Brain-wide functional connectivity artifactually inflates throughout functional magnetic resonance imaging scans.

Functional magnetic resonance imaging (fMRI) is a central tool for investigating human brain function, organization and disease. Here, we show that fMRI-based estimates of functional brain connectivity artifactually inflate at spatially heterogeneous rates during resting-state and task-based scans. This produces false positive connection strength changes and spatial distortion of brain connectivity maps. We demonstrate that this artefact is driven by temporal inflation of the non-neuronal, systemic low-frequency oscillation (sLFO) blood flow signal during fMRI scanning and is not addressed by standard denoising procedures. We provide evidence that sLFO inflation reflects perturbations in cerebral blood flow by respiration and heart rate changes that accompany diminishing arousal during scanning, although the mechanisms of this pathway are uncertain. Finally, we show that adding a specialized sLFO denoising procedure to fMRI processing pipelines mitigates the artifactual inflation of functional connectivity, enhancing the validity and within-scan reliability of fMRI findings.

Prefrontal-Limbic Circuitry Is Associated With Reward Sensitivity in Nonhuman Primates.

BACKGROUND: Abnormal reward sensitivity is a risk factor for psychiatric disorders, including eating disorders such as overeating and binge-eating disorder, but the brain structural mechanisms that underlie it are not completely understood. Here, we sought to investigate the relationship between multimodal whole-brain structural features and reward sensitivity in nonhuman primates. METHODS: Reward sensitivity was evaluated through behavioral economic analysis in which monkeys (adult rhesus macaques; 7 female, 5 male) responded for sweetened condensed milk (10%, 30%, 56%), Gatorade, or water using an operant procedure in which the response requirement increased incrementally across sessions (i.e., fixed ratio 1, 3, 10). Animals were divided into high (n = 6) or low (n = 6) reward sensitivity groups based on essential value for 30% milk. Multimodal magnetic resonance imaging was used to measure gray matter volume and white matter microstructure. Brain structural features were compared between groups, and their correlations with reward sensitivity for various stimuli was investigated. RESULTS: Animals in the high sensitivity group had greater dorsolateral prefrontal cortex, centromedial amygdaloid complex, and middle cingulate cortex volumes than animals in the low sensitivity group. Furthermore, compared with monkeys in the low sensitivity group, high sensitivity monkeys had lower fractional anisotropy in the left dorsal cingulate bundle connecting the centromedial amygdaloid complex and middle cingulate cortex to the dorsolateral prefrontal cortex, and in the left superior longitudinal fasciculus 1 connecting the middle cingulate cortex to the dorsolateral prefrontal cortex. CONCLUSIONS: These results suggest that neuroanatomical variation in prefrontal-limbic circuitry is associated with reward sensitivity. These brain structural features may serve as predictive biomarkers for vulnerability to food-based and other reward-related disorders.

Data-driven connectivity profiles relate to smoking cessation outcomes.

At a group level, nicotine dependence is linked to differences in resting-state functional connectivity (rs-FC) within and between three large-scale brain networks: the salience network (SN), default mode network (DMN), and frontoparietal network (FPN). Yet, individuals may display distinct patterns of rs-FC that impact treatment outcomes. This study used a data-driven approach, Group Iterative Multiple Model Estimation (GIMME), to characterize shared and person-specific rs-FC features linked with clinically-relevant treatment outcomes. 49 nicotine-dependent adults completed a resting-state fMRI scan prior to a two-week smoking cessation attempt. We used GIMME to identify group, subgroup, and individual-level networks of SN, DMN, and FPN connectivity. Regression models assessed whether within- and between-network connectivity of individual rs-FC models was associated with baseline cue-induced craving, and craving and use of regular cigarettes (i.e., "slips") during cessation. As a group, participants displayed shared patterns of connectivity within all three networks, and connectivity between the SN-FPN and DMN-SN. However, there was substantial heterogeneity across individuals. Individuals with greater within-network SN connectivity experienced more slips during treatment, while individuals with greater DMN-FPN connectivity experienced fewer slips. Individuals with more anticorrelated DMN-SN connectivity reported lower craving during treatment, while SN-FPN connectivity was linked to higher craving. In conclusion, in nicotine-dependent adults, GIMME identified substantial heterogeneity within and between the large-scale brain networks. Individuals with greater SN connectivity may be at increased risk for relapse during treatment, while a greater positive DMN-FPN and negative DMN-SN connectivity may be protective for individuals during smoking cessation treatment.

