age-related volume loss in the prefrontal callosal tracts without gray matter loss. This result suggests the role of APOE epsilon4 in the brain aging by primarily affecting white matter structures particularly in the frontal lobe.

The APOE epsilon4 allele is a risk factor for late-life pathological changes that is also associated with anatomical and functional brain changes in middle-aged and elderly healthy subjects. We investigated structural and functional effects of the APOE polymorphism in 18 young healthy APOE epsilon4-carriers and 18 matched noncarriers (age range: 20-35 years). Brain activity was studied both at rest and during an encoding memory paradigm using blood oxygen level-dependent fMRI. Resting fMRI revealed increased "default mode network" (involving retrosplenial, medial temporal, and medial-prefrontal cortical areas) coactivation in epsilon4-carriers relative to noncarriers. The encoding task produced greater hippocampal activation in epsilon4-carriers relative to noncarriers. Neither result could be explained by differences in memory performance, brain morphology, or resting cerebral blood flow. The APOE epsilon4 allele modulates brain function decades before any clinical or neurophysiological expression of neurodegenerative processes.

FMRI research in Alzheimer's disease (AD) and mild cognitive impairment (MCI) typically is aimed at determining regional changes in brain function, most commonly by creating a model of the expected BOLD-response and estimating its magnitude using a general linear model (GLM) analysis. This crucially depends on the suitability of the temporal assumptions of the model and on assumptions about normality of group distributions. Exploratory data analysis techniques such as independent component analysis (ICA) do not depend on these assumptions and are able to detect unknown, yet structured spatiotemporal processes in neuroimaging data. Tensorial probabilistic ICA (T-PICA) is a model free technique that can be used for analyzing multiple subjects and groups, extracting signals of interest (components) in the spatial, temporal, and also subject domain of FMRI data. We applied T-PICA and model-based GLM to study FMRI signal during face encoding in 18 AD, 28 MCI patients, and 41 healthy elderly controls. T-PICA showed activation in regions associated with motor, visual, and cognitive processing, and deactivation in the default mode network. Six networks showed a significantly decreased response in patients. For two networks the T-PICA technique was significantly more sensitive to detect group differences than the standard model-based technique. We conclude that T-PICA is a promising tool to identify and detect differences in (de)activated brain networks in elderly controls and dementia patients. The technique is more sensitive than the commonly applied model-based method. Consistent with other research, we show that networks of activation and deactivation show decreased reactivity in dementia., ((c) 2007 Wiley-Liss, Inc.)

Normal aging is associated with cognitive decline. Functions such as attention, information processing, and working memory are compromised. It has been hypothesized that not only regional changes, but also alterations in the integration of regional brain activity (functional brain connectivity) underlie the observed age-related deficits. Here, we examined the functional properties of brain networks based on spontaneous fluctuations within brain systems using functional magnetic resonance imaging. We hypothesized that functional connectivity of intrinsic brain activity in the "default-mode" network (DMN) is affected by normal aging and that this relates to cognitive function. Ten younger and 22 older subjects were scanned at "rest," that is, lying awake with eyes closed. Our results show decreased activity in older versus younger subjects in 2 resting-state networks (RSNs) resembling the previously described DMN, containing the superior and middle frontal gyrus, posterior cingulate, middle temporal gyrus, and the superior parietal region. These results remain significant after correction for RSN-specific gray matter volume. The relevance of these findings is illustrated by the correlation between reduced activity of one of these RSNs and less effective executive functioning/processing speed in the older group.

Recent anatomical and electrophysiological evidence in primates indicates the presence of direct connections between primary auditory and primary visual cortex that constitute cross-modal systems. We examined the intrinsic functional connectivity (fcMRI) of putative primary auditory cortex in 32 young adults during resting state scanning. We found that the medial Heschl's gyrus was strongly coupled, in particular, to visual cortex along the anterior banks of the calcarine fissure. This observation was confirmed using novel group-level, tensor-based independent components analysis. fcMRI analysis revealed that although overall coupling between the auditory and visual cortex was significantly reduced when subjects performed a visual perception task, coupling between the anterior calcarine cortex and auditory cortex was not disrupted. These results suggest that primary auditory cortex has a functionally distinct relationship with the anterior visual cortex, which is known to represent the peripheral visual field. Our study provides novel, fcMRI-based, support for a neural system involving low-level auditory and visual cortices., ((c) 2008 Wiley-Liss, Inc.)

