Resting-state networks derived from temporal correlations of spontaneous hemodynamic fluctuations have been extensively used to elucidate the functional organization of the brain in adults and infants. We have previously developed functional connectivity diffuse optical tomography methods in adults, and we now apply these techniques to study functional connectivity in newborn infants at the bedside. We present functional connectivity maps in the occipital cortices obtained from healthy term-born infants and premature infants, including one infant with an occipital stroke. Our results suggest that functional connectivity diffuse optical tomography has potential as a valuable clinical tool for the early detection of functional deficits and for providing prognostic information on future development.
Resting state; Functional connectivity; Neonates; Prematurity; Optical tomography
Using rare intracranial recordings from the posterior interhemispheric region of the human brain, we explored the oscillatory properties of the posteromedial cortex (PMC) during rest. The PMC is a core structure of the default mode network, which is known for its higher activity during the resting state. We found that resting PMC spectral power peaked in the theta band range (4–7 Hz) and was clearly distinguishable from adjacent cortical sites in the occipital lobe displaying peaks in the alpha band range (8–12 Hz). Additionally, the phase of PMC theta oscillations modulated the amplitude of ongoing high gamma (70–180Hz) activity during the resting state. The magnitude of this cross-frequency modulation was shown to fluctuate at time scales comparable to those observed in functional neuroimaging studies of intrinsic functional connectivity networks (~0.1 Hz). The difference of canonical oscillations in the PMC compared to its adjacent cortical sites conforms to functional specialization across anatomical boundaries. Such differences may reflect separate oscillatory preferences between networks that are functionally connected.
Electrocorticography; posteromedial cortex; theta oscillations; phase-amplitude coupling; default mode network
Reactivity to smoking-related cues may be an important factor that precipitates relapse in smokers who are trying to quit. The neurobiology of smoking cue reactivity has been investigated in several fMRI studies. We combined the results of these studies using activation likelihood estimation, a meta-analytic technique for fMRI data. Results of the meta-analysis indicated that smoking cues reliably evoke larger fMRI responses than neutral cues in the extended visual system, precuneus, posterior cingulate gyrus, anterior cingulate gyrus, dorsal and medial prefrontal cortex, insula, and dorsal striatum. Subtraction meta-analyses revealed that parts of the extended visual system and dorsal prefrontal cortex are more reliably responsive to smoking cues in deprived smokers than in non-deprived smokers, and that short-duration cues presented in event-related designs produce larger responses in the extended visual system than long-duration cues presented in blocked designs. The areas that were found to be responsive to smoking cues agree with theories of the neurobiology of cue reactivity, with two exceptions. First, there was a reliable cue reactivity effect in the precuneus, which is not typically considered a brain region important to addiction. Second, we found no significant effect in the nucleus accumbens, an area that plays a critical role in addiction, but this effect may have been due to technical difficulties associated with measuring fMRI data in that region. The results of this meta-analysis suggest that the extended visual system should receive more attention in future studies of smoking cue reactivity.
smoking; cue reactivity; fMRI; meta-analysis; tobacco; addiction
Guilt is a core emotion governing social behavior by promoting compliance with social norms or self-imposed standards. The goal of this study was to contrast guilty responses to actions that affect self versus others, since actions with social consequences are hypothesized to yield greater guilty feelings due to adopting the perspective and subjective emotional experience of others. Sixteen participants were presented with brief hypothetical scenarios in which the participant’s actions resulted in harmful consequences to self (guilt-self) or to others (guilt-other) during functional MRI. Participants felt more intense guilt for guilt-other than guilt-self and guilt-neutral scenarios. Guilt scenarios revealed distinct regions of activity correlated with intensity of guilt, social consequences of actions, and the interaction of guilt by social consequence. Guilt intensity was associated with activation of the dorsomedial PFC, superior frontal gyrus, supramarginal gyrus, and anterior inferior frontal gyrus. Guilt accompanied by social consequences was associated with greater activation than without social consequences in the ventromedial and dorsomedial PFC, precuneus, posterior cingulate, and posterior superior temporal sulcus. Finally, the interaction analysis highlighted select regions that were more strongly correlated with guilt intensity as a function of social consequence, including the left anterior inferior frontal gyrus, left ventromedial PFC, and left anterior inferior parietal cortex. Our results suggest these regions intensify guilt where harm to others may incur a greater social cost.