Brain-wide functional connectivity artifactually inflates throughout fMRI scans: a problem and solution.

The fMRI blood oxygen level-dependent (BOLD) signal is a mainstay of neuroimaging assessment of neuronal activity and functional connectivity in vivo. Thus, a chief priority is maximizing this signal's reliability and validity. To this end, the fMRI community has invested considerable effort into optimizing both experimental designs and physiological denoising procedures to improve the accuracy, across-scan reproducibility, and subject discriminability of BOLD-derived metrics like functional connectivity. Despite these advances, we discover that a substantial and ubiquitous defect remains in fMRI datasets: functional connectivity throughout the brain artifactually inflates during the course of fMRI scans - by an average of more than 70% in 15 minutes of scan time - at spatially heterogeneous rates, producing both spatial and temporal distortion of brain connectivity maps. We provide evidence that this inflation is driven by a previously unrecognized time-dependent increase of non-neuronal, systemic low-frequency oscillation (sLFO) blood flow signal during fMRI scanning. This signal is not removed by standard denoising procedures such as independent component analysis (ICA). However, we demonstrate that a specialized sLFO denoising procedure - Regressor Interpolation at Progressive Time Delays (RIPTiDe) - can be added to standard denoising pipelines to significantly attenuate functional connectivity inflation. We confirm the presence of sLFO-driven functional connectivity inflation in multiple independent fMRI datasets - including the Human Connectome Project - as well as across resting-state, task, and sleep-state conditions, and demonstrate its potential to produce false positive findings. Collectively, we present evidence for a previously unknown physiological phenomenon that spatiotemporally distorts estimates of brain connectivity in human fMRI datasets, and present a solution for mitigating this artifact.

Using carpet plots to analyze blood transit times in the brain during hypercapnic challenge magnetic resonance imaging.

Blood arrival time and blood transit time are useful metrics in characterizing hemodynamic behaviors in the brain. Functional magnetic resonance imaging in combination with a hypercapnic challenge has been proposed as a non-invasive imaging tool to determine blood arrival time and replace dynamic susceptibility contrast (DSC) magnetic resonance imaging, a current gold-standard imaging tool with the downsides of invasiveness and limited repeatability. Using a hypercapnic challenge, blood arrival times can be computed by cross-correlating the administered CO2 signal with the fMRI signal, which increases during elevated CO2 due to vasodilation. However, whole-brain transit times derived from this method can be significantly longer than the known cerebral transit time for healthy subjects (nearing 20 s vs. the expected 5-6 s). To address this unrealistic measurement, we here propose a novel carpet plot-based method to compute improved blood transit times derived from hypercapnic blood oxygen level dependent fMRI, demonstrating that the method reduces estimated blood transit times to an average of 5.32 s. We also investigate the use of hypercapnic fMRI with cross-correlation to compute the venous blood arrival times in healthy subjects and compare the computed delay maps with DSC-MRI time to peak maps using the structural similarity index measure (SSIM). The strongest delay differences between the two methods, indicated by low structural similarity index measure, were found in areas of deep white matter and the periventricular region. SSIM measures throughout the remainder of the brain reflected a similar arrival sequence derived from the two methods despite the exaggerated spread of voxel delays computed using CO2 fMRI.

Introduction to the shared near infrared spectroscopy format.

