Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI

Influence of heart rate on the BOLD signal: the cardiac response function

It has previously been shown that low-frequency fluctuations in both respiratory volume and cardiac rate can induce changes in the blood-oxygen level dependent (BOLD) signal. Such physiological noise can obscure the detection of neural activation using fMRI, and it is therefore important to model and remove the effects of this noise. While a hemodynamic response function relating respiratory variation (RV) and the BOLD signal has been described [Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A., 2008b. The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40, 644-654.], no such mapping for heart rate (HR) has been proposed. In the current study, the effects of RV and HR are simultaneously deconvolved from resting state fMRI. It is demonstrated that a convolution model including RV and HR can explain significantly more variance in gray matter BOLD signal than a model that includes RV alone, and an average HR response function is proposed that well characterizes our subject population. It is observed that the voxel-wise morphology of the deconvolved RV responses is preserved when HR is included in the model, and that its form is adequately modeled by Birn et al.'s previously-described respiration response function. Furthermore, it is shown that modeling out RV and HR can significantly alter functional connectivity maps of the default-mode network.

Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease

Encoding and retention of information in memory are associated with a sustained increase in the amplitude of neuronal oscillations for up to several seconds. We reasoned that coordination of oscillatory activity over time might be important for memory and, therefore, that the amplitude modulation of oscillations may be abnormal in Alzheimer disease (AD). To test this hypothesis, we measured magnetoencephalography (MEG) during eyes-closed rest in 19 patients diagnosed with early-stage AD and 16 age-matched control subjects and characterized the autocorrelation structure of ongoing oscillations using detrended fluctuation analysis and an analysis of the life- and waiting-time statistics of oscillation bursts. We found that Alzheimer's patients had a strongly reduced incidence of alpha-band oscillation bursts with long life- or waiting-times (< 1 s) over temporo-parietal regions and markedly weaker autocorrelations on long time scales (1-25 seconds). Interestingly, the life- and waiting-times of theta oscillations over medial prefrontal regions were greatly increased. Whereas both temporo-parietal alpha and medial prefrontal theta oscillations are associated with retrieval and retention of information, metabolic and structural deficits in early-stage AD are observed primarily in temporo-parietal areas, suggesting that the enhanced oscillations in medial prefrontal cortex reflect a compensatory mechanism. Together, our results suggest that amplitude modulation of neuronal oscillations is important for cognition and that indices of amplitude dynamics of oscillations may prove useful as neuroimaging biomarkers of early-stage AD.

Stability Of Default-Mode Network Activity In The Aging Brain

Activity attributed to the default-mode occurs during the resting state and is thought to represent self-referential and other intrinsic processes. Although activity in default-associated regions changes across the lifespan, little is known about the stability of default-mode activity in the healthy aging brain. We investigated changes in rest-specific activity across an 8 year period in older participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. Comparison of resting-state and recognition memory PET regional cerebral blood flow conditions from baseline and 8-year follow-up shows relative stability of rest-specific activity over time in medial frontal/anterior cingulate, hippocampal and posterior cingulate regions commonly associated with the default-mode. In contrast, prefrontal, parahippocampal and occipital cortical regions, which are not typically associated with default-mode activity, show changes over time Overall, activity in the major components of the default-mode network remains stable in healthy older individuals, a finding which may assist in identifying factors that discriminate between normal and pathological aging.

Resting-state functional connectivity reflects structural connectivity in the default mode network

Resting-state functional connectivity magnetic resonance imaging (fcMRI) studies constitute a growing proportion of functional brain imaging publications. This approach detects temporal correlations in spontaneous blood oxygen level-dependent (BOLD) signal oscillations while subjects rest quietly in the scanner. Although distinct resting-state networks related to vision, language, executive processing, and other sensory and cognitive domains have been identified, considerable skepticism remains as to whether resting-state functional connectivity maps reflect neural connectivity or simply track BOLD signal correlations driven by nonneural artifact. Here we combine diffusion tensor imaging (DTI) tractography with resting-state fcMRI to test the hypothesis that resting-state functional connectivity reflects structural connectivity. These 2 modalities were used to investigate connectivity within the default mode network, a set of brain regions--including medial prefrontal cortex (MPFC), medial temporal lobes (MTLs), and posterior cingulate cortex (PCC)/retropslenial cortex (RSC)--implicated in episodic memory processing. Using seed regions from the functional connectivity maps, the DTI analysis revealed robust structural connections between the MTLs and the retrosplenial cortex whereas tracts from the MPFC contacted the PCC (just rostral to the RSC). The results demonstrate that resting-state functional connectivity reflects structural connectivity and that combining modalities can enrich our understanding of these canonical brain networks.

Reaching across the abyss: recent advances in functional magnetic resonance imaging and their potential relevance to disorders of consciousness

Disorders of consciousness (DOC) raise profound scientific, clinical, ethical, and philosophical issues. Growing knowledge on fundamental principles of brain organization in healthy individuals offers new opportunities for a better understanding of residual brain function in DOCs. We here discuss new perspectives derived from a recently proposed scheme of brain organization underlying consciousness in healthy individuals. In this scheme, thalamo-cortical networks can be divided into two, often antagonistic, global systems: (i) a system of externally oriented, sensory-motor networks (the "extrinsic" system); and (ii) a system of inward-oriented networks (the "intrinsic" or default system). According to this framework, four distinct mental states would be possible that could be relevant for understanding DOCs. In normal healthy volunteers and locked-in syndrome patients, a state of high functionality of both the extrinsic and intrinsic or default systems is expected--associated with full awareness of environment and self. In this case, mental imagery tasks combined with fMRI can be used to detect covert awareness in patients that are unable to communicate. According to the framework, two complementary states of system imbalance are also possible, in which one system is in a hyperfunctional state, while the other is hypoactive. Extrinsic system hyperfunction is expected to lead to a state of total sensory-motor "absorption" or "lost self." In contrast, intrinsic or default system hyperfunction is expected to lead to a state of complete detachment from the external world. A state where both extrinsic and intrinsic systems are hypofunctional is predicted to lead to markedly impaired consciousness as seen in DOCs. Finally, we review the potential use of ultra-slow fluctuations in BOLD signal as a tool for assessing the functional integrity of extrinsic and intrinsic systems during "resting state" fMRI acquisitions. In particular, we discuss the potential provided by assessment of these slow spontaneous BOLD fluctuations as a novel tool in assessing the cognitive state and chances of recovery from brain pathologies underlying DOCs.