guilt; empathy; perspective taking; social emotions; functional magnetic resonance imaging
Functionally, anxiety serves to increase vigilance towards aversive stimuli and improve the ability to detect and avoid danger. We have recently shown, for instance, that anxiety increases the ability to a) detect and b) instigate defensive responses towards aversive and not appetitive face stimuli in healthy individuals. This is arguably the key adaptive function of anxiety, yet the neural circuitry underlying this valence-specific effect is unknown. In the present translational study, we sought evidence for the proposition that dorsomedial regions of the prefrontal (DMPFC) and cingulate cortex constitute the human homologue of the rodent prelimbic and are thus associated with increased amygdala responding during this adaptive threat bias in anxiety. To this end, we applied a novel functional connectivity analysis to healthy subjects (N=20) identifying the emotion of fearful and happy faces in an fMRI scanner under anxious (threat of unpredictable foot shock) and non-anxious (safe) conditions. We showed that anxiety significantly increased positive DMPFC-amygdala connectivity during the processing of fearful faces. This effect was a) valence-specific (it was not seen for happy faces), b) paralleled by faster behavioral response to fearful faces, and c) correlated positively with trait anxiety. As such we provide the first experimental support for an anxiety-mediated, valence-specific, DMPFC-amygdala aversive amplification mechanism in healthy humans. This may be homologous to the rodent prelimbic-amygdala circuit and may, given the relationship with trait anxiety, underlie vulnerability to anxiety disorders. This study thus pinpoints a key neural mechanism in adaptive anxiety and highlights its potential link to maladaptive anxiety.
amygdala; dMPFC; functional connectivity; prelimbic; anxiety; threat bias
Experience-dependent plasticity in deaf participants has been shown in a variety of studies focused on either the dorsal or ventral aspects of the visual system, but both systems have never been investigated in concert. Using functional magnetic resonance imaging (fMRI), we investigated functional plasticity for spatial processing (a dorsal visual pathway function) and for object processing (a ventral visual pathway function) concurrently, in the context of differing sensory (auditory deprivation) and language (use of a signed language) experience. During scanning, deaf native users of American Sign Language (ASL), hearing native ASL users, and hearing participants without ASL experience attended to either the spatial arrangement of frames containing objects or the identity of the objects themselves. These two tasks revealed the expected dorsal/ventral dichotomy for spatial versus object processing in all groups. In addition, the object identity matching task contained both face and house stimuli, allowing us to examine category-selectivity in the ventral pathway in all three participant groups. When contrasting the groups we found that deaf signers differed from the two hearing groups in dorsal pathway parietal regions involved in spatial cognition, suggesting sensory experience-driven plasticity. Group differences in the object processing system indicated that responses in the face-selective right lateral fusiform gyrus and anterior superior temporal cortex were sensitive to a combination of altered sensory and language experience, whereas responses in the amygdala were more closely tied to sensory experience. By selectively engaging the dorsal and ventral visual pathways within participants in groups with different sensory and language experiences, we have demonstrated that these experiences affect the function of both of these systems, and that certain changes are more closely tied to sensory experience, while others are driven by the combination of sensory and language experience.
plasticity; dorsal stream; ventral stream; spatial processing; face processing; deaf; sign language
This review and meta-analysis aims at summarizing and integrating the human neuroimaging studies that report periaqueductal gray (PAG) involvement; 250 original manuscripts on human neuroimaging of the PAG were identified. A narrative review and meta-analysis using activation likelihood estimates is included. Behaviors covered include pain and pain modulation, anxiety, bladder and bowel function and autonomic regulation. Methods include structural and functional magnetic resonance imaging, functional connectivity measures, diffusion weighted imaging and positron emission tomography. Human neuroimaging studies in healthy and clinical populations largely confirm the animal literature indicating that the PAG is involved in homeostatic regulation of salient functions such as pain, anxiety and autonomic function. Methodological concerns in the current literature, including resolution constraints, imaging artifacts and imprecise neuroanatomical labeling are discussed, and future directions are proposed. A general conclusion is that PAG neuroimaging is a field with enormous potential to translate animal data onto human behaviors, but with some growing pains that can and need to be addressed in order to add to our understanding of the neurobiology of this key region.
