Whole-brain fMRI Research Articles

Meta-Analysis of fMRI Studies of Disruptive Behavior Disorders.

Functional magnetic resonance imaging (fMRI) studies in conduct disorder and in oppositional defiant disorder have shown inconsistencies. The aim of this meta-analysis of fMRI studies in disruptive behavior disorders was to establish the most consistent brain dysfunctions and to address task- and subtype-related heterogeneity. Web-based publication databases were searched to conduct a meta-analysis of all whole-brain fMRI studies of youths with disruptive behavior disorder or conduct problems up to August 2015. Sub-meta-analyses were conducted in functional subdomains of emotion processing; in cool and hot executive functions, which refer to goal-directed higher cognitive functions with and without motivational and affective significance; and in a subgroup of youths with additional psychopathic traits. The authors performed a meta-analysis of voxel-based group differences in functional activation using the anisotropic effect-size version of seed-based d mapping. Across 24 studies, 338 youths with disruptive behavior disorder or conduct problems relative to 298 typically developing youths had consistent underactivation in the rostral and dorsal anterior cingulate and in the medial prefrontal cortex and ventral caudate. Sub-meta-analyses of fMRI studies showed that medial fronto-cingulate dysfunction was driven by hot executive function. The sub-meta-analysis of emotion processing fMRI studies showed the most consistent underactivation in the dorsolateral prefrontal cortex and temporal pole, while cool executive functions were associated with temporal abnormalities. Youths with disruptive behavior disorder with psychopathic traits showed reduced ventromedial prefrontal-hypothalamic-limbic activation, but they also showed hyperactivation in cognitive control mediating dorsolateral prefrontal-dorsal and striatal regions. The findings show that the most consistent dysfunction in youths with disruptive behavior disorder is in the rostro-dorsomedial, fronto-cingulate, and ventral-striatal regions that mediate reward-based decision making, which is typically compromised in the disorder. Youths with psychopathic traits, on the other hand, have dysfunctions associated with the ventromedial prefrontal cortex and limbic system, together with dorsal and fronto-striatal hyperfunctioning, which may reflect poor affect reactivity and empathy in the presence of hyperactive executive control. These findings provide potential targets for neurotherapeutic and pharmacological interventions.

Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex.

Various studies in the brain mapping field have demonstrated that there exist multiple concurrent functional networks that are spatially overlapped and interacting with each other during specific task performance to jointly realize the total brain function. Assessing such spatial overlap patterns of functional networks (SOPFNs) based on functional magnetic resonance imaging (fMRI) has thus received increasing interest for brain function studies. However, there are still two crucial issues to be addressed. First, the SOPFNs are assessed over the entire fMRI scan assuming the temporal stationarity, while possibly time-dependent dynamics of the SOPFNs is not sufficiently explored. Second, the SOPFNs are assessed within individual subjects, while group-wise consistency of the SOPFNs is largely unknown. To address the two issues, we propose a novel computational framework of group-wise sparse representation of whole-brain fMRI temporal segments to assess the temporal dynamic spatial patterns of SOPFNs that are consistent across different subjects. Experimental results based on the recently publicly released Human Connectome Project grayordinate task fMRI data demonstrate that meaningful SOPFNs exhibiting dynamic spatial patterns across different time periods are effectively and robustly identified based on the reconstructed concurrent functional networks via the proposed framework. Specifically, those SOPFNs locate significantly more on gyral regions than on sulcal regions across different time periods. These results reveal novel functional architecture of cortical gyri and sulci. Moreover, these results help better understand functional dynamics mechanisms of cerebral cortex in the future.

Neural Activation during Anticipation of Near Pain-Threshold Stimulation among the Pain-Fearful.