Microstructural organization of the cingulum tract and the level of default mode functional connectivity

The default mode network is a functionally connected network of brain regions that show highly synchronized intrinsic neuronal activation during rest. However, less is known about the structural connections of this network, which could play an important role in the observed functional connectivity patterns. In this study, we examined the microstructural organization of the cingulum tract in relation to the level of resting-state default mode functional synchronization. Resting-state functional magnetic resonance imaging and diffusion tensor imaging data of 45 healthy subjects were acquired on a 3 tesla scanner. Both structural and functional connectivity of the default mode network were examined. In all subjects, the cingulum tract was identified from the total collection of reconstructed tracts to interconnect the precuneus/posterior cingulate cortex and medial frontal cortex, key regions of the default mode network. A significant positive correlation was found between the average fractional anisotropy value of the cingulum tract and the level of functional connectivity between the precuneus/posterior cingulate cortex and medial frontal cortex. Our results suggest a direct relationship between the structural and functional connectivity measures of the default mode network and contribute to the understanding of default mode network connectivity.

Evidence for a frontoparietal control system revealed by intrinsic functional connectivity

Two functionally distinct, and potentially competing, brain networks have been recently identified that can be broadly distinguished by their contrasting roles in attention to the external world versus internally directed mentation involving long-term memory. At the core of these two networks are the dorsal attention system and the hippocampal-cortical memory system, a component of the brain's default network. Here spontaneous blood-oxygenation-level-dependent (BOLD) signal correlations were used in three separate functional magnetic resonance imaging data sets (n = 105) to define a third system, the frontoparietal control system, which is spatially interposed between these two previously defined systems. The frontoparietal control system includes many regions identified as supporting cognitive control and decision-making processes including lateral prefrontal cortex, anterior cingulate cortex, and inferior parietal lobule. Detailed analysis of frontal and parietal cortex, including use of high-resolution data, revealed clear evidence for contiguous but distinct regions: in general, the regions associated with the frontoparietal control system are situated between components of the dorsal attention and hippocampal-cortical memory systems. The frontoparietal control system is therefore anatomically positioned to integrate information from these two opposing brain systems.

Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations

Diffusion tensor imaging (DTI) and resting state temporal correlations (RSTC) are two leading techniques for investigating the connectivity of the human brain. They have been widely used to investigate the strength of anatomical and functional connections between distant brain regions in healthy subjects, and in clinical populations. Though they are both based on magnetic resonance imaging (MRI) they have not yet been compared directly. In this work both techniques were employed to create global connectivity matrices covering the whole brain gray matter. This allowed for direct comparisons between functional connectivity measured by RSTC with anatomical connectivity quantified using DTI tractography. We found that connectivity matrices obtained using both techniques showed significant agreement. Connectivity maps created for a priori defined anatomical regions showed significant correlation, and furthermore agreement was especially high in regions showing strong overall connectivity, such as those belonging to the default mode network. Direct comparison between functional RSTC and anatomical DTI connectivity, presented here for the first time, links two powerful approaches for investigating brain connectivity and shows their strong agreement. It provides a crucial multi-modal validation for resting state correlations as representing neuronal connectivity. The combination of both techniques presented here allows for further combining them to provide richer representation of brain connectivity both in the healthy brain and in clinical conditions.

Medial temporal lobe BOLD activity at rest predicts individual differences in memory ability in healthy young adults

Human beings differ in their ability to form and retrieve lasting long-term memories. To explore the source of these individual differences, we used functional magnetic resonance imaging to measure blood-oxygen-level-dependent (BOLD) activity in healthy young adults (n = 50) during periods of resting fixation that were interleaved with periods of simple cognitive tasks. We report that medial temporal lobe BOLD activity during periods of rest predicts individual differences in memory ability. Specifically, individuals who exhibited greater magnitudes of task-induced deactivations in medial temporal lobe BOLD signal (as compared to periods of rest) demonstrated superior memory during offline testing. This relationship was independent of differences in general cognitive function and persisted across different control tasks (i.e., number judgment versus checkerboard detection) and experimental designs (i.e., blocked versus event-related). These results offer a neurophysiological basis for the variability in mnemonic ability that is present amongst healthy young adults and may help to guide strategies aimed at early detection and intervention of neurological and mnemonic impairment.

Modulation of spontaneous fMRI activity in human visual cortex by behavioral state

The phenomenon of spontaneous fMRI activity is increasingly being exploited to investigate the connectivity of functional networks in human brain with high spatial-resolution. Although mounting evidence points towards a neuronal contribution to this activity, its functional role and dependence on behavioral state remain unclear. In this work, we used BOLD fMRI at 7 T to study the modulation of spontaneous activity in occipital areas by various behavioral conditions, including resting with eyes closed, eyes open with visual fixation, and eyes open with fixation and focal visual stimulation. Spontaneous activity was separated from evoked activity and from signal fluctuations related to cardiac and respiratory cycles. We found that spontaneous activity in visual areas was substantially reduced (amplitude (44%) and coherence (25%)) with the fixation conditions relative to the eyes-closed condition. No significant further modulation was observed when the visual stimulus was added. The observed dependence on behavioral condition suggests that part of spontaneous fMRI signal fluctuations represents neuronal activity. Possible mechanisms for the modulation of spontaneous activity by behavioral state are discussed. The observed linear superposition of spontaneous fMRI activity with focal evoked activity related to visual processing has important implications for fMRI studies, which ideally should take into account the effect of spontaneous activity to properly define brain activations during task conditions.

