Differential Laminar Activation Dissociates Encoding and Retrieval in the Human Medial and Lateral Entorhinal Cortex.

The hierarchically organized structures of the medial temporal lobe are critically important for episodic memory function. Accumulating evidence suggests dissociable information processing pathways are maintained throughout these structures including in the medial and lateral entorhinal cortex. Cortical layers provide an additional dimension of dissociation as the primary input to the hippocampus derives from layer 2 neurons in the entorhinal cortex, whereas the deeper layers primarily receive output from the hippocampus. Here, novel high-resolution T2-prepared functional MRI methods were successfully used to mitigate susceptibility artifacts typically affecting MRI signals in this region providing uniform sensitivity across the medial and lateral entorhinal cortex. During the performance of a memory task, healthy human subjects (age 25-33 years, mean age 28.2 ± 3.3 years, 4 female) showed differential functional activation in the superficial and deep layers of the entorhinal cortex associated with task-related encoding and retrieval conditions, respectively. The methods provided here offer an approach to probe layer-specific activation in normal cognition and conditions contributing to memory impairment.SIGNIFICANCE STATEMENT This study provides new evidence for differential neuronal activation in the superficial versus deep layers of the entorhinal cortex associated with encoding and retrieval memory processes, respectively, in cognitively normal adults. The study further shows that this dissociation can be observed in both the medial and the lateral entorhinal cortex. The study was achieved by using a novel functional MRI method allowing us to measure robust functional MRI signals in both the medial and lateral entorhinal cortex that was not possible in previous studies. The methodology established here in healthy human subjects lays a solid foundation for subsequent studies investigating layer-specific and region-specific changes in the entorhinal cortex associated with memory impairment in various conditions such as Alzheimer's disease.

A second dimension of somatosensory system injury? Thalamic volume loss and neuropathic pain in adults with cerebral palsy and periventricular white matter injury.

Objectives: Lemniscal (motor-related) and spinothalamic (neuropathic pain-related) somatosensory abnormalities affect different subsets of adults with cerebral palsy (CP). Lemniscal/motor abnormalities are associated with posterior thalamic radiation white matter disruption in individuals with CP and white matter injury. We tested the hypothesis that neuropathic pain symptoms in this population are rather associated with injury of the somatosensory (posterior group nuclei) thalamus. Methods: In this cross-sectional study, communicative adults with CP and bilateral white matter injury and neurotypical control participants volunteered to self-report pain symptoms and undergo research MRI. Posterior group thalamic nuclei volume was computed and correlated against neuropathic pain scores. Results: Participants with CP (n=6) had, on average, 24% smaller posterior group thalamic volumes (95% CI [10-39%]; corrected p=0.01) than control participants. More severe volume loss was correlated with more severe neuropathic pain scores (ρ=-0.87 [-0.99,-0.20]; p=0.02). Discussion: Association with thalamic volume loss suggests that neuropathic pain in adults with CP may frequently be central neuropathic pain. Complementing assessments of white matter microstructure, regional brain volumes hold promise as diagnostic biomarkers for central neuropathic pain in individuals with structural brain disorders.

Functional and Structural Brain Connectivity in Children With Bilateral Cerebral Palsy Compared to Age-Related Controls and in Response to Intensive Rapid-Reciprocal Leg Training.

Background: Compared to unilateral cerebral palsy (CP), less is known about brain reorganization and plasticity in bilateral CP especially in relation or response to motor training. The few trials that reported brain imaging results alongside functional outcomes include a handful of studies in unilateral CP, and one pilot trial of three children with bilateral CP. This study is the first locomotor training randomized controlled trial (RCT) in bilateral CP to our knowledge reporting brain imaging outcomes. Methods: Objective was to compare MRI brain volumes, resting state connectivity and white matter integrity using DTI in children with bilateral CP with PVL and preterm birth history (<34 weeks), to age-related controls, and from an RCT of intensive 12 week rapid-reciprocal locomotor training using an elliptical or motor-assisted cycle. We hypothesized that connectivity in CP compared to controls would be greater across sensorimotor-related brain regions and that functional (resting state) and structural (fractional anisotropy) connectivity would improve post intervention. We further anticipated that baseline and post-intervention imaging and functional measures would correlate. Results: Images were acquired with a 3T MRI scanner for 16/27 children with CP in the trial, and 18 controls. No conclusive evidence of training-induced neuroplastic effects were seen. However, analysis of shared variance revealed that greater increases in precentral gyrus connectivity with the thalamus and pons may be associated with larger improvements in the trained device speed. Exploratory analyses also revealed interesting potential relationships between brain integrity and multiple functional outcomes in CP, with functional connectivity between the motor cortex and midbrain showing the strongest potential relationship with mobility. Decreased posterior white matter, corpus callosum and thalamic volumes, and FA in the posterior thalamic radiation were the most prominent group differences with corticospinal tract differences notably not found. Conclusions: Results reinforce the involvement of sensory-related brain areas in bilateral CP. Given the wide individual variability in imaging results and clinical responses to training, a greater focus on neural and other mechanisms related to better or worse outcomes is recommended to enhance rehabilitation results on a patient vs. group level.