Significance: Functional near-infrared spectroscopy (fNIRS) is a popular neuroimaging technique with proliferating hardware platforms, analysis approaches, and software tools. There has not been a standardized file format for storing fNIRS data, which has hindered the sharing of data as well as the adoption and development of software tools. Aim: We endeavored to design a file format to facilitate the analysis and sharing of fNIRS data that is flexible enough to meet the community's needs and sufficiently defined to be implemented consistently across various hardware and software platforms. Approach: The shared NIRS format (SNIRF) specification was developed in consultation with the academic and commercial fNIRS community and the Society for functional Near Infrared Spectroscopy. Results: The SNIRF specification defines a format for fNIRS data acquired using continuous wave, frequency domain, time domain, and diffuse correlation spectroscopy devices. Conclusions: We present the SNIRF along with validation software and example datasets. Support for reading and writing SNIRF data has been implemented by major hardware and software platforms, and the format has found widespread use in the fNIRS community.

Spatial variation of changes in test-retest reliability of functional connectivity after global signal regression: The effect of considering hemodynamic delay.

Global signal regression (GSR) is a controversial analysis method, since its removal of signal has been observed to reduce the reliability of functional connectivity estimates. Here, we used test-retest reliability to characterize potential differences in spatial patterns between conventional, static GSR (sGSR) and a novel dynamic form of GSR (dGSR). In contrast with sGSR, dGSR models the global signal at a time delay to correct for blood arrival time. Thus, dGSR accounts for greater variation in global signal, removes blood-flow-related nuisance signal, and leaves higher quality neuronal signal remaining. We used intraclass correlation coefficients (ICCs) to estimate the reliability of functional connectivity in 462 healthy controls from the Human Connectome Project. We tested across two factors: denoising method used (control, sGSR, and dGSR), and interacquisition interval (between days, or within session while varying phase encoding direction). Reliability was estimated regionally to identify topographic patterns for each condition. sGSR and dGSR provided global reductions in reliability compared with the non-GSR control. Test-retest reliability was highest in the frontoparietal and default mode regions, and lowest in sensorimotor cortex for all conditions. dGSR provides more effective denoising in regions where both strategies greatly reduce reliability. Both GSR methods substantially reduced test-retest reliability, which was most evident in brain regions that had low reliability prior to denoising. These findings suggest that reliability of interregional correlation is likely inflated by the global signal, which is thought to primarily reflect dynamic blood flow.

Effects of cannabinoid exposure on short-term memory and medial orbitofrontal cortex function and chemistry in adolescent female rhesus macaques.

Aim: There is increasing concern that cannabinoid exposure during adolescence may disturb brain maturation and produce long-term cognitive deficits. However, studies in human subjects have provided limited evidence for such causality. The present study utilized behavioral and neuroimaging endpoints in female non-human primates to examine the effects of acute and chronic exposure during adolescence to the cannabinoid receptor full agonist, AM2389, on cognitive processing and brain function and chemistry. Materials and methods: Adolescent female rhesus macaques were trained on a titrating-delay matching-to-sample (TDMTS) touchscreen task that assays working memory. TDMTS performance was assessed before and during chronic exposure to AM2389, following antagonist (rimonabant) administration, and after discontinuation of the chronic regimen. Resting-state fMRI connectivity and magnetic resonance spectroscopy data were acquired prior to drug treatment, during chronic exposure, and following its discontinuation. Voxels were placed in the medial orbitofrontal cortex (mOFC), a region involved in memory processing that undergoes maturation during adolescence. Results: TDMTS performance was dose-dependently disrupted by acute AM2389; however, chronic treatment resulted in tolerance to these effects. TDMTS performance also was disrupted by discontinuation of the chronic regimen but surprisingly, not by rimonabant administration during chronic AM2389 treatment. mOFC N-acetylaspartate/creatine ratio decreased after acute and chronic administration but returned to baseline values following discontinuation of chronic treatment. Finally, intra-network functional connectivity (mOFC) increased during the chronic regimen and returned to baseline values following its discontinuation. Conclusion: Neural effects of a cannabinergic drug may persist during chronic exposure, notwithstanding the development of tolerance to behavioral effects. However, such effects dissipate upon discontinuation, reflecting the restorative capacity of affected brain processes.

WHOCARES: WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions.

Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions.Approach. Cardiac pulsation is a physiological confound of fMRI time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency (1/TR, with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation so that its removal through spectral filtering techniques is generally not possible. In this paper, we show that it is feasible to temporally and spatially resolve cardiac signals throughout the brain even when cardiac aliasing occurs by combining fMRI hyper-sampling with simultaneous multislice (SMS) imaging. The technique, which we name WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), was developed on 695 healthy subjects selected from the Human Connectome Project and its performance validated against the RETROICOR, HAPPY and the pulse oxymeter signal regression methods.Main results.WHOCARES is capable of retrieving voxel-wise cardiac signal regressors. This is achieved without employing external physiological recordings nor through ad hoc modelling assumptions. The performance of WHOCARES was, on average, superior to RETROICOR, HAPPY and the pulse oxymeter regression methods.Significance.WHOCARES holds basis for the reliable mapping of cardiac activity in fMRI time-series. WHOCARES can be employed for the retrospective removal of cardiac noise in publicly available fMRI datasets where physiological recordings are not available. WHOCARES is freely available athttps://github.com/gferrazzi/WHOCARES.

Safety and target engagement of an oral small-molecule sequestrant in adolescents with autism spectrum disorder: an open-label phase 1b/2a trial.

Autism spectrum disorder (ASD) is defined by hallmark behaviors involving reduced communication and social interaction as well as repetitive activities and restricted interests. ASD represents a broad spectrum, from minimally affected individuals to those requiring intense support, with additional manifestations often including anxiety, irritability/aggression and altered sensory processing. Gastrointestinal (GI) issues are also common in ASD, and studies have identified changes in the gut microbiome of individuals with ASD compared to control populations, complementing recent findings of differences in gut-derived metabolites in feces and circulation. However, a role for the GI tract or microbiome in ASD remains controversial. Here we report that an oral GI-restricted adsorbent (AB-2004) that has affinity for small aromatic or phenolic molecules relieves anxiety-like behaviors that are driven by a gut microbial metabolite in mice. Accordingly, a pilot human study was designed and completed to evaluate the safety of AB-2004 in an open-label, single-cohort, multiple-ascending-dose clinical trial that enrolled 30 adolescents with ASD and GI symptoms in New Zealand and Australia. AB-2004 was shown to have good safety and tolerability across all dose levels, and no drug-related serious adverse events were identified. Significant reductions in specific urinary and plasma levels of gut bacterial metabolites were observed between baseline and end of AB-2004 treatment, demonstrating likely target engagement. Furthermore, we observed improvements in multiple exploratory behavioral endpoints, most significantly in post hoc analysis of anxiety and irritability, as well as GI health, after 8 weeks of treatment. These results from an open-label study (trial registration no. ACTRN12618001956291) suggest that targeting gut-derived metabolites with an oral adsorbent is a safe and well-tolerated approach to improving symptoms associated with ASD, thereby emboldening larger placebo-controlled trials.

Estimating and mitigating the effects of systemic low frequency oscillations (sLFO) on resting state networks in awake non-human primates using time lag dependent methodology.

Aim: Resting-state fMRI (rs-fMRI) is often used to infer regional brain interactions from the degree of temporal correlation between spontaneous low-frequency fluctuations, thought to reflect local changes in the BOLD signal due to neuronal activity. One complication in the analysis and interpretation of rs-fMRI data is the existence of non-neuronal low frequency physiological noise (systemic low frequency oscillations; sLFOs) which occurs within the same low frequency band as the signal used to compute functional connectivity. Here, we demonstrate the use of a time lag mapping technique to estimate and mitigate the effects of the sLFO signal on resting state functional connectivity of awake squirrel monkeys. Methods: Twelve squirrel monkeys (6 male/6 female) were acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Rs-fMRI data was preprocessed using an in-house pipeline and sLFOs were detected using a seed regressor generated by averaging BOLD signal across all voxels in the brain, which was then refined recursively within a time window of -16-12 s. The refined regressor was then used to estimate the voxel-wise sLFOs; these regressors were subsequently included in the general linear model to remove these moving hemodynamic components from the rs-fMRI data using general linear model filtering. Group level independent component analysis (ICA) with dual regression was used to detect resting-state networks and compare networks before and after sLFO denoising. Results: Results show sLFOs constitute ~64% of the low frequency fMRI signal in squirrel monkey gray matter; they arrive earlier in regions in proximity to the middle cerebral arteries (e.g., somatosensory cortex) and later in regions close to draining vessels (e.g., cerebellum). Dual regression results showed that the physiological noise was significantly reduced after removing sLFOs and the extent of reduction was determined by the brain region contained in the resting-state network. Conclusion: These results highlight the need to estimate and remove sLFOs from fMRI data before further analysis.