Neuroimaging experiments have identified several brain regions that appear to play roles in motor learning. Here we apply a novel multivariate analytical approach to explore the dynamic interactions of brain activation regions as spatio-temporally coherent functional networks. We acquired BOLD fMRI signal during explicit motor sequence learning task to characterize the adaptive functional changes in the early phase of motor learning. Subjects practiced a 10-digit, visually cued, fixed motor sequence during 15 consecutive 30 s practice blocks interleaved with similarly cued random sequence blocks. Tensor Independent Component Analysis (TICA) decomposed the data into statistically independent spatio-temporal processes. Two components were identified that represented task-related activations. The first component showed decreasing activity of a fronto-parieto-cerebellar network during task conditions. The other exclusively related to sequence learning blocks showed activation in a network including the posterior parietal and premotor cortices. Variation in expression of this component across individual subjects correlated with differences in behavior. Relative deactivations also were found in patterns similar to those described previously as "resting state" networks. Some of these deactivation components also showed task- and time-related modulations and were related to the behavioral improvement. The spatio-temporal coherence within these networks suggests that their elements are functionally integrated. Their anatomical plausibility and correlation with behavioral measures also suggest that this approach allows characterization of the interactions of functional networks relevant to the task. Particular value for multi-variant, model-free methods such as TICA lies in the potential for generating hypotheses regarding functional anatomical networks underlying specific behaviors.

Spinal cord functional imaging allows assessment of activity in primary synaptic connections made by sensory neurons relaying information about the state of the body. However, reported human data based on gradient-echo techniques have been largely inconsistent, with no clear patterns of activation emerging. One reason for this variability is the influence of physiological noise, which is typically not corrected for. By acquiring single-slice resting data from the spinal cord with a conventional gradient-echo EPI pulse sequence at TR=200 ms (critically sampled) and TR=3 s (under-sampled), we have characterised various sources of physiological noise. In 8 healthy subjects, the presence of physiologically dependent signal was explored using probabilistic independent component analysis (PICA). Based on the insights provided by PICA, we defined a new physiological noise model (PNM) based on retrospective image correction (RETROICOR), which uses independent physiological measurements taken from the subject to model sources of noise. Statistical significance of individual components included in the PNM was assessed by F-tests, which demonstrated that the optimal PNM included cardiac, respiratory, interaction and low-frequency regressors. In a group of 10 healthy subjects, activation data were acquired from the cervical spinal region (T1 to C5) during painful thermal stimulation of the right and left hands. The improvement obtained when using a PNM in estimating spinal cord activation was reflected in a reduction of false-positive activation (active voxels in the CSF space surrounding the cord), when compared to conventional GLM modelling without a PNM.

Echo-planar imaging (EPI) generates considerable acoustic noise by rapidly oscillating gradients. In functional magnetic resonance imaging (FMRI), unshielded EPI sounds activate the auditory system inasmuch as it is responsive. Instead of attenuating EPI noise, our goal was to utilize it for auditory FMRI by omitting read-outs from the pulse sequence's gradient train. Read-out gradient pulses are the primary noise determinant of EPI introducing its peak sound level and fundamental frequency peak which inversely relates to twice the echo spacing. Using model-driven analyses, we demonstrate that withholding read-outs from EPI is suited to reliably evoke hemodynamic blood oxygenation level-dependent (BOLD) signal modulations bilaterally in the auditory cortex of normal hearing subjects (n=60). To investigate the utility of EPI read-out omissions for auditory FMRI at an individual subject's level, we compare traditional Family-Wise-Error-Rate (FWER)-corrected maximum height thresholding to spatial mixture modeling (SMM). With the latter, appropriate bilateral auditory activations were confirmed in 95% of the individuals, whereas FWER-based voxel thresholding detected such activations in up to 72%. We illustrate the applicability of this novel EPI modification for clinical diagnostic purposes and report on a patient with bilateral large vestibular aqueducts (LVAs) and severe binaural sensorineural hearing loss (SNHL). In this particular case, read-out omissions from EPI were used to assert residual audition prior to cochlear implantation (CI). Requiring no specific task compliance or sophisticated stimulation equipment other than the scanner on its own, FMRI by read-out omissions lends itself to auditory investigations and to quickly probe audition.