Functional imaging of stroke recovery: what have we learnt and where do we go from here?

Functional brain imaging techniques have been used to visualise patterns of activity following stroke and to characterise how these patterns change with recovery or rehabilitation. Some consensus is now emerging on patterns that are predictive of improved outcome, and therapeutic strategies are beginning to be guided by such findings. However, patient heterogeneity predicts that the same approach will not be appropriate for all. Future studies should aim to characterise the factors that influence this heterogeneity, and to individualise rehabilitation strategies based in part on early imaging findings. Functional imaging studies of stroke should also embrace recent methodological and conceptual advances that allow for fuller characterisation of the structural and functional properties of distributed brain networks.

White matter integrity linked to functional impairments in aging and early Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is associated with changes in cerebral white matter (WM), but the functional significance of such findings is not yet established. We hypothesized that diffusion tensor imaging (DTI) might reveal links between regional WM changes and specific neuropsychologically and psychophysically defined impairments in early AD.

METHODS

Older adult control subjects (OA, n = 18) and mildly impaired AD patients (n = 14) underwent neuropsychological and visual perceptual testing along with DTI of cerebral WM. DTI yielded factional anisotropy (FA) and mean diffusivity (D) maps for nine regions of interest in three brain regions that were then compared with the performance measures.

RESULTS

AD patients exhibited nonsignificant trends toward lower FAs in the posterior region's callosal and subcortical regions of interest. However, posterior callosal FA was significantly correlated with verbal fluency and figural memory impairments, whereas posterior subcortical FA was correlated with delayed verbal memory, figural memory, and optic flow perceptual impairments.

CONCLUSIONS

WM changes in early AD are concentrated in posterior cerebral areas, with distributions that correspond to specific functional impairments. DTI can be used to assess regional pathology related to individual's deficits in early AD.

Control networks in paediatric Tourette syndrome show immature and anomalous patterns of functional connectivity

Tourette syndrome (TS) is a developmental disorder characterized by unwanted, repetitive behaviours that manifest as stereotyped movements and vocalizations called ‘tics’. Operating under the hypothesis that the brain's control systems may be impaired in TS, we measured resting-state functional connectivity MRI (rs-fcMRI) between 39 previously defined putative control regions in 33 adolescents with TS. We were particularly interested in the effect of TS on two of the brain's task control networks—a fronto-parietal network likely involved in more rapid, adaptive online control, and a cingulo-opercular network apparently important for set-maintenance. To examine the relative maturity of connections in the Tourette subjects, functional connections that changed significantly over typical development were examined. Age curves were created for each functional connection charting correlation coefficients over age for 210 healthy people aged 7–31 years, and the TS group correlation coefficients were compared to these curves. Many of these connections were significantly less ‘mature’ than expected in the TS group. This immaturity was true not only for functional connections that grow stronger with age, but also for those that diminish in strength with age. To explore other differences between Tourette and typically developing subjects further, we performed a second analysis in which the TS group was directly compared to an age-matched, movement-matched group of typically developing, unaffected adolescents. A number of functional connections were found to differ between the two groups. For these identified connections, a large number of connectional differences were found where the TS group value was out of range compared to typical developmental age curves. These anomalous connections were primarily found in the fronto-parietal network, thought to be important for online adaptive control. These results suggest that in adolescents with TS, immature functional connectivity is widespread, with additional, more profound deviation of connectivity in regions related to adaptive online control.

Mapping and correction of vascular hemodynamic latency in the BOLD signal

Correlation and causality metrics can be applied to blood-oxygen level-dependent (BOLD) signal time series in order to infer neural synchrony and directions of information flow from fMRI data. However, the BOLD signal reflects both the underlying neural activity and the vascular response, the latter of which is governed by local vasomotor physiology. The presence of potential vascular latency differences thus poses a confound in the detection of neural synchrony as well as inferences about the causality of neural processes. In the present study, we investigate the use of a breath holding (BH) task for characterizing and correcting for voxel-wise neurovascular latency differences across the whole brain. We demonstrate that BH yields reliable measurements of relative timing differences between voxels, and further show that a BH-derived latency correction can impact both functional connectivity maps of the resting-state default-mode network and activation maps of an event-related working memory (WM) task.

Origin of synchronized low-frequency blood oxygen level-dependent fluctuations in the primary visual cortex

BACKGROUND AND PURPOSE

Low-frequency (<0.08 Hz) fluctuations in spontaneous blood oxygen level-dependent (BOLD) signal intensity show synchronization across anatomically interconnected and functionally specific brain regions, suggesting a neural origin of fluctuations. To determine the mechanism by which high-frequency neural activity results in low-frequency BOLD fluctuations, I obtained measurements of the effects of neurovascular coupling on the frequency content of BOLD fluctuations.

MATERIALS AND METHODS

3T recordings of BOLD signal intensity in the primary visual cortex were obtained in response to visual stimuli presented at varying temporal frequencies to determine which stimulus frequencies were successfully transmitted to BOLD signal intensity. Additional BOLD time series recordings were performed in a resting state and during natural visual stimulation, and frequencies comprising BOLD fluctuations were measured. Magnetoencephalographic (MEG) time series recordings were obtained in a resting state to measure which components of MEG signal intensity best correlated in frequency distribution to observed BOLD fluctuations.

RESULTS

Visually driven oscillations in BOLD signal intensity were observed up to 0.2 Hz, representing a mismatch between low-pass filter properties of neurovascular coupling and observed frequencies of spontaneous BOLD fluctuations, which are <0.05 Hz in the primary visual cortex. Visual stimulation frequencies of >0.2 Hz resulted in frequency-dependent increases in mean BOLD response. Amplitude modulation of high-frequency neural activity was measured in MEG time series data, which demonstrated 1/frequency distribution with the greatest power comprising frequencies <0.05 Hz, consistent with the distribution of observed BOLD fluctuations.