An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.

High dimensionality data have become common in neuroimaging fields, especially group-level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data-driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group datasets is necessary for such analyses and is crucial to ensure the accuracy and stability of the outputs. In this study, we describe an fMRI data reduction strategy based on an adapted neighborhood preserving embedding (NPE) algorithm. Both simulated and real data results indicate that, compared with the widely used data reduction method, principal component analysis, the NPE-based data reduction method (a) shows superior performance on efficient data reduction, while enhancing group-level information, (b) develops a unique stratagem for selecting components based on an adjacency graph of eigenvectors, (c) generates more reliable and reproducible brain networks under different model orders when the outputs of NPE are used for ICA, (d) is more sensitive to revealing task-evoked activation for task fMRI, and (e) is extremely attractive and powerful for the increasingly popular fast fMRI and very large datasets.

Unravelling psychiatric heterogeneity and predicting suicide attempts in women with trauma-related dissociation using artificial intelligence.

Background: Suicide is a leading cause of death, and rates of attempted suicide have increased during the COVID-19 pandemic. The under-diagnosed psychiatric phenotype of dissociation is associated with elevated suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide.Objective: We designed an artificial intelligence approach to identify dissociative patients and predict prior suicide attempts in an unbiased, data-driven manner.Method: Participants were 30 controls and 93 treatment-seeking female patients with posttraumatic stress disorder (PTSD) and various levels of dissociation, including some with the PTSD dissociative subtype and some with dissociative identity disorder (DID).Results: Unsupervised learning models identified patients along a spectrum of dissociation. Moreover, supervised learning models accurately predicted prior suicide attempts with an F1 score up to 0.83. DID had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in PTSD and DID.Conclusions: These findings expand our understanding of the dissociative phenotype and underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.

Dissociation, feelings of detachment and disruption in one's sense of self and surroundings, is associated with an elevated risk of suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide.Using machine learning techniques, we found dissociative identity disorder had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in posttraumatic stress disorder and dissociative identity disorder.These findings underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.

Nicotine acutely alters temporal properties of resting brain states.

BACKGROUND: Nicotine-dependent individuals have altered activity in neurocognitive networks such as the default mode (DMN), salience (SN) and central executive networks (CEN). One theory suggests that, among chronic tobacco smokers, nicotine abstinence drives more DMN-related internal processing while nicotine replacement suppresses DMN and enhances SN and CEN. Whether acute nicotine impacts network dynamics in non-smokers is, however, unknown. METHODS: In a randomized double-blind crossover study, 17 healthy non-smokers (8 females) were administered placebo and nicotine (2-mg lozenge) on two different days prior to collecting resting-state functional magnetic resonance imaging (fMRI). Previously defined brain states in 462 individuals that spatially overlap with well-characterized resting-state networks including the DMN, SN, and CEN were applied to compute state-specific dynamics at rest: total time spent in state, persistence in each state after entry, and frequency of state transitions. We examined whether nicotine acutely alters these resting-state dynamics. RESULTS: A significant drug-by-state interaction emerged; post-hoc analyses clarified that, relative to placebo, nicotine suppressed time spent in a frontoinsular-DMN state (posterior cingulate cortex, medial prefrontal cortex, anterior insula, striatum and orbitofrontal cortex) and enhanced time spent in a SN state (anterior cingulate cortex and insula). No significant findings were observed for persistence and frequency. CONCLUSIONS: In non-smokers, nicotine biases resting-state brain function away from the frontoinsular-DMN and toward the SN, which may reduce internally focused cognition and enhance salience processing. While past work suggests nicotine impacts DMN activity, the current work shows nicotinic influences on a specific DMN-like network that has been linked with rumination and depression.