Meta-analysis based techniques are emerging as powerful, robust tools for developing models of connectivity in functional neuroimaging. Here, we apply meta-analytic connectivity modeling to the human caudate to 1) develop a model of functional connectivity, 2) determine if meta-analytic methods are sufficiently sensitive to detect behavioral domain specificity within region-specific functional connectivity networks, and 3) compare meta-analytic driven segmentation to structural connectivity parcellation using diffusion tensor imaging. Results demonstrate strong coherence between meta-analytic and data-driven methods. Specifically, we found that behavioral filtering resulted in cognition and emotion related structures and networks primarily localized to the head of the caudate nucleus, while perceptual and action specific regions localized to the body of the caudate, consistent with early models of nonhuman primate histological studies and postmortem studies in humans. Diffusion tensor imaging (DTI) revealed support for meta-analytic connectivity modeling's (MACM) utility in identifying both direct and indirect connectivity. Our results provide further validation of meta-analytic connectivity modeling, while also highlighting an additional potential, namely the extraction of behavioral domain specific functional connectivity.
meta-analytic connectivity modeling; functional connectivity; MACM; DTI; caudate
Fatigue caused by sustaining submaximal-intensity muscle contraction(s) involves increased activation in the brain such as primary motor cortex (M1), primary sensory cortex (S1), Premotor and supplementary motor area (PM&SMA) and prefrontal cortex (PFC). The synchronized increases in activation level in these cortical areas suggest fatigue-related strengthening of functional coupling within the motor control network. In the present study, this hypothesis was tested using the cross-correlation based functional connectivity (FC) analysis method. Ten subjects performed a 20-minute intermittent (3.5s ON/6.5s OFF, 120 trials total) handgrip task using the right hand at 50% maximal voluntary contraction (MVC) force level while their brain was scanned by a 3T Siemens Trio scanner using echo planar imaging (EPI) sequence. A representative signal time course of the left M1 was extracted by averaging the time course data of a 2-mm cluster of neighboring voxels of local maximal activation foci, which was identified by a general linear model. Two FC activation maps were created for each subject by cross-correlating the time course data of the minimal (the first 10 trials) and significant (the last 10 trials) fatigue stages across all the voxels in the brain to the corresponding representative time course. Histogram and quantile regression analysis were used to compare the FC between the minimal and significant fatigue stages and the results showed a significant increase in FC among multiple cortical regions, including right M1 and bilateral PM&SMA, S1 and PFC. This strengthened FC indicates that when muscle fatigue worsens, many brain regions increase their coupling with the left M1, the primary motor output control center for the right handgrip, to compensate for diminished force generating capability of the muscle in a coordinated fashion by enhancing the descending command for greater muscle recruitment to maintain the same force.
muscle fatigue; functional connectivity; fMRI; quantile regression; motor control network
When speech is interrupted by noise, listeners often perceptually “fill-in” the degraded signal, giving an illusion of continuity and improving intelligibility. This phenomenon involves a neural process in which the auditory cortex (AC) response to onsets and offsets of acoustic interruptions is suppressed. Since meaningful visual cues behaviorally enhance this illusory filling-in, we hypothesized that during the illusion, lip movements congruent with acoustic speech should elicit a weaker AC response to interruptions relative to static (no movements) or incongruent visual speech. AC response to interruptions was measured as the power and inter-trial phase consistency of the auditory evoked theta band (4-8 Hz) activity of the electroencephalogram (EEG) and the N1 and P2 auditory evoked potentials (AEPs). A reduction in the N1 and P2 amplitudes and in theta phase-consistency reflected the perceptual illusion at the onset and/or offset of interruptions regardless of visual condition. These results suggest that the brain engages filling-in mechanisms throughout the interruption, which repairs degraded speech lasting up to ~250 ms following the onset of the degradation. Behaviorally, participants perceived greater speech continuity over longer interruptions for congruent compared to incongruent or static audiovisual streams. However, this specific behavioral profile was not mirrored in the neural markers of interest. We conclude that lip-reading enhances illusory perception of degraded speech not by altering the quality of the AC response, but by delaying it during degradations so that longer interruptions can be tolerated.