CONCLUSION

Synchronized low-frequency BOLD fluctuations likely arise from a combination of vascular low-pass filtering and low-frequency amplitude modulation of neural activity.

Distinctive alterations of the cingulum bundle during aging and Alzheimer's disease

Brain imaging studies have revealed frontal disruption during aging and parieto-temporal disruption during Alzheimer's disease (AD). The present study aims at developing a specific method based on precise anatomical landmarks for assessing the integrity all along the course of the cingulum bundle, so as to determine if it presents the classical aging and AD dissociation. Five regions of interest (ROIs) were placed on fractional anisotropy (FA) maps all along the cingulum in 15 young (Gyoung), 15 70-year-old (Gold), and 15 AD subjects (Galz). An age-related decrease of FA occurred in the anterior part of the bundle. Moreover, a specific alteration of the supero-posterior region of the cingulum during AD was observed since mean FA values as well as mean number of fibers were significantly decreased in Galz compared to Gold and Gyoung. This multiple ROIs placement allows for revealing distinctive alterations of the cingulum bundle during aging and AD, which could constitute the anatomical basis for the distinctive functional disconnection recently described in the literature using functional connectivity at rest.

Dedifferentiation in the visual cortex: an fMRI investigation of individual differences in older adults

Dedifferentiation, or decreased processing specificity, has been suggested to represent a ubiquitous characteristic of cognitive aging. In this study, we examined both age-related differences and intra-group differences in neural specificity in the ventral visual cortex for color, words, faces and places. Our results demonstrated that neural dedifferentiation was not ubiquitous across stimulus categories. Neural dedifferentiation was also relatively stable, across age, in a group of older adults. Older adults with more overall gray matter showed less neural dedifferentiation in the visual cortex. However, regional gray matter volume was not associated with neural dedifferentiation. We illustrate these effects using a discriminability metric, a signal detection theory measure, for neural dedifferentiation that takes into account both magnitude and variance of brain activation. The dedifferentiation measure provides a quantitative means to examine activation patterns and individual difference factors associated with neural dedifferentiation, and to test theories of behavioral dedifferentiation in cognitive aging literature.

Resting-state functional connectivity in neuropsychiatric disorders

PURPOSE OF REVIEW

This review considers recent advances in the application of resting-state functional magnetic resonance imaging to the study of neuropsychiatric disorders.

RECENT FINDINGS

Resting-state functional magnetic resonance imaging is a relatively novel technique that has several potential advantages over task-activation functional magnetic resonance imaging in terms of its clinical applicability. A number of research groups have begun to investigate the use of resting-state functional magnetic resonance imaging in a variety of neuropsychiatric disorders including Alzheimer's disease, depression, and schizophrenia. Although preliminary results have been fairly consistent in some disorders (for example, Alzheimer's disease) they have been less reproducible in others (schizophrenia). Resting-state connectivity has been shown to correlate with behavioral performance and emotional measures. It's potential as a biomarker of disease and an early objective marker of treatment response is genuine but still to be realized.

SUMMARY

Resting-state functional magnetic resonance imaging has made some strides in the clinical realm but significant advances are required before it can be used in a meaningful way at the single-patient level.

Neuroimaging in dementia

Although dementia is a clinical diagnosis, neuroimaging often is crucial for proper assessment. Magnetic resonance imaging (MRI) and computed tomography (CT) may identify nondegenerative and potentially treatable causes of dementia. Recent neuroimaging advances, such as the Pittsburgh Compound-B (PIB) ligand for positron emission tomography imaging in Alzheimer's disease, will improve our ability to differentiate among the neurodegenerative dementias. High-resolution volumetric MRI has increased the capacity to identify the various forms of the frontotemporal lobar degeneration spectrum and some forms of parkinsonism or cerebellar neurodegenerative disorders, such as corticobasal degeneration, progressive supranuclear palsy, multiple system atrophy, and spinocerebellar ataxias. In many cases, the specific pattern of cortical and subcortical abnormalities on MRI has diagnostic utility. Finally, among the new MRI methods, diffusion-weighted MRI can help in the early diagnosis of Creutzfeldt-Jakob disease. Although only clinical assessment can lead to a diagnosis of dementia, neuroimaging is clearly an invaluable tool for the clinician in the differential diagnosis.

Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex

Animal studies have shown robust electrophysiological activity in the sensory cortex in the absence of stimuli or tasks. Similarly, recent human functional magnetic resonance imaging (fMRI) revealed widespread, spontaneously emerging cortical fluctuations. However, it is unknown what neuronal dynamics underlie this spontaneous activity in the human brain. Here we studied this issue by combining bilateral single-unit, local field potentials (LFPs) and intracranial electrocorticography (ECoG) recordings in individuals undergoing clinical monitoring. We found slow (<0.1 Hz, following 1/f-like profiles) spontaneous fluctuations of neuronal activity with significant interhemispheric correlations. These fluctuations were evident mainly in neuronal firing rates and in gamma (40-100 Hz) LFP power modulations. Notably, the interhemispheric correlations were enhanced during rapid eye movement and stage 2 sleep. Multiple intracranial ECoG recordings revealed clear selectivity for functional networks in the spontaneous gamma LFP power modulations. Our results point to slow spontaneous modulations in firing rate and gamma LFP as the likely correlates of spontaneous fMRI fluctuations in the human sensory cortex.