Using carpet plots to analyze transit times of low frequency oscillations in resting state fMRI.

A "carpet plot" is a 2-dimensional plot (time vs. voxel) of scaled fMRI voxel intensity values. Low frequency oscillations (LFOs) can be successfully identified from BOLD fMRI and used to study characteristics of neuronal and physiological activity. Here, we evaluate the use of carpet plots paired with a developed slope-detection algorithm as a means to study LFOs in resting state fMRI (rs-fMRI) data with the help of dynamic susceptibility contrast (DSC) MRI data. Carpet plots were constructed by ordering voxels according to signal delay time for each voxel. The slope-detection algorithm was used to identify and calculate propagation times, or "transit times", of tilted vertical edges across which a sudden signal change was observed. We aim to show that this metric has applications in understanding LFOs in fMRI data, possibly reflecting changes in blood flow speed during the scan, and for evaluating alternative blood-tracking contrast agents such as inhaled CO2. We demonstrate that the propagations of LFOs can be visualized and automatically identified in a carpet plot as tilted lines of sudden intensity change. Resting state carpet plots produce edges with transit times similar to those of DSC carpet plots. Additionally, resting state carpet plots indicate that edge transit times vary at different time points during the scan.

Sex differences in functional network dynamics observed using coactivation pattern analysis.

Sex differences in the organization of large-scale resting-state brain networks have been identified using traditional static measures, which average functional connectivity over extended time periods. In contrast, emerging dynamic measures have the potential to define sex differences in network changes over time, providing additional understanding of neurobiological sex differences. To meet this goal, we used a Coactivation Pattern Analysis (CAP) using resting-state functional magnetic resonance imaging data from 181 males and 181 females from the Human Connectome Project. Significant main effects of sex were observed across two independent imaging sessions. Relative to males, females spent more total time in two transient network states (TNSs) spatially overlapping with the dorsal attention network and occipital/sensory-motor network. Greater time spent in these TNSs was related to females making more frequent transitions into these TNSs compared to males. In contrast, males spent more total time in TNSs spatially overlapping with the salience network, which was related to males staying for longer periods once entering these TNSs compared to females. State-to-state transitions also significantly differed between sexes: females transitioned more frequently from default mode network (DMN) states to the dorsal attention network state, whereas males transitioned more frequently from DMN states to salience network states. Results show that males and females spend differing amounts of time at rest in two distinct attention-related networks and show sex-specific transition patterns from DMN states into these attention-related networks. This work lays the groundwork for future investigations into the cognitive and behavioral implications of these sex-specific network dynamics.

An MRI protocol for anatomical and functional evaluation of the California sea lion brain.

BACKGROUND: Domoic acid (DOM) is a neurotoxin produced by some harmful algae blooms in coastal waters. California sea lions (Zalophus californianus) exposed to DOM often strand on beaches where they exhibit a variety of symptoms, including seizures. These animals typically show hippocampal atrophy on MRI scans. NEW METHOD: We describe an MRI protocol for comprehensive evaluation of DOM toxicosis in the sea lion brain. We intend to study brain development in pups exposed in utero. The protocol depicts the hippocampal formation as the primary region of interest. We include scans for quantitative morphometry, functional and structural connectivity, and a cerebral blood flow map. RESULTS: High-resolution 3D anatomical scans facilitate post hoc slicing in arbitrary planes and accurate morphometry. We demonstrate the first cerebral blood flow map using MRI, and the first structural tractography from a live sea lion brain. COMPARISON WITH EXISTING METHODS: Scans were compared to prior anatomical and functional studies in live sea lions, and structural connectivity in post mortem specimens. Hippocampal volumes were broadly in line with prior studies, with differences likely attributable to the 3D approach used here. Functional connectivity of the dorsal left hippocampus matched that found in a prior study conducted at a lower magnetic field, while structural connectivity in the live brain agreed with findings observed in post mortem studies. CONCLUSIONS: Our protocol provides a comprehensive, longitudinal view of the functional and anatomical changes expected to result from DOM toxicosis. It can also screen for other common neurological pathologies and is suitable for any pinniped that can fit inside an MRI scanner.