Audiovisual integration; Auditory Evoked Potentials; EEG; Illusory filling-in; phase-locking; Theta band
Most of what is known about the reorganization of functional brain networks that accompanies normal aging is based on neuroimaging studies in which participants perform specific tasks. In these studies, reorganization is defined by the differences in task activation between young and old adults. However, task activation differences could be the result of differences in task performance, strategy, or motivation, and not necessarily reflect reorganization. Resting-state fMRI provides a method of investigating functional brain networks without such confounds. Here, a support vector machine (SVM) classifier was used in an attempt to differentiate older adults from younger adults based on their resting-state functional connectivity. In addition, the information used by the SVM was investigated to see what functional connections best differentiated younger adult brains from older adult brains. Three separate resting-state scans from 26 younger adults (18-35 yrs) and 26 older adults (55-85) were obtained from the International Consortium for Brain Mapping (ICBM) dataset made publically available in the 1000 Functional Connectomes project www.nitrc.org/projects/fcon_1000. 100 seed-regions from four functional networks with 5 mm3 radius were defined based on a recent study using machine learning classifiers on adolescent brains. Time-series for every seed-region were averaged and three matrices of z-transformed correlation coefficients were created for each subject corresponding to each individual’s three resting-state scans. SVM was then applied using leave-one-out cross-validation. The SVM classifier was 84% accurate in classifying older and younger adult brains. The majority of the connections used by the classifier to distinguish subjects by age came from seed-regions belonging to the sensorimotor and cingulo-opercular networks. These results suggest that age-related decreases in positive correlations within the cingulo-opercular and default networks, and decreases in negative correlations between the default and sensorimotor networks, are the distinguishing characteristics of age-related reorganization.
machine learning; resting-state fMRI; aging; reorganization
Working memory subsumes the capability to memorize, retrieve and utilize information for a limited period of time which is essential to many human behaviours. Moreover, impairments of working memory functions may be found in nearly all neurological and psychiatric diseases. To examine what brain regions are commonly and differently active during various working memory tasks, we performed a coordinate-based meta-analysis over 189 fMRI experiments on healthy subjects. The main effect yielded a widespread bilateral fronto-parietal network. Further meta-analyses revealed that several regions were sensitive to specific task components, e.g. Broca’s region was selectively active during verbal tasks or ventral and dorsal premotor cortex were preferentially involved in memory for object identity and location, respectively. Moreover, the lateral prefrontal cortex showed a division in a rostral and a caudal part based on differential involvement in task-set and load effects. Nevertheless, a consistent but more restricted “core” network emerged from conjunctions across analyses of specific task designs and contrasts. This “core” network appears to comprise the quintessence of regions, which are necessary during working memory tasks. It may be argued that the core regions form a distributed executive network with potentially generalized functions for focusing on competing representations in the brain. The present study demonstrates that meta-analyses are a powerful tool to integrate the data of functional imaging studies on a (broader) psychological construct, probing the consistency across various paradigms as well as the differential effects of different experimental implementations.
activation likelihood estimation; DLPFC; manipulation; memory load; short-term memory; storage
This paper describes how behavioral and imaging data can be combined with a Hidden Markov Model (HMM) to track participants’ trajectories through a complex state space. Participants completed a problem-solving variant of a memory game that involved 625 distinct states, 24 operators, and an astronomical number of paths through the state space. Three sources of information were used for classification purposes. First, an Imperfect Memory Model was used to estimate transition probabilities for the HMM. Second, behavioral data provided information about the timing of different events. Third, multivoxel pattern analysis of the imaging data was used to identify features of the operators. By combining the three sources of information, an HMM algorithm was able to efficiently identify the most probable path that participants took through the state space, achieving over 80% accuracy. These results support the approach as a general methodology for tracking mental states that occur during individual problem-solving episodes.
Functional magnetic resonance imaging; Hidden Markov Models; Multivoxel Pattern Matching; Problem Solving; Statistical Methods
Antisocial traits are common among alcoholics— particularly in certain subtypes. Although people with antisocial tendencies show atypical brain activation in some emotion and reward paradigms, how the brain reward systems of heavy drinkers (HD) are influenced by antisocial traits remains unclear. We used subjects’ preferred alcohol drink odors (AO), appetitive (ApCO) and non-appetitive (NApO) control odors in functional magnetic resonance imaging (fMRI) to determine if reward system responses varied as a function of antisocial trait density (ASD). In this retrospective analysis, we examined 30 HD who had participated in imaging twice: once while exposed to clamped intravenous alcohol infusion targeted to 50 mg%, and once during placebo saline infusion. Under placebo, there were positive correlations between ASD and blood oxygenation level dependent (BOLD) activation in the [AO > ApCO] contrast in the left dorsal putamen, while negative correlations were present in medial orbitofrontal cortex (OFC) and the bilateral amygdala. A similar pattern was observed in the correlation with the [AO > NApO] contrast. This inverse relationship between ASD and activation to alcohol odors in OFC and amygdala was specific to AO. However, negative correlations between ASD and the [ApCO > NApO] contrast were also present in the insula, putamen, and medial frontal cortex. These data suggest that frontal and limbic reward circuits of those with significant ASD are less responsive to reward cues in general, and particularly to alcohol cues in medial OFC and amygdala. These findings are broadly consistent with the reward deficiency syndrome hypothesis, although positive correlation in the striatum suggests regional variability.