Frequency specificity of functional connectivity in brain networks

Synchronized low-frequency spontaneous fluctuations of the functional MRI (fMRI) signal have been shown to be associated with electroencephalography (EEG) power fluctuations in multiple brain networks within predefined frequency bands. However, it remains unclear whether frequency-specific characteristics exist in the resting-state fMRI signal. In this study, fMRI signals in five functional brain networks (sensorimotor, 'default mode', visual, amygdala, and hippocampus) were decomposed into various frequency bands within a low-frequency range (0-0.24 Hz). Results show that the correlations in cortical networks concentrate within ultra-low frequencies (0.01-0.06 Hz) while connections within limbic networks distribute over a wider frequency range (0.01-0.14 Hz), suggesting distinct frequency-specific features in the resting-state fMRI signal within these functional networks. Moreover, the connectivity decay rates along the frequency bands are positively correlated with the physical distances between connected brain regions and seed points. This distance-frequency relationship might be attributed to a larger attenuation of synchrony of brain regions separated with longer distance and/or connected with more synaptic steps.

Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity

Recent studies indicate that functional connectivity using low-frequency BOLD fluctuations (LFBFs) is reduced between the bilateral primary sensorimotor regions in multiple sclerosis. In addition, it has been shown that pathway-dependent measures of the transverse diffusivity of water in white matter correlate with related clinical measures of functional deficit in multiple sclerosis. Taken together, these methods suggest that MRI methods can be used to probe both functional connectivity and anatomic connectivity in subjects with known white matter impairment. We report the results of a study comparing anatomic connectivity of the transcallosal motor pathway, as measured with diffusion tensor imaging (DTI) and functional connectivity of the bilateral primary sensorimotor cortices (SMC), as measured with LFBFs in the resting state. High angular resolution diffusion imaging was combined with functional MRI to define the transcallosal white matter pathway connecting the bilateral primary SMC. Maps were generated from the probabilistic tracking employed and these maps were used to calculate the mean pathway diffusion measures fractional anisotropy FA, mean diffusivity MD, longitudinal diffusivity lambda(1), and transverse diffusivity lambda(2). These were compared with LFBF-based functional connectivity measures (F(c)) obtained at rest in a cohort of 11 multiple sclerosis patients and approximately 10 age- and gender-matched control subjects. The correlation between FA and F(c) for MS patients was r = -0.63, P < 0.04. The correlation between all subjects lambda(2) and F(c) was r = 0.42, P < 0.05. The correlation between all subjects lambda(2) and F(c) was r = -0.50, P < 0.02. None of the control subject correlations were significant, nor were FA, lambda(1), or MD significantly correlated with F(c) for MS patients. This constitutes the first in vivo observation of a correlation between measures of anatomic connectivity and functional connectivity using spontaneous LFBFs.

Resting in peace or noise: scanner background noise suppresses default-mode network

Studies have identified specific brain regions that increase activation during rest relative to attention-demanding tasks; these regions subserve the "default mode of brain function". Most of these studies have been conducted in the presence of scanner background noise (SBN). This noise has been shown to lead to altered attentional demands, and thus may modulate the default-mode network. Twelve subjects were examined during a rest condition that was contrasted with an auditory task. Words were presented either with SBN employing a conventional acquisition or without SBN using a sparse sampling approach. The number of experimental and resting trials was equated between the designs. Selecting the images in the condition with SBN that corresponded in time with the images in the condition without SBN made a direct comparison of the default-mode network (rest contrasted with active task) possible. There was typical activation of the default-mode network during rest versus task for both designs. However, SBN suppressed major components of the default-mode network, including medial prefrontal cortex, posterior cingulate, and precuneus. Our results suggest that the default mode of brain function differs when assessed in the presence compared to the absence of scanner noise, with the presence of scanner noise perhaps adding attentional demands that diminish activation changes between rest and task in a nonlinear way within the default network. Further studies are needed to clarify whether the use of a sparse sampling technique might enhance clinical utilities that have been proposed for analysis of the default-mode network.

Magnetic resonance imaging of neural circuits

A major goal of modern MRI research is to be able to image neural circuits in the central nervous system. Critical to this mission is the ability to describe a number of important parameters associated with neural circuits. These parameters include neural architecture, functional activation of neural circuits, anatomical and functional connectivity of neural circuits, and factors that might alter neural circuits, such as trafficking of immune cells and brain precursor cells in the brain. Remarkably, a variety of work in human and animal brains has demonstrated that all these features of neural circuits can be visualized with MRI. In this Article we provide a brief summary of the new directions in neural imaging research, which should prove useful in future analyses of normal and pathological human brains and in studies of animal models of neurological and psychiatric disorders. At present, few MRI data characterizing the neural circuits in the heart are available, but in this Article we discuss the applicable present developments and the prospects for the future.

Spontaneous low-frequency blood oxygenation level-dependent fluctuations and functional connectivity analysis of the 'resting' brain

Functional magnetic resonance imaging techniques using the blood oxygenation level-dependent (BOLD) contrast are widely used to map human brain function by relating local hemodynamic responses to neuronal stimuli compared to control conditions. There is increasing interest in spontaneous cerebral BOLD fluctuations that are prominent in the low-frequency range (<0.1 Hz) and show intriguing spatio-temporal correlations in functional networks. The nature of these signal fluctuations remains unclear, but there is accumulating evidence for a neural basis opening exciting new avenues to study human brain function and its connectivity at rest. Moreover, an increasing number of patient studies report disease-dependent variation in the amplitude and spatial coherence of low-frequency BOLD fluctuations (LFBF) that may afford greater diagnostic sensitivity and easier clinical applicability than standard fMRI. The main disadvantage of this emerging tool relates to physiological (respiratory, cardiac and vasomotion) and motion confounds that are challenging to disentangle requiring thorough preprocessing. Technical aspects of functional connectivity fMRI analysis and the neuroscientific potential of spontaneous LFBF in the default mode and other resting-state networks have been recently reviewed. This review will give an update on the current knowledge of the nature of LFBF, their relation to physiological confounds and potential for clinical diagnostic and pharmacological studies.