Fear of pain (FOP) can increase risk for chronic pain and disability but little is known about corresponding neural responses in anticipation of potential pain. In this study, more (10 women, 6 men) and less (7 women, 6 men) pain-fearful groups underwent whole-brain functional magnetic resonance imaging (fMRI) during anticipation of near pain-threshold stimulation. Groups did not differ in the proportion of stimuli judged to be painful but pain-fearful participants reported significantly more state fear prior to stimulus exposure. Within the entire sample, stronger activation was found in several pain perception regions (e.g., bilateral insula, midcingulate cortex (MCC), thalamus, superior frontal gyrus) and visual areas linked to decoding stimulus valences (inferior orbital cortex) during anticipation of “painful” stimuli. Between groups and correlation analyses indicated pain-fearful participants experienced comparatively more activity in regions implicated in evaluating potential threats and processing negative emotions during anticipation (i.e., MCC, mid occipital cortex, superior temporal pole), though group differences were not apparent in most so-called “pain matrix” regions. In sum, trait- and task-based FOP is associated with enhanced responsiveness in regions involved in threat processing and negative affect during anticipation of potentially painful stimulation.

Differential roles for parietal and frontal cortices in fixed versus evolving temporal expectations: Dissociating prior from posterior temporal probabilities with fMRI

The ability to predict when an event will occur allows us to respond optimally to that event. Temporal predictability can be either fixed (prior probability) or evolving (posterior probability), in which case it is dynamically updated as a function of the elapse of time itself (“hazard function”). We used fMRI to identify the brain regions involved in either form of temporal prediction, within a single experimental paradigm. Participants performed a cued reaction time (RT) task, in which the target appeared after one of four intervals (“foreperiods”) that was either predictable (temporal condition) or variable (neutral condition). As expected, RTs were faster in temporal versus neutral conditions, indicating the behavioural benefit of fixed temporal predictability. RTs also got faster as a function of foreperiod in the neutral, but not temporal, condition, reflecting the evolving temporal predictability of the hazard function. We confirmed that left inferior parietal cortex was preferentially activated by the fixed temporal predictability of temporal (versus neutral) cues. Then, by directly comparing how activity varied as a function of foreperiod in the neutral versus time conditions, we identified the neural substrates of the changes in temporal probability defined by the hazard function, while simultaneously controlling for changes related simply to the elapse of time itself. Whole-brain fMRI analyses (independently confirmed by anatomically guided ROI analyses) showed that activity in left inferior parietal cortex tracked the evolving temporal probabilities of the hazard function. ROI analysis further revealed a similar role for right inferior frontal cortex. Our data highlight a key role for left parietal cortex in instantiating the behavioural benefits of temporal predictability, whether predictions are fixed or dynamically evolving.

Tuning the Brake While Raising the Stake: Network Dynamics during Sequential Decision-Making.

When gathering valued goods, risk and reward are often coupled and escalate over time, for instance, during foraging, trading, or gambling. This escalating frame requires agents to continuously balance expectations of reward against those of risk. To address how the human brain dynamically computes these tradeoffs, we performed whole-brain fMRI while healthy young individuals engaged in a sequential gambling task. Participants were repeatedly confronted with the option to continue with throwing a die to accumulate monetary reward under escalating risk, or the alternative option to stop to bank the current balance. Within each gambling round, the accumulation of gains gradually increased reaction times for "continue" choices, indicating growing uncertainty in the decision to continue. Neural activity evoked by "continue" choices was associated with growing activity and connectivity of a cortico-subcortical "braking" network that positively scaled with the accumulated gains, including pre-supplementary motor area (pre-SMA), inferior frontal gyrus, caudate, and subthalamic nucleus (STN). The influence of the STN on continue-evoked activity in the pre-SMA was predicted by interindividual differences in risk-aversion attitudes expressed during the gambling task. Furthermore, activity in dorsal anterior cingulate cortex (ACC) reflected individual choice tendencies by showing increased activation when subjects made nondefault "continue" choices despite an increasing tendency to stop, but ACC activity did not change in proportion with subjective choice uncertainty. Together, the results implicate a key role of dorsal ACC, pre-SMA, inferior frontal gyrus, and STN in computing the trade-off between escalating reward and risk in sequential decision-making. Using a paradigm where subjects experienced increasing potential rewards coupled with increasing risk, this study addressed two unresolved questions in the field of decision-making: First, we investigated an "inhibitory" network of regions that has so far been investigated with externally cued action inhibition. In this study, we show that the dynamics in this network under increasingly risky decisions are predictive of subjects' risk attitudes. Second, we contribute to a currently ongoing debate about the anterior cingulate cortex's role in sequential foraging decisions by showing that its activity is related to making nondefault choices rather than to choice uncertainty.