Psilocybin therapy increases cognitive and neural flexibility in patients with major depressive disorder.

Psilocybin has shown promise for the treatment of mood disorders, which are often accompanied by cognitive dysfunction including cognitive rigidity. Recent studies have proposed neuropsychoplastogenic effects as mechanisms underlying the enduring therapeutic effects of psilocybin. In an open-label study of 24 patients with major depressive disorder, we tested the enduring effects of psilocybin therapy on cognitive flexibility (perseverative errors on a set-shifting task), neural flexibility (dynamics of functional connectivity or dFC via functional magnetic resonance imaging), and neurometabolite concentrations (via magnetic resonance spectroscopy) in brain regions supporting cognitive flexibility and implicated in acute psilocybin effects (e.g., the anterior cingulate cortex, or ACC). Psilocybin therapy increased cognitive flexibility for at least 4 weeks post-treatment, though these improvements were not correlated with the previously reported antidepressant effects. One week after psilocybin therapy, glutamate and N-acetylaspartate concentrations were decreased in the ACC, and dFC was increased between the ACC and the posterior cingulate cortex (PCC). Surprisingly, greater increases in dFC between the ACC and PCC were associated with less improvement in cognitive flexibility after psilocybin therapy. Connectome-based predictive modeling demonstrated that baseline dFC emanating from the ACC predicted improvements in cognitive flexibility. In these models, greater baseline dFC was associated with better baseline cognitive flexibility but less improvement in cognitive flexibility. These findings suggest a nuanced relationship between cognitive and neural flexibility. Whereas some enduring increases in neural dynamics may allow for shifting out of a maladaptively rigid state, larger persisting increases in neural dynamics may be of less benefit to psilocybin therapy.

Increased integration between default mode and task-relevant networks in children with ADHD is associated with impaired response control.

Default mode network (DMN) dysfunction is theorized to play a role in attention lapses and task errors in children with attention-deficit/hyperactivity disorder (ADHD). In ADHD, the DMN is hyperconnected to task-relevant networks, and both increased functional connectivity and reduced activation are related to poor task performance. The current study extends existing literature by considering interactions between the DMN and task-relevant networks from a brain network perspective and by assessing how these interactions relate to response control. We characterized both static and time-varying functional brain network organization during the resting state in 43 children with ADHD and 43 age-matched typically developing (TD) children. We then related aspects of network integration to go/no-go performance. We calculated participation coefficient (PC), a measure of a region's inter-network connections, for regions of the DMN, canonical cognitive control networks (fronto-parietal, salience/cingulo-opercular), and motor-related networks (somatomotor, subcortical). Mean PC was higher in children with ADHD as compared to TD children, indicating greater integration across networks. Further, higher and less variable PC was related to greater commission error rate in children with ADHD. Together, these results inform our understanding of the role of the DMN and its interactions with task-relevant networks in response control deficits in ADHD.

Children with attention-deficit/hyperactivity disorder spend more time in hyperconnected network states and less time in segregated network states as revealed by dynamic connectivity analysis.