alcoholism; alcohol use disorder; ethanol; personality disorder; prefrontal; orbital
Amide proton transfer (APT) MRI is sensitive to ischemic tissue acidosis and has been increasingly used as a research tool to investigate disrupted tissue metabolism during acute stroke. However, magnetization transfer asymmetry (MTRasym) analysis is often used for calculating APT contrast, which only provides pH-weighted images. In addition to pH- dependent APT contrast, in vivo MTRasym is subject to a baseline shift (ΔMTR′asym) attributable to the slightly asymmetric magnetization transfer (MT) effect. Additionally, APT contrast approximately scales with T1 relaxation time. Tissue relaxation time may also affect the experimentally obtainable APT contrast via saturation efficiency and RF spillover effects. In this study, we acquired perfusion, diffusion, relaxation and pH-weighted APT MRI data, and spectroscopy (MRS) in an animal model of acute ischemic stroke. We modeled in vivo MTRasym as a superposition of pH-dependent APT contrast and a baseline shift ΔMTR′asym (i.e., MTRasym=APTR(pH) + ΔMTR′asym), and quantified tissue pH. We found pH of the contralateral normal tissue to be 7.03 ± 0.05 and the ipsilateral ischemic tissue pH was 6.44 ± 0.24, which correlated with tissue perfusion and diffusion rates. In summary, our study established an endogenous and quantitative pH imaging technique for improved characterization of ischemic tissue acidification and metabolism disruption.
acute stroke; amide proton transfer (APT); chemical exchange saturation transfer (CEST); MRI; pH; tissue acidosis
The amplitude of the BOLD response to a stimulus is not only determined by changes in cerebral blood flow (CBF) and oxygen metabolism (CMRO2), but also by baseline physiological parameters such as haematocrit, oxygen extraction fraction (OEF) and blood volume. The calibrated BOLD approach aims to account for this physiological variation by performing an additional calibration scan. This calibration typically consists of a hypercapnia or hyperoxia respiratory challenge, although we propose that a measurement of the reversible transverse relaxation rate, R2′, might also be used. A detailed model of the BOLD effect was used to simulate each of the calibration experiments, as well as the activation experiment, whilst varying a number of physiological parameters associated with the baseline state and response to activation. The effectiveness of the different calibration methods was considered by testing whether the BOLD response to activation scaled by the calibration parameter combined with the measured CBF provides sufficient information to reliably distinguish different levels of CMRO2 response despite underlying physiological variability. In addition the effect of inaccuracies in the underlying assumptions of each technique were tested, e.g. isometabolism during hypercapnia.
The three primary findings of the study were: 1) The new calibration method based on R2′ worked reasonably well, although not as well as the ideal hypercapnia method; 2) The hyperoxia calibration method was significantly worse because baseline haematocrit and OEF must be assumed, and these physiological parameters have a significant effect on the measurements; and 3) the venous blood volume change with activation is an important confounding variable for all of the methods, with the hypercapnia method being the most robust when this is uncertain.
Calibrated BOLD; Cerebral metabolic rate of oxygen; Functional MRI; Hypercapnia; Hyperoxia
The amygdala is critically involved in detecting emotionally salient stimuli and in enhancing memory for emotional information. Growing evidence also suggests that the amygdala plays a crucial role in addiction, perhaps by strengthening associations between emotionally-charged drug cues and drug-seeking behavior. In the current study, by integrating functional MRI (fMRI), genetics, and outcome data from a large group of smokers who completed a smoking-cessation intervention and attempted to quit, we show that the amygdala also plays a role in quitting. Specifically, we demonstrate that the amygdala response to smoking-cessation messages in smokers trying to quit is a predictor of their post-intervention quitting outcome. We further show that the amygdala response is modulated by genetic variation in the serotonin transporter and mediates the impact of this genetic variation on quitting. These results point to a gene-brain-behavior pathway relevant to smoking cessation, and add to our understanding of the role of the amygdala in nicotine addiction.