Prediction of individual clinical scores in patients with Parkinson's disease using resting-state functional magnetic resonance imaging

Neuroimaging holds the promise that it may one day aid the clinical assessment. However, the vast majority of studies using resting-state functional magnetic resonance imaging (fMRI) have reported average differences between Parkinson's disease (PD) patients and healthy controls, which do not permit inferences at the level of individuals. This study was to develop a model for the prediction of PD illness severity ratings from individual fMRI brain scan. The resting-state fMRI scans were obtained from 84 patients with PD and the Unified Parkinson's Disease Rating Scale-III (UPDRS-III) scores were obtained before scanning. The RVR method was used to predict clinical scores (UPDRS-III) from fMRI scans. The application of RVR to whole-brain resting-state fMRI data allowed prediction of UPDRS-III scores with statistically significant accuracy (correlation=0.35, P-value=0.001; mean sum of squares=222.17, P-value=0.002). This prediction was informed strongly by negative weight areas including prefrontal lobe and medial occipital lobe, and positive weight areas including medial parietal lobe. It was suggested that fMRI scans contained sufficient information about neurobiological change in patients with PD to permit accurate prediction about illness severity, on an individual subject basis. Our results provided preliminary evidence, as proof-of-concept, to support that fMRI might be possible to be a clinically useful quantitative assessment aid in PD at individual level. This may enable clinicians to target those uncooperative patients and machines to replace human for a more efficient use of health care resources.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation.

Resting-state abnormalities in amnestic mild cognitive impairment: a meta-analysis.

Amnestic mild cognitive impairment (aMCI) is a prodromal stage of Alzheimer's disease (AD). As no effective drug can cure AD, early diagnosis and intervention for aMCI are urgently needed. The standard diagnostic procedure for aMCI primarily relies on subjective neuropsychological examinations that require the judgment of experienced clinicians. The development of other objective and reliable aMCI markers, such as neural markers, is therefore required. Previous neuroimaging findings revealed various abnormalities in resting-state activity in MCI patients, but the findings have been inconsistent. The current study provides an updated activation likelihood estimation meta-analysis of resting-state functional magnetic resonance imaging (fMRI) data on aMCI. The authors searched on the MEDLINE/PubMed databases for whole-brain resting-state fMRI studies on aMCI published until March 2015. We included 21 whole-brain resting-state fMRI studies that reported a total of 156 distinct foci. Significant regional resting-state differences were consistently found in aMCI patients relative to controls, including the posterior cingulate cortex, right angular gyrus, right parahippocampal gyrus, left fusiform gyrus, left supramarginal gyrus and bilateral middle temporal gyri. Our findings support that abnormalities in resting-state activities of these regions may serve as neuroimaging markers for aMCI.

Oxytocin blurs the self-other distinction during trait judgments and reduces medial prefrontal cortex responses.

The neuropeptide oxytocin (OXT) may act either to increase or blur the distinction between self and other and thereby promote either more selfish or altruistic behaviors. To attempt to distinguish between these two possibilities we performed a double-blind, between-subject, placebo-controlled design study to investigate the effect of intranasal OXT on self and other (mother, classmate, or stranger) trait judgments in conjunction with functional magnetic resonance imaging. Results showed that OXT reduced response times for making both self and other judgments, but also reduced the accuracy of their subsequent recall, thereby abolishing the normal self-bias observed in this task. OXT also abolished the positive correlation between response and self-esteem scale scores seen in the PLC group, suggesting that its effects were strongest in individuals with higher levels of self-esteem. A whole-brain functional magnetic resonance imaging analysis revealed that OXT also reduced responses during both self and other trait judgments in the dorsal (dmPFC) and ventral (vmPFC) medial prefrontal cortex. A subsequent region of interest analysis revealed that behavioral performance and self-esteem scale scores were associated with dmPFC activation and its functional connectivity with the anterior cingulate and between the vmPFC and posterior cingulate. Thus overall, while OXT may improve speed of decision making in self -vs. other trait judgments it also blunts the normal bias towards remembering self-attributes and reduces mPFC responses and connectivity with other cortical midline regions involved in self-processing. This is consistent with the view that OXT can reduce self-centered behavior. Hum Brain Mapp 37:2512-2527, 2016. © 2016 Wiley Periodicals, Inc.

Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions

State-space multivariate dynamical systems (MDS) (Ryali et al. 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods are poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort, optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in fMRI. More generally, our study demonstrates that the combined use of optogenetics and fMRI provides a powerful new tool for evaluating computational methods designed to estimate causal interactions between distributed brain regions.

Bayesian Community Detection in the Space of Group-Level Functional Differences.

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

Altered Striatal Response During Effort-Based Valuation and Motivation in Alcohol-Dependent Individuals.

To use functional magnetic resonance imaging (fMRI) to investigate the neural circuitry behind effort-related valuation and motivation in a population of alcohol-dependent participants and healthy controls. Seventeen alcohol-dependent participants and a comparison group of 17 healthy control participants completed an effort-based motivation paradigm during an fMRI scan, in which they were required to exert effort at varying levels in order to earn a monetary reward. We found that alcohol-dependent participants were less motivated during trials requiring high levels of effort. The whole-brain fMRI analysis revealed that alcohol-dependent participants displayed an increased blood-oxygen-level dependent (BOLD) signal during low and unknown effort cues in the dorsal and ventral striatum compared with healthy controls. These findings provide the first evidence that alcohol-dependent participants and healthy controls differ in their effort-based valuation and motivation processing. Alcohol-dependent participants displayed a hyperactive mesolimbic reward circuitry recruited by non-drug rewards, potentially reflecting a sensitization to reward in this patient population.

ID 312 – Sensorimotor modulation by botulinum Toxin A in post-stroke arm spasticity: Passive hand movement

Objective The aim of the study was to locate brain activation changes in stroke patients treated with Botulinum Toxin A (BoNT) for arm spasticity using functional MRI (fMRI). Methods Seven ischemic stroke patients (4 females, mean age 58.9) with severe spastic hand paralysis were studied. Spasticity was scored according to the modified Ashworth scale (MAS). FMRI was performed before (W0), 4 (W4) and 11 weeks (W11) after BoNT. Whole-brain fMRI data (1.5 T) were acquired during paced repetitive passive flexion/extension at the wrist of plegic limb alternating with rest. General Linear Model analysis used corrected cluster-wise p 0.05. Results BoNT transiently lowered MAS in W4. In all sessions, fMRI activation of the ipsilesional sensorimotor cortex (contralateral to the moved hand) dominated. At W4, bilateral cerebellum transiently engaged. Paired contrasts demonstrated significant differences W4 > W0 (bilateral cerebellum, occipital and prefrontal cortex) and W4 > W11 (ipsilesional cerebellum, contralesional thalamus, bilateral premotor and posterior cingulate cortex). Conclusions Whole-brain activation patterns during BoNT treatment of post-stroke arm spasticity demonstrated changes both within and outside the classical sensorimotor system. Specific BoNT-related changes were revealed in the cerebellum. Supported by IGA Ministry of Health Czech Republic Grant NT13575 .

Sensorimotor modulation by botulinum toxin A in post-stroke arm spasticity: Passive hand movement