Previous studies in children with attention-deficit/hyperactivity disorder (ADHD) have observed functional brain network disruption on a whole-brain level, as well as on a sub-network level, particularly as related to the default mode network, attention-related networks, and cognitive control-related networks. Given behavioral findings that children with ADHD have more difficulty sustaining attention and more extreme moment-to-moment fluctuations in behavior than typically developing (TD) children, recently developed methods to assess changes in connectivity over shorter time periods (i.e., "dynamic functional connectivity"), may provide unique insight into dysfunctional network organization in ADHD. Thus, we performed a dynamic functional connectivity (FC) analysis on resting state fMRI data from 38 children with ADHD and 79 TD children. We used Hidden semi-Markov models (HSMMs) to estimate six network states, as well as the most probable sequence of states for each participant. We quantified the dwell time, sojourn time, and transition probabilities across states. We found that children with ADHD spent less total time in, and switched more quickly out of, anticorrelated states involving the default mode network and task-relevant networks as compared to TD children. Moreover, children with ADHD spent more time in a hyperconnected state as compared to TD children. These results provide novel evidence that underlying dynamics may drive the differences in static FC patterns that have been observed in ADHD and imply that disrupted FC dynamics may be a mechanism underlying the behavioral symptoms and cognitive deficits commonly observed in children with ADHD.

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm.

A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its "pacemaker", continuously generating a ca 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of "mismatched" data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.

Emotions and brain function are altered up to one month after a single high dose of psilocybin.

Psilocybin is a classic psychedelic compound that may have efficacy for the treatment of mood and substance use disorders. Acute psilocybin effects include reduced negative mood, increased positive mood, and reduced amygdala response to negative affective stimuli. However, no study has investigated the long-term, enduring impact of psilocybin on negative affect and associated brain function. Twelve healthy volunteers (7F/5M) completed an open-label pilot study including assessments 1-day before, 1-week after, and 1-month after receiving a 25 mg/70 kg dose of psilocybin to test the hypothesis that psilocybin administration leads to enduring changes in affect and neural correlates of affect. One-week post-psilocybin, negative affect and amygdala response to facial affect stimuli were reduced, whereas positive affect and dorsal lateral prefrontal and medial orbitofrontal cortex responses to emotionally-conflicting stimuli were increased. One-month post-psilocybin, negative affective and amygdala response to facial affect stimuli returned to baseline levels while positive affect remained elevated, and trait anxiety was reduced. Finally, the number of significant resting-state functional connections across the brain increased from baseline to 1-week and 1-month post-psilocybin. These preliminary findings suggest that psilocybin may increase emotional and brain plasticity, and the reported findings support the hypothesis that negative affect may be a therapeutic target for psilocybin.

Differential Changes in Functional Connectivity of Striatum-Prefrontal and Striatum-Motor Circuits in Premanifest Huntington's Disease.

BACKGROUND: Huntington's disease (HD) is a progressive neurodegenerative disorder. The striatum is one of the first brain regions that show detectable atrophy in HD. Previous studies using functional magnetic resonance imaging (fMRI) at 3 tesla (3 T) revealed reduced functional connectivity between striatum and motor cortex in the prodromal period of HD. Neuroanatomical and neurophysiological studies have suggested segregated corticostriatal pathways with distinct loops involving different cortical regions, which may be investigated using fMRI at an ultra-high field (7 T) with enhanced sensitivity compared to lower fields. OBJECTIVES: We performed fMRI at 7 T to assess functional connectivity between the striatum and several chosen cortical areas including the motor and prefrontal cortex, in order to better understand brain changes in the striatum-cortical pathways. METHOD: 13 manifest subjects (age 51 ± 13 years, cytosine-adenine-guanine [CAG] repeat 45 ± 5, Unified Huntington's Disease Rating Scale [UHDRS] motor score 32 ± 17), 8 subjects in the close-to-onset premanifest period (age 38 ± 10 years, CAG repeat 44 ± 2, UHDRS motor score 8 ± 2), 11 subjects in the far-from-onset premanifest period (age 38 ± 11 years, CAG repeat 42 ± 2, UHDRS motor score 1 ± 2), and 16 healthy controls (age 44 ± 15 years) were studied. The functional connectivity between the striatum and several cortical areas was measured by resting state fMRI at 7 T and analyzed in all participants. RESULTS: Compared to controls, functional connectivity between striatum and premotor area, supplementary motor area, inferior frontal as well as middle frontal regions was altered in HD (all p values <0.001). Specifically, decreased striatum-motor connectivity but increased striatum-prefrontal connectivity were found in premanifest HD subjects. Altered functional connectivity correlated consistently with genetic burden, but not with clinical scores. CONCLUSIONS: Differential changes in functional connectivity of striatum-prefrontal and striatum-motor circuits can be found in early and premanifest HD. This may imply a compensatory mechanism, where additional cortical regions are recruited to subserve functions that have been impaired due to HD pathology. Our results suggest the potential value of functional connectivity as a marker for future clinical trials in HD.