Altered functional connectivity of primary visual cortex in early blindness

In early blindness, the primary visual area (PVA) loses the ability to process visual information, and shifts to working on the processing of somatosensory input, auditory input, and some higher-level cognitive functions. It has not yet been investigated whether such functional changes can lead to alterations of the functional connectivity between the PVA and other brain areas in resting state. The purpose of this study is to investigate the differences in the functional connectivity of the PVA between early blind and sighted subjects using resting functional MRI data. The altered functional connectivity was identified by comparing the correlation coefficients of the PVA with other brain areas between 16 early blind subjects (blindness onset within 1 year of age) and 32 gender- and age-matched healthy sighted volunteers. Compared with the sighted, the early blind subjects showed decreased functional connectivity between the left PVA and the bilateral supplementary motor area (SMA), pre- and postcentral gyri, superior parietal lobule, and the left superior and middle temporal gyri. Early blind subjects also showed decreased functional connectivity between the right PVA and the bilateral SMA, pre- and postcentral gyri. Our findings suggest that early deprivation of a single sensory modality induces alterations of functional connectivity between the deprived functional area and other associated brain areas.

An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF

Most of the resting-state functional magnetic resonance imaging (fMRI) studies demonstrated the correlations between spatially distinct brain areas from the perspective of functional connectivity or functional integration. The functional connectivity approaches do not directly provide information of the amplitude of brain activity of each brain region within a network. Alternatively, an index named amplitude of low-frequency fluctuation (ALFF) of the resting-state fMRI signal has been suggested to reflect the intensity of regional spontaneous brain activity. However, it has been indicated that the ALFF is also sensitive to the physiological noise. The current study proposed a fractional ALFF (fALFF) approach, i.e., the ratio of power spectrum of low-frequency (0.01-0.08 Hz) to that of the entire frequency range and this approach was tested in two groups of resting-state fMRI data. The results showed that the brain areas within the default mode network including posterior cingulate cortex, precuneus, medial prefrontal cortex and bilateral inferior parietal lobule had significantly higher fALFF than the other brain areas. This pattern was consistent with previous neuroimaging results. The non-specific signal components in the cistern areas in resting-state fMRI were significantly suppressed, indicating that the fALFF approach improved the sensitivity and specificity in detecting spontaneous brain activities. Its mechanism and sensitivity to abnormal brain activity should be evaluated in the future studies.

Default-mode function and task-induced deactivation have overlapping brain substrates in children

The regions that comprise the functionally connected resting-state default-mode network (DMN) in adults appear to be the same as those that are characterized by task-induced decreases in blood-oxygen-level-dependent (BOLD) signal. Independent component analysis can be used to produce a picture of the DMN as an individual rests quietly in the scanner. Contrasts across conditions in which cognitive load is parametrically modulated can delineate neural structures that have decreases in activation in response to high-demand task conditions. Examination of the degree to which these networks subsume dissociable brain substrates, and of the degree to which they overlap, provides insight concerning their purpose, function, and the nature of their associations. Few studies have examined the DMN in children, and none have tested whether the neural regions that comprise the DMN during a resting condition are the same regions that show reduced activity when children engage in cognitive tasks. In this paper we describe regions that show both task-related decreases and spontaneous intrinsic activity at rest in children, and we examine the co-localization of these networks. We describe ways in which the DMN in 7-12-year-old children is both similar to and different from the DMN in adults; moreover, we document that task-induced deactivations and default-mode resting-state activity in children share common neural substrates. It appears, therefore, that even before adolescence a core aspect of task-induced deactivation involves reallocating processing resources that are active at rest. We describe how future studies assessing the development of these systems would benefit from examining these constructs as part of one continuous system.

Altered medial temporal lobe responses during visuospatial encoding in healthy APOE*4 carriers

The apolipoprotein varepsilon4 allele (APOE*4) is a major genetic risk factor for Alzheimer's disease (AD) and has been associated with altered cortical activation as assessed by functional neuroimaging in cognitively normal younger and older carriers. We chose to evaluate medial temporal lobe (MTL) activation during encoding and recognition using a perspective-dependent (route or survey) visuospatial memory task by monitoring the blood-oxygen-level-dependent (BOLD) fMRI response in older, non-demented APOE*4 carriers (APOE*4+) and non-carriers (APOE*4-). During encoding, the APOE*4- group had greater average task-associated BOLD responses in ventral visual pathways, including the MTLs, as compared to the APOE*4+ group. Furthermore, MTL activation was greater during route encoding than survey encoding on average in APOE*4-, but not APOE*4+, subjects. During recognition, both groups performed similarly and no BOLD signal differences were found. Finally, within-group analysis revealed MTL activation during encoding was correlated with recognition performance in APOE*4-, but not APOE*4+ subjects. Reduced task-associated MTL activation that does not correlate with either visuospatial perspective or task performance suggests that MTL dysregulation occurs prior to clinical symptoms of dementia in APOE*4 carriers.

fMRI: use in early Alzheimer’s disease and in clinical trials

The pathophysiological process of Alzheimer’s disease (AD) begins years, even decades, prior to the time a clinical diagnosis can be established. This long asymptomatic or minimally symptomatic phase of AD provides a potential period for early therapeutic interventions to slow and perhaps ultimately prevent the progression to clinical dementia. Functional MRI (fMRI) provides an in vivo means to investigate alterations in brain function related to the earliest symptoms of AD, possibly before development of significant irreversible structural damage. fMRI during tasks probing episodic memory, which is the cognitive function most characteristically impaired in early AD, are of particular interest. In this paper, we review the current knowledge of the pathophysiological fMRI correlates of AD and of at-risk states for AD, such as presence of mild cognitive impairment or the apolipoprotein E ε4 allele. We will summarize previous studies demonstrating changes in task-related fMRI activity, primarily focusing on memory tasks, as well as studies investigating resting-state fMRI findings in clinical AD patients and at-risk subjects compared with healthy elderly individuals. We will also discuss the potential use of fMRI in clinical trials of AD therapeutic agents, as well as the limitations of this promising imaging technique.

Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks

Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail.