IntroductionIn post-stroke spasticity, functional imaging may uncover modulation in the central sensorimotor networks associated with botulinum toxin type A (BoNT) therapy. Investigations were performed to localize brain activation changes in stroke patients treated with BoNT for upper limb spasticity using functional magnetic resonance imaging (fMRI). MethodsSeven ischemic stroke patients (4 females; mean age 58.86) with severe hand paralysis and notable spasticity were studied. Spasticity was scored according to the modified Ashworth scale (MAS). fMRI examination was performed 3 times: before (W0) and 4 (W4) and 11weeks (W11) after BoNT. The whole-brain fMRI data were acquired during paced repetitive passive movements of the plegic hand (flexion/extension at the wrist) alternating with rest. Voxel-by-voxel statistical analysis using the General Linear Model (GLM) implemented in FSL (v6.00)/FEAT yielded group session-wise statistical maps and paired between-session contrasts, thresholded at the corrected cluster-wise significance level of p<0.05. ResultsAs expected, BoNT transiently lowered MAS scores at W4. Across all the sessions, fMRI activation of the ipsilesional sensorimotor cortex (M1, S1, and SMA) dominated. At W4, additional clusters transiently emerged bilaterally in the cerebellum, in the contralesional sensorimotor cortex, and in the contralesional occipital cortex. Paired contrasts demonstrated significant differences W4>W0 (bilateral cerebellum and contralesional occipital cortex) and W4>W11 (ipsilesional cerebellum and SMA). The remaining paired contrast (W0>W11) showed activation decreases mainly in the ipsilesional sensorimotor cortex (M1, S1, and SMA). ConclusionsThe present study confirms the feasibility of using passive hand movements to map the cerebral sensorimotor networks in patients with post-stroke arm spasticity and demonstrates that BoNT-induced spasticity relief is associated with changes in task-induced central sensorimotor activation, likely mediated by an altered afferent drive from the spasticity-affected muscles.

Age of acquisition of the second language modulates structural and functional dynamics of bilingual reading

Age of acquisition of the second language modulates structural and functional dynamics of bilingual reading

HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).

Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI ‘big data.’ Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big data is much needed. To address this challenge, in this work, we introduce our newly developed informatics platform, namely, ‘HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).’ HELPNI implements our recently developed computational framework of sparse representation of whole-brain fMRI signals which is called holistic atlases of functional networks and interactions (HAFNI) for fMRI data analysis. HELPNI provides integrated solutions to archive and process large-scale fMRI data automatically and structurally, to extract and visualize meaningful results information from raw fMRI data, and to share open-access processed and raw data with other collaborators through web. We tested the proposed HELPNI platform using publicly available 1000 Functional Connectomes dataset including over 1200 subjects. We identified consistent and meaningful functional brain networks across individuals and populations based on resting state fMRI (rsfMRI) big data. Using efficient sampling module, the experimental results demonstrate that our HELPNI system has superior performance than other systems for large-scale fMRI data in terms of processing and storing the data and associated results much faster.

Real-time fMRI processing with physiological noise correction – Comparison with off-line analysis

BackgroundWhile applications of real-time functional magnetic resonance imaging (rtfMRI) are growing rapidly, there are still limitations in real-time data processing compared to off-line analysis. New methodsWe developed a proof-of-concept real-time fMRI processing (rtfMRIp) system utilizing a personal computer (PC) with a dedicated graphic processing unit (GPU) to demonstrate that it is now possible to perform intensive whole-brain fMRI data processing in real-time. The rtfMRIp performs slice-timing correction, motion correction, spatial smoothing, signal scaling, and general linear model (GLM) analysis with multiple noise regressors including physiological noise modeled with cardiac (RETROICOR) and respiration volume per time (RVT). ResultsThe whole-brain data analysis with more than 100,000voxels and more than 250volumes is completed in less than 300ms, much faster than the time required to acquire the fMRI volume. Real-time processing implementation cannot be identical to off-line analysis when time-course information is used, such as in slice-timing correction, signal scaling, and GLM. We verified that reduced slice-timing correction for real-time analysis had comparable output with off-line analysis. The real-time GLM analysis, however, showed over-fitting when the number of sampled volumes was small. Comparison with existing methodsOur system implemented real-time RETROICOR and RVT physiological noise corrections for the first time and it is capable of processing these steps on all available data at a given time, without need for recursive algorithms. ConclusionsComprehensive data processing in rtfMRI is possible with a PC, while the number of samples should be considered in real-time GLM.