Amygdala; smoking cessation; serotonin transporter gene; fMRI; imaging genetics
There are strong correlations between cortical atrophy observed by MRI and clinical disability and disease duration in multiple sclerosis (MS). The objective of this study was to evaluate the progression of cortical atrophy over time in vivo in experimental autoimmune encephalomyelitis (EAE), the most commonly used animal model for MS. Volumetric changes in brains of EAE mice and matched healthy controls were quantified by collecting high-resolution T2-weighted magnetic resonance images in vivo and labeling anatomical structures on the images. In vivo scanning permitted us to evaluate brain structure volumes in individual animals over time and we observed that though brain atrophy progressed differently in each individual animal, all mice with EAE demonstrated significant atrophy in whole brain, cerebral cortex, and whole cerebellum compared to normal controls. Furthermore, we found a strong correlation between cerebellar atrophy and cumulative disease score in mice with EAE. Ex vivo MRI showed a significant decrease in brain and cerebellar volume and a trend that did not reach significance in cerebral cortex volume in mice with EAE compared to controls. Cross modality correlations revealed a significant association between neuronal loss on neuropathology and in vivo atrophy of the cerebral cortex by neuroimaging. These results demonstrate that longitudinal in vivo imaging is more sensitive to changes that occur in neurodegenerative disease models than cross-sectional ex vivo imaging. This is the first report of progressive cortical atrophy in vivo in a mouse model of MS.
Imaging biomarkers for Alzheimer’s disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer’s Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer’s disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer’s disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer’s disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials.
Alzheimer’s disease; mild cognitive impairment; classification; longitudinal analysis; [18F]fluorodeoxyglucose positron emission tomography; image segmentation
In a recent study we found that multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data could predict which of several touch-implying video clips a subject saw, only using voxels from primary somatosensory cortex. Here, we re-analyzed the same dataset using cross-individual MVPA to locate patterns of information that were common across participants’ brains. In this procedure a classifier learned to distinguish the neural patterns evoked by each stimulus based on the data from a sub-group of the subjects and was then tested on data from an individual that was not part of that sub-group. We found prediction performance to be significantly above chance both when using voxels from the whole brain and when only using voxels from the postcentral gyrus. SVM voxel weight maps established based on the whole-brain analysis as well as a separate searchlight analysis suggested foci of especially high information content in medial and lateral occipital cortex and around the intraparietal sulcus. Classification across individuals appeared to rely on similar brain areas as classification within individuals. These data show that observing touch leads to stimulus-specific patterns of activity in sensorimotor networks and that these patterns are similar across individuals. More generally, the results suggest that cross-individual MVPA can succeed even when applied to restricted regions of interest.
fMRI; touch observation; MVPA; pattern analysis; multisensory; perception
Conventional functional magnetic resonance imaging (FMRI) group analysis makes two key assumptions that are not always justified. First, the data from each subject is condensed into a single number per voxel, under the assumption that within-subject variance for the effect of interest is the same across all subjects or is negligible relative to the cross-subject variance. Second, it is assumed that all data values are drawn from the same Gaussian distribution with no outliers. We propose an approach that does not make such strong assumptions, and present a computationally efficient frequentist approach to FMRI group analysis, which we term mixed-effects multilevel analysis (MEMA), that incorporates both the variability across subjects and the precision estimate of each effect of interest from individual subject analyses. On average, the more accurate tests result in higher statistical power, especially when conventional variance assumptions do not hold, or in the presence of outliers. In addition, various heterogeneity measures are available with MEMA that may assist the investigator in further improving the modeling. Our method allows group effect t-tests and comparisons among conditions and among groups. In addition, it has the capability to incorporate subject-specific covariates such as age, IQ, or behavioral data. Simulations were performed to illustrate power comparisons and the capability of controlling type I errors among various significance testing methods, and the results indicated that the testing statistic we adopted struck a good balance between power gain and type I error control. Our approach is instantiated in an open-source, freely distributed program that may be used on any dataset stored in the universal neuroimaging file transfer (NIfTI) format. To date, the main impediment for more accurate testing that incorporates both within- and cross-subject variability has been the high computational cost. Our efficient implementation makes this approach practical. We recommend its use in lieu of the less accurate approach in the conventional group analysis.