The effect of respiration variations on independent component analysis results of resting state functional connectivity

The analysis of functional connectivity in fMRI can be severely affected by cardiac and respiratory fluctuations. While some of these artifactual signal changes can be reduced by physiological noise correction routines, signal fluctuations induced by slower breath-to-breath changes in the depth and rate of breathing are typically not removed. These slower respiration-induced signal changes occur at low frequencies and spatial locations similar to the fluctuations used to infer functional connectivity, and have been shown to significantly affect seed-ROI or seed-voxel based functional connectivity analysis, particularly in the default mode network. In this study, we investigate the effect of respiration variations on functional connectivity maps derived from independent component analysis (ICA) of resting-state data. Regions of the default mode network were identified by deactivations during a lexical decision task. Variations in respiration were measured independently and correlated with the MRI time series data. ICA appears to separate the default mode network and the respiration-related changes in most cases. In some cases, however, the component automatically identified as the default mode network was the same as the component identified as respiration-related. Furthermore, in most cases the time series associated with the default mode network component was still significantly correlated with changes in respiration volume per time, suggesting that current methods of ICA may not completely separate respiration from the default mode network. An independent measure of the respiration provides valuable information to help distinguish the default mode network from respiration-related signal changes, and to assess the degree of residual respiration related effects.

Endogenous brain fluctuations and diagnostic imaging

Much of the rising health care costs in aging populations can be attributed to congenital disease and psychiatric and neurologic disorders. Early detection of changes related to these diseases can promote the development of new therapeutic strategies and effective treatments. Changes in tissue, such as damage resulting from continued functional abnormality, often exhibit a time-delay before detection is possible. Methods for detecting functional alterations in endogenous brain fluctuations allow for an early diagnosis before tissue damage occurs, enabling early treatment and a more likely positive outcome. A literature review and comprehensive overview of the current state of knowledge about endogenous brain fluctuations is presented here. Recent findings of the association between various pathological conditions and endogenous fluctuations are discussed. A particular emphasis is placed on research showing the relationship between clinical measures and pathological findings to the dynamics of endogenous fluctuations of the brain. Recent discoveries of methods for detecting abnormal functional connectivity are discussed and future research directions explored.

Persistent default-mode network connectivity during light sedation

The default-mode network (DMN) is a set of specific brain regions whose activity, predominant in the resting-state, is attenuated during cognitively demanding, externally-cued tasks. The cognitive correlates of this network have proven difficult to interrogate, but one hypothesis is that regions in the network process episodic memories and semantic knowledge integral to internally-generated mental activity. Here, we compare default-mode functional connectivity in the same group of subjects during rest and conscious sedation with midazolam, a state characterized by anterograde amnesia and a reduced level of consciousness. Although the DMN showed functional connectivity during both rest and conscious sedation, a direct comparison found that there was significantly reduced functional connectivity in the posterior cingulate cortex during conscious sedation. These results confirm that low-frequency oscillations in the DMN persist and remain highly correlated even at reduced levels of consciousness. We hypothesize that focal reductions in DMN connectivity, as shown here in the posterior cingulate cortex, may represent a stable correlate of reduced consciousness.

Concept of functional imaging of memory decline in Alzheimer's disease

Functional imaging methods such as Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI) have contributed inestimably to the understanding of physiological cognitive processes in the brain in the recent decades. These techniques for the first time allowed the in vivo assessment of different features of brain function in the living human subject. It was therefore obvious to apply these methods to evaluate pathomechanisms of cognitive dysfunction in disorders such as Alzheimer's disease (AD) as well. One of the most dominant symptoms of AD is the impairment of memory. In this context, the term "memory" represents a simplification and summarizes a set of complex cognitive functions associated with encoding and retrieval of different types of information. A number of imaging studies assessed the functional changes of neuronal activity in the brain at rest and also during performance of cognitive work, with regard to specific characteristics of memory decline in AD. In the current article, basic principles of common functional imaging procedures will be explained and it will be discussed how they can be reasonably applied for the assessment of memory decline in AD. Furthermore, it will be illustrated how these imaging procedures have been employed to improve early and specific diagnosis of the disease, to understand specific pathomechanisms of memory dysfunction and associated compensatory mechanisms, and to draw reverse conclusions on physiological function of memory.

Network analysis of intrinsic functional brain connectivity in Alzheimer's disease

Functional brain networks detected in task-free (“resting-state”) functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.

Alzheimer's disease (AD) is a brain disorder characterized by progressive impairment of episodic memory and other cognitive domains resulting in dementia and, ultimately, death. Functional neuroimaging studies have identified brain regions that show abnormal brain function in AD. Although there is converging evidence about the identity of these regions, it is not clear how this abnormality affects the functional organization of the whole brain. In order to characterize the functional organization of the brain, our approach uses small-world measures, which have also been used to study systems such as social networks and the internet. We use graph analytical methods to compute these measures of functional connectivity brain networks, which are derived from fMRI data obtained from healthy elderly controls and AD patients. The AD patients had significantly lower regional connectivity, and showed disrupted global functional organization, when compared to healthy controls. Moreover, our results indicate that cognitive decline in Alzheimer's disease patients is associated with disrupted functional connectivity in the entire brain. Our findings further suggest that small-world measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.

The brain's default network: anatomy, function, and relevance to disease

Thirty years of brain imaging research has converged to define the brain's default network-a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment. Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system. Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others. Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems. The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations. These two subsystems converge on important nodes of integration including the posterior cingulate cortex. The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world. We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease.