Increased cerebral blood flow in the right anterior cingulate cortex and fronto-orbital cortex during go/no-go task in children with ADHD.

OBJECTIVE: Arterial spin labeling (ASL) is a relatively new imaging modality in the field of the cognitive neuroscience. In the present study, we aimed to compare the dynamic regional cerebral blood flow alterations of children with ADHD and healthy controls during a neurocognitive task by using event-related ASL scanning. METHODS: The study comprised of 17 healthy controls and 20 children with ADHD. The study subjects were scanned on 3 Tesla MRI scanner to obtain ASL imaging data. Subjects performed go/no-go task during the ASL image acquisition. The image analyses were performed by FEAT (fMRI Expert Analysis Tool) Version 6. RESULTS: The mean age was 10.88 ± 1.45 and 11 ± 1.91 for the control and ADHD group, respectively (p = .112). The go/no-go task was utilized during the ASL scanning. The right anterior cingulate cortex (BA32) extending into the frontopolar and orbitofrontal cortices (BA10 and 11) displayed greater activation in ADHD children relative to the control counterparts (p < .001). With a lenient significance threshold, greater activation was revealed in the right-sided frontoparietal regions during the go session, and in the left precuneus during the no-go session. CONCLUSION: These results indicate that children with ADHD needed to over-activate frontopolar cortex, anterior cingulate as well as the dorsal and ventral attention networks to compensate for the attention demanded in a given cognitive task.

Consistency of independent component analysis for FMRI.

BACKGROUND: Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts. Most critical is the inconsistency of some of the extracted components when ICA is run with different model orders (MOs). NEW METHOD: In this study, a novel method of determining the consistency of component analysis (CoCA) is proposed to evaluate the consistency of extracted components with different model orders. In the method, "consistent components" (CCs) are defined as those which can be extracted repeatably over a range of model orders. RESULT: The efficacy of the method was evaluated with simulation data and fMRI datasets. With our method, the simulation result showed a clear difference of consistency between ground truths and noise. COMPARISON WITH EXISTING METHODS: The information criteria were implemented to provide suggestions for the optimal model order, where some of the ICs were revealed inconsistent in our proposed method. CONCLUSIONS: This method provided an objective protocol for choosing CCs of an ICA decomposition of a data matrix, independent of model order. This is especially useful with high model orders, where noise or other disturbances could possibly lead to an instability of the components.

Post-hoc physiological waveform extraction from motion estimation in simultaneous multislice (SMS) functional MRI using separate stack processing.

PURPOSE: Motion estimation is an essential step in functional MRI (fMRI) preprocessing. Usually, fMRI processing software packages (eg, FSL and AFNI) automatically estimate motion parameters in order to counteract the effects of motion. However, the time courses of the motion estimation for fMRI data also contain information about physiological processes. Here, we show that respiration and cardiac signals can be extracted from motion estimation at significantly higher bandwidth than is possible with current methods. METHOD: To detect motion at high effective temporal resolution (HighRes), the motion parameters of stacks of simultaneously acquired slices were estimated separately, then combined. This method was validated by extracting physiological motion signals from resting state fMRI (rsfMRI) data (Enhanced Nathan Kline Institute-Rockland Sample) and comparing them to respiration belt and pulse oximeter signals. RESULTS: HighRes motion time-courses with an effective sampling rate of 15.5 and 11.4 Hz were extracted from repetition time (TR) = 0.645 and 1.4 s data, respectively. Respiration waveforms were extracted with significantly higher accuracy than the original motion parameters. Even cardiac waveforms could be extracted, despite the fact that the sampling time or TR values were too long to sample cardiac frequencies. CONCLUSION: HighRes motion traces provide insight into the subjects' motion at higher frequencies than can be estimated using standard techniques. In its simplest form, this technique can recover accurate respiration signals and may reveal additional complexity in brain motion.