FMRI group analysis; Effect estimate precision or reliability; Mixed-effects multilevel analysis (MEMA); Weighted least squares (WLS); Restricted maximum likelihood (REML); Outliers; AFNI
The overall goal of this research is the design of statistical atlas models that can be created from normal subjects, but may generalize to be applicable to abnormal brains. We present a new style of joint modeling of fMRI, DTI, and structural MRI. Motivated by the fact that a white matter tract and related cortical areas are likely to displace together in the presence of a mass lesion (brain tumor), in this work we propose a rotation and translation invariant model that represents the spatial relationship between fiber tracts and anatomic and functional landmarks. This landmark distance model provides a new basis for representation of fiber tracts and can be used for detection and prediction of fiber tracts based on landmarks. Our results indicate that the measured model is consistent across normal subjects, and thus suitable for atlas building. Our experiments demonstrate that the model is robust to displacement and missing data, and can be successfully applied to a small group of patients with mass lesions.
Diffusion MRI; Functional MRI; Atlas; White matter; Neuroimaging; Structure-function
Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimer’s disease (AD). Using a sample from the Alzheimer’s Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data re-sampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD.
Catechol-O-methyltransferase (COMT) modulates dopamine in the prefrontal cortex (PFC) and influences PFC dopamine-dependent cognitive task performance. A human COMT polymorphism (Val158Met) alters enzyme activity and is associated with both the activation and functional connectivity of the PFC during task performance, particularly working memory. Here, we used functional magnetic resonance imaging and a data-driven, independent components analysis (ICA) approach to compare resting state functional connectivity within the executive control network (ECN) between young, male COMT Val158 (n = 27) and Met158 (n = 28) homozygotes. COMT genotype effects on grey matter were assessed using voxel-based morphometry. COMT genotype significantly modulated functional connectivity within the ECN, which included the head of the caudate, and anterior cingulate and frontal cortical regions. Val158 homozygotes showed greater functional connectivity between a cluster within the left ventrolateral PFC and the rest of the ECN (using a threshold of Z > 2.3 and a family-wise error cluster significance level of p < 0.05). This difference occurred in the absence of any alterations in grey matter. Our data show that COMT Val158Met affects the functional connectivity of the PFC at rest, complementing its prominent role in the activation and functional connectivity of this region during cognitive task performance. The results suggest that genotype-related differences in prefrontal dopaminergic tone result in neuroadaptive changes in basal functional connectivity, potentially including subtle COMT genotype-dependent differences in the relative coupling of task-positive and task-negative regions, which could in turn contribute to its effects on brain activation, connectivity, and behaviour.
► We studied the impact of COMT Val158Met genotype on resting state connectivity. ► We compared resting state functional connectivity in Val/Val vs. Met/Met men. ► We focussed on the predominantly prefrontal (PFC) executive control network (ECN). ► The ECN was identified using a group ICA approach. ► We found greater resting PFC functional connectivity in Val/Val vs. Met/Met men.
Resting state network; Dopamine; Working memory; Prefrontal cortex; Polymorphism; fMRI
In this work, we address the problem of using dynamic causal modelling (DCM) to estimate the coupling parameters (effective connectivity) of large models with many regions. This is a potentially important problem because meaningful graph theoretic analyses of effective connectivity rest upon the statistics of the connections (edges). This calls for characterisations of networks with an appreciable number of regions (nodes). The problem here is that the number of coupling parameters grows quadratically with the number of nodes—leading to severe conditional dependencies among their estimates and a computational load that quickly becomes unsustainable. Here, we describe a simple solution, in which we use functional connectivity to provide prior constraints that bound the effective number of free parameters. In brief, we assume that priors over connections between individual nodes can be replaced by priors over connections between modes (patterns over nodes). By using a small number of modes, we can reduce the dimensionality of the problem in an informed way. The modes we use are the principal components or eigenvectors of the functional connectivity matrix. However, this approach begs the question of how many modes to use. This question can be addressed using Bayesian model comparison to optimise the number of modes. We imagine that this form of prior – over the extrinsic (endogenous) connections in large DCMs – may be useful for people interested in applying graph theory to distributed networks in the brain or to characterise connectivity beyond the subgraphs normally examined in DCM.
► Here we investigate the possibility to invert large DCMs with many regions. ► To do that, we place constraints on priors to bind the number of free parameters. ► Constraints are provided by the principal modes of the functional connectivity. ► Bayesian model comparison is used to identify the optimal number of modes. ► The ability to invert large DCMs provides a new opportunity for graph theory users.
Effective connectivity; Functional MRI; Dynamic causal modelling; Connectivity; Graph theory; Bayesian