Episodic simulation of future events: concepts, data, and applications

This article focuses on the neural and cognitive processes that support imagining or simulating future events, a topic that has recently emerged in the forefront of cognitive neuroscience. We begin by considering concepts of simulation from a number of areas of psychology and cognitive neuroscience in order to place our use of the term in a broader context. We then review neuroimaging, neuropsychological, and cognitive studies that have examined future-event simulation and its relation to episodic memory. This research supports the idea that simulating possible future events depends on much of the same neural machinery, referred to here as a core network, as does remembering past events. After discussing several theoretical accounts of the data, we consider applications of work on episodic simulation for research concerning clinical populations suffering from anxiety or depression. Finally, we consider other aspects of future-oriented thinking that we think are related to episodic simulation, including planning, prediction, and remembering intentions. These processes together comprise what we have termed "the prospective brain," whose primary function is to use past experiences to anticipate future events.

The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis

Recent research has shown that intrinsic brain activity as observed by functional magnetic resonance imaging (fMRI) manifest itself as coherent signal changes in networks encompassing brain regions that span long-range neuronal pathways. One of these networks, the so called default mode network, has become the primary target in recent investigations to link intrinsic activity to cognition and how intrinsic signal changes may be altered in disease. In this study we assessed functional connectivity within the default mode network during both rest and a continuous working memory task on a region-by-region basis using partial correlation analysis, a data-driven method that provides insight into effective connectivity within neuronal networks. Prominent features of functional connectivity within the default mode network included an overall strong level of interaction between the precuneus/posterior cingulate region and the rest of the default mode network, as well as a high degree of interaction between the left and right medial temporal lobes combined with weak interactions between the medial temporal lobes and the rest of the default mode network. Additionally, we found support for strong interactions between the precuneus/posterior cingulate cortex and the left inferior parietal lobe as well as between the dorsal and ventral sections of the medial prefrontal cortex. The suggested pivotal role of the precuneus/posterior cingulate cortex in the default mode network is discussed.

Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease

Recent research on Alzheimer's disease (AD) has shown that cognitive and memory decline in this disease is accompanied by disrupted changes in the coordination of large-scale brain functional networks. However, alterations in coordinated patterns of structural brain networks in AD are still poorly understood. Here, we used cortical thickness measurement from magnetic resonance imaging to investigate large-scale structural brain networks in 92 AD patients and 97 normal controls. Brain networks were constructed by thresholding cortical thickness correlation matrices of 54 regions and analyzed using graph theoretical approaches. Compared with controls, AD patients showed decreased cortical thickness intercorrelations between the bilateral parietal regions and increased intercorrelations in several selective regions involving the lateral temporal and parietal cortex as well as the cingulate and medial frontal cortex regions. Specially, AD patients showed abnormal small-world architecture in the structural cortical networks (increased clustering and shortest paths linking individual regions), implying a less optimal topological organization in AD. Moreover, AD patients were associated with reduced nodal centrality predominantly in the temporal and parietal heteromodal association cortex regions and increased nodal centrality in the occipital cortex regions. Finally, the brain networks of AD were about equally as robust to random failures as those of controls, but more vulnerable against targeted attacks, presumably because of the effects of pathological topological organization. Our findings suggest that the coordinated patterns of cortical morphology are widely altered in AD patients, thus providing structural evidence for disrupted integrity in large-scale brain networks that underlie cognition. This work has implications for our understanding of how functional deficits in patients are associated with their underlying structural (morphological) basis.

Acupuncture modulates resting state connectivity in default and sensorimotor brain networks

Previous studies have defined low-frequency, spatially consistent networks in resting fMRI data which may reflect functional connectivity. We sought to explore how a complex somatosensory stimulation, acupuncture, influences intrinsic connectivity in two of these networks: the default mode network (DMN) and sensorimotor network (SMN). We analyzed resting fMRI data taken before and after verum and sham acupuncture. Electrocardiography data were used to infer autonomic modulation through measures of heart rate variability (HRV). Probabilistic independent component analysis was used to separate resting fMRI data into DMN and SMN components. Following verum, but not sham, acupuncture there was increased DMN connectivity with pain (anterior cingulate cortex (ACC), periaqueductal gray), affective (amygdala, ACC), and memory (hippocampal formation, middle temporal gyrus) related brain regions. Furthermore, increased DMN connectivity with the hippocampal formation, a region known to support memory and interconnected with autonomic brain regions, was negatively correlated with acupuncture-induced increase in a sympathetic related HRV metric (LFu), and positively correlated with a parasympathetic related metric (HFu). Following verum, but not sham, acupuncture there was also increased SMN connectivity with pain-related brain regions (ACC, cerebellum). We attribute differences between verum and sham acupuncture to more varied and stronger sensations evoked by verum acupuncture. Our results demonstrate for the first time that acupuncture can enhance the post-stimulation spatial extent of resting brain networks to include anti-nociceptive, memory, and affective brain regions. This modulation and sympathovagal response may relate to acupuncture analgesia and other potential therapeutic effects.

Defining functional areas in individual human brains using resting functional connectivity MRI

The cerebral cortex is anatomically organized at many physical scales starting at the level of single neurons and extending up to functional systems. Current functional magnetic resonance imaging (fMRI) studies often focus at the level of areas, networks, and systems. Except in restricted domains, (e.g., topographically-organized sensory regions), it is difficult to determine area boundaries in the human brain using fMRI. The ability to delineate functional areas non-invasively would enhance the quality of many experimental analyses allowing more accurate across-subject comparisons of independently identified functional areas. Correlations in spontaneous BOLD activity, often referred to as resting state functional connectivity (rs-fcMRI), are especially promising as a way to accurately localize differences in patterns of activity across large expanses of cortex. In the current report, we applied a novel set of image analysis tools to explore the utility of rs-fcMRI for defining wide-ranging functional area boundaries. We find that rs-fcMRI patterns show sharp transitions in correlation patterns and that these putative areal boundaries can be reliably detected in individual subjects as well as in group data. Additionally, combining surface-based analysis techniques with image processing algorithms allows automated mapping of putative areal boundaries across large expanses of cortex without the need for prior information about a region's function or topography. Our approach reliably produces maps of bounded regions appropriate in size and number for putative functional areas. These findings will hopefully stimulate further methodological refinements and validations.