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing.

Functional magnetic resonance imaging (fMRI) studies of the human brain have suggested that low-frequency fluctuations in resting fMRI data collected using blood oxygen level dependent (BOLD) contrast correspond to functionally relevant resting state networks (RSNs). Whether the fluctuations of resting fMRI signal in RSNs are a direct consequence of neocortical neuronal activity or are low-frequency artifacts due to other physiological processes (e.g., autonomically driven fluctuations in cerebral blood flow) is uncertain. In order to investigate further these fluctuations, we have characterized their spatial and temporal properties using probabilistic independent component analysis (PICA), a robust approach to RSN identification. Here, we provide evidence that: i. RSNs are not caused by signal artifacts due to low sampling rate (aliasing); ii. they are localized primarily to the cerebral cortex; iii. similar RSNs also can be identified in perfusion fMRI data; and iv. at least 5 distinct RSN patterns are reproducible across different subjects. The RSNs appear to reflect "default" interactions related to functional networks related to those recruited by specific types of cognitive processes. RSNs are a major source of non-modeled signal in BOLD fMRI data, so a full understanding of their dynamics will improve the interpretation of functional brain imaging studies more generally. Because RSNs reflect interactions in cognitively relevant functional networks, they offer a new approach to the characterization of state changes with pathology and the effects of drugs.

The combination of functional magnetic resonance imaging (FMRI) and electroencephalography (EEG) has received much recent attention, since it potentially offers a new tool for neuroscientists that makes simultaneous use of the strengths of the two modalities. However, EEG data collected in such experiments suffer from two kinds of artifact. First, gradient artifacts are caused by the switching of magnetic gradients during FMRI. Second, ballistocardiographic (BCG) artifacts related to cardiac activities further contaminate the EEG data. Here we present new methods to remove both kinds of artifact. The methods are based primarily on the idea that temporal variations in the artifacts can be captured by performing temporal principal component analysis (PCA), which leads to the identification of a set of basis functions which describe the temporal variations in the artifacts. These basis functions are then fitted to, and subtracted from, EEG data to produce artifact-free results. In addition, we also describe a robust algorithm for the accurate detection of heart beat peaks from poor quality electrocardiographic (ECG) data that are collected for the purpose of BCG artifact removal. The methods are tested and are shown to give superior results to existing methods. The methods also demonstrate the feasibility of simultaneous EEG/FMRI experiments using the relatively low EEG sampling frequency of 2048 Hz.

Color has a profound effect on the perception of odors. For example, strawberry-flavored drinks smell more pleasant when colored red than green and descriptions of the "nose" of a wine are dramatically influenced by its color. Using functional magnetic resonance imaging, we demonstrate a neurophysiological correlate of these cross-modal visual influences on olfactory perception. Subjects were scanned while exposed either to odors or colors in isolation or to color-odor combinations that were rated on the basis of how well they were perceived to match. Activity in caudal regions of the orbitofrontal cortex and in the insular cortex increased progressively with the perceived congruency of the odor-color pairs. These findings demonstrate the neuronal correlates of olfactory response modulation by color cues in brain areas previously identified as encoding the hedonic value of smells.

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.