A deconvolution-based approach to identifying large-scale effective connectivity

Rapid, robust computation of effective connectivity between neural regions is an important next step in characterizing the brain’s organization, particularly in the resting state. However, recent work has called into question the value of causal inference computed directly from BOLD, demonstrating that valid inferences require transformation of the BOLD signal into its underlying neural events as necessary for accurate causal inference. In this work we develop an approach for effective connectivity estimation directly from deconvolution-based features and estimates of inter-regional communication lag. We then test, in both simulation as well as whole-brain fMRI BOLD signal, the viability of this approach. Our results show that deconvolution precision and network size play outsized roles in effective connectivity estimation performance. Idealized simulation conditions allow for statistically significant effective connectivity estimation of networks of up to four hundred regions-of-interest (ROIs). Under simulation of realistic recording conditions and deconvolution performance, however, our result indicates that effective connectivity is viable in networks containing up to approximately sixty ROIs. We then validated the ability for the proposed method to reliably detect effective connectivity in whole-brain fMRI signal parcellated into networks of viable size.

Three-dimensional echo planar imaging with controlled aliasing: A sequence for high temporal resolution functional MRI.

In this work, we combine three-dimensional echo planar imaging (3D-EPI) with controlled aliasing to substantially increase temporal resolution in whole-brain functional MRI (fMRI) while minimizing geometry-factor (g-factor) losses. The study was performed on a 7 Tesla scanner equipped with a 32-channel receive coil. The proposed 3D-EPI-CAIPI sequence was evaluated for: (i) image quality, compared with a conventionally undersampled parallel imaging acquisition; (ii) temporal resolution, the ability to sample and remove physiological signal fluctuations from the fMRI signal of interest and (iii) the ability to distinguish small changes in hemodynamic responses in an event-related fMRI experiment. Whole-brain fMRI data with a voxel size of 2 × 2 × 2 mm(3) and temporal resolution of 371 ms could be acquired with acceptable image quality. Ten-fold parallel imaging accelerated 3D-EPI-CAIPI data were shown to lower the maximum g-factor losses up to 62% with respect to a 10-fold accelerated 3D-EPI dataset. FMRI with 400 ms temporal resolution allowed the detection of time-to-peak variations in functional responses due to multisensory facilitation in temporal, occipital and frontal cortices. 3D-EPI-CAIPI allows increased temporal resolution that enables better characterization of physiological noise, thus improving sensitivity to signal changes of interest. Furthermore, subtle changes in hemodynamic response dynamics can be studied in shorter scan times by avoiding the need for jittering. Magn Reson Med 75:2350-2361, 2016. © 2015 Wiley Periodicals, Inc.

Monaural and binaural contributions to interaural-level-difference sensitivity in human auditory cortex

Whole-brain functional magnetic resonance imaging was used to measure blood-oxygenation-level-dependent (BOLD) responses in human auditory cortex (AC) to sounds with intensity varying independently in the left and right ears. Echoplanar images were acquired at 3 Tesla with sparse image acquisition once per 12-second block of sound stimulation. Combinations of binaural intensity and stimulus presentation rate were varied between blocks, and selected to allow measurement of response-intensity functions in three configurations: monaural 55–85 dB SPL, binaural 55–85 dB SPL with intensity equal in both ears, and binaural with average binaural level of 70 dB SPL and interaural level differences (ILD) ranging ±30 dB (i.e., favoring the left or right ear). Comparison of response functions equated for contralateral intensity revealed that BOLD-response magnitudes (1) generally increased with contralateral intensity, consistent with positive drive of the BOLD response by the contralateral ear, (2) were larger for contralateral monaural stimulation than for binaural stimulation, consistent with negative effects (e.g., inhibition) of ipsilateral input, which were strongest in the left hemisphere, and (3) also increased with ipsilateral intensity when contralateral input was weak, consistent with additional, positive, effects of ipsilateral stimulation. Hemispheric asymmetries in the spatial extent and overall magnitude of BOLD responses were generally consistent with previous studies demonstrating greater bilaterality of responses in the right hemisphere and stricter contralaterality in the left hemisphere. Finally, comparison of responses to fast (40/s) and slow (5/s) stimulus presentation rates revealed significant rate-dependent adaptation of the BOLD response that varied across ILD values.