We discuss model-free analysis of multisubject or multisession FMRI data by extending the single-session probabilistic independent component analysis model (PICA; Beckmann and Smith, 2004. IEEE Trans. on Medical Imaging, 23 (2) 137-152) to higher dimensions. This results in a three-way decomposition that represents the different signals and artefacts present in the data in terms of their temporal, spatial, and subject-dependent variations. The technique is derived from and compared with parallel factor analysis (PARAFAC; Harshman and Lundy, 1984. In Research methods for multimode data analysis, chapter 5, pages 122-215. Praeger, New York). Using simulated data as well as data from multisession and multisubject FMRI studies we demonstrate that the tensor PICA approach is able to efficiently and accurately extract signals of interest in the spatial, temporal, and subject/session domain. The final decompositions improve upon PARAFAC results in terms of greater accuracy, reduced interference between the different estimated sources (reduced cross-talk), robustness (against deviations of the data from modeling assumptions and against overfitting), and computational speed. On real FMRI 'activation' data, the tensor PICA approach is able to extract plausible activation maps, time courses, and session/subject modes as well as provide a rich description of additional processes of interest such as image artefacts or secondary activation patterns. The resulting data decomposition gives simple and useful representations of multisubject/multisession FMRI data that can aid the interpretation and optimization of group FMRI studies beyond what can be achieved using model-based analysis techniques.

We revisit a previous study on inter-session variability (McGonigle et al. [2000]: Neuroimage 11:708-734), showing that contrary to one popular interpretation of the original article, inter-session variability is not necessarily high. We also highlight how evaluating variability based on thresholded single-session images alone can be misleading. Finally, we show that the use of different first-level preprocessing, time-series statistics, and registration analysis methodologies can give significantly different inter-session analysis results., (Copyright 2005 Wiley-Liss, Inc.)

Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.

Functional magnetic resonance imaging studies often involve the acquisition of data from multiple sessions and/or multiple subjects. A hierarchical approach can be taken to modelling such data with a general linear model (GLM) at each level of the hierarchy introducing different random effects variance components. Inferring on these models is nontrivial with frequentist solutions being unavailable. A solution is to use a Bayesian framework. One important ingredient in this is the choice of prior on the variance components and top-level regression parameters. Due to the typically small numbers of sessions or subjects in neuroimaging, the choice of prior is critical. To alleviate this problem, we introduce to neuroimage modelling the approach of reference priors, which drives the choice of prior such that it is noninformative in an information-theoretic sense. We propose two inference techniques at the top level for multilevel hierarchies (a fast approach and a slower more accurate approach). We also demonstrate that we can infer on the top level of multilevel hierarchies by inferring on the levels of the hierarchy separately and passing summary statistics of a noncentral multivariate t distribution between them.

We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.

The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).

This article discusses general modeling of multisubject and/or multisession FMRI data. In particular, we show that a two-level mixed-effects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the single-session level) can be made equivalent to a single complete mixed-effects model (where parameters of interest at the group level are estimated directly from all of the original single sessions' time series data) if the (co-)variance at the second level is set equal to the sum of the (co-)variances in the single-level form, using the BLUE with known covariances. This result has significant implications for group studies in FMRI, since it shows that the group analysis requires only values of the parameter estimates and their (co-)variance from the first level, generalizing the well-established "summary statistics" approach in FMRI. The simple and generalized framework allows different prewhitening and different first-level regressors to be used for each subject. The framework incorporates multiple levels and cases such as repeated measures, paired or unpaired t tests and F tests at the group level; explicit examples of such models are given in the article. Using numerical simulations based on typical first-level covariance structures from real FMRI data we demonstrate that by taking into account lower-level covariances and heterogeneity a substantial increase in higher-level Z score is possible.

It is common clinical experience that anxiety about pain can exacerbate the pain sensation. Using event-related functional magnetic resonance imaging (FMRI), we compared activation responses to noxious thermal stimulation while perceived pain intensity was manipulated by changes in either physical intensity or induced anxiety. One visual signal, which reliably predicted noxious stimulation of moderate intensity, came to evoke low anxiety about the impending pain. Another visual signal was followed by the same, moderate-intensity stimulation on most of the trials, but occasionally by discriminably stronger noxious stimuli, and came to evoke higher anxiety. We found that the entorhinal cortex of the hippocampal formation responded differentially to identical noxious stimuli, dependent on whether the perceived pain intensity was enhanced by pain-relevant anxiety. During this emotional pain modulation, entorhinal responses predicted activity in closely connected, affective (perigenual cingulate), and intensity coding (mid-insula) areas. Our finding suggests that accurate preparatory information during medical and dental procedures alleviates pain by disengaging the hippocampus. It supports the proposal that during anxiety, the hippocampal formation amplifies aversive events to prime behavioral responses that are adaptive to the worst possible outcome.