Investigating the impact of autocorrelation on time-varying connectivity.

In recent years, a number of studies have reported on the existence of time-varying functional connectivity (TVC) in resting-state functional magnetic resonance imaging (rs-fMRI) data. The sliding-window technique is currently one of the most commonly used methods to estimate TVC. Although previous studies have shown that autocorrelation can negatively impact estimates of static functional connectivity, its impact on TVC estimates is not well known at this time. In this paper, we show both theoretically and empirically that the existence of autocorrelation within a time series can inflate the sampling variability of TVC estimated using the sliding-window technique. This can in turn increase the risk of misinterpreting noise as true TVC and negatively impact subsequent estimation of whole-brain time-varying FC profiles, or "brain states". The latter holds as more variable input measures lead to more variable output measures in the state estimation procedure. Finally, we demonstrate that prewhitening the data prior to analysis can lower the variance of the estimated TVC and improve brain state estimation. These results suggest that careful consideration is required when making inferences on TVC.

Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models.

The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subject's most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms.

Altered functional connectivity between sub-regions in the thalamus and cortex in schizophrenia patients measured by resting state BOLD fMRI at 7T.

The thalamus is a small brain structure that relays neuronal signals between subcortical and cortical regions. Abnormal thalamocortical connectivity in schizophrenia has been reported in previous studies using blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI) performed at 3T. However, anatomically the thalamus is not a single entity, but is subdivided into multiple distinct nuclei with different connections to various cortical regions. We sought to determine the potential benefit of using the enhanced sensitivity of BOLD fMRI at ultra-high magnetic field (7T) in exploring thalamo-cortical connectivity in schizophrenia based on subregions in the thalamus. Seeds placed in thalamic subregions of 14 patients and 14 matched controls were used to calculate whole-brain functional connectivity. Our results demonstrate impaired thalamic connectivity to the prefrontal cortex and the cerebellum, but enhanced thalamic connectivity to the motor/sensory cortex in schizophrenia. This altered functional connectivity significantly correlated with disease duration in the patients. Remarkably, comparable effect sizes observed in previous 3T studies were detected in the current 7T study with a heterogeneous and much smaller cohort, providing evidence that ultra-high field fMRI may be a powerful tool for measuring functional connectivity abnormalities in schizophrenia. Further investigation with a larger cohort is merited to validate the current findings.

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage.

Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICCMSE) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully when using partial correlations.

Accounting for the role of hematocrit in between-subject variations of MRI-derived baseline cerebral hemodynamic parameters and functional BOLD responses.

Baseline hematocrit fraction (Hct) is a determinant for baseline cerebral blood flow (CBF) and between-subject variation of Hct thus causes variation in task-based BOLD fMRI signal changes. We first verified in healthy volunteers (n = 12) that Hct values can be derived reliably from venous blood T1 values by comparison with the conventional lab test. Together with CBF measured using phase-contrast MRI, this noninvasive estimation of Hct, instead of using a population-averaged Hct value, enabled more individual determination of oxygen delivery (DO2 ), oxygen extraction fraction (OEF), and cerebral metabolic rate of oxygen (CMRO2 ). The inverse correlation of CBF and Hct explained about 80% of between-subject variation of CBF in this relatively uniform cohort of subjects, as expected based on the regulation of DO2 to maintain constant CMRO2 . Furthermore, we compared the relationships of visual task-evoked BOLD response with Hct and CBF. We showed that Hct and CBF contributed 22%-33% of variance in BOLD signal and removing the positive correlation with Hct and negative correlation with CBF allowed normalization of BOLD signal with 16%-22% lower variability. The results of this study suggest that adjustment for Hct effects is useful for studies of MRI perfusion and BOLD fMRI. Hum Brain Mapp 39:344-353, 2018. © 2017 Wiley Periodicals, Inc.

Language Mapping Using T2-Prepared BOLD Functional MRI in the Presence of Large Susceptibility Artifacts-Initial Results in Patients With Brain Tumor and Epilepsy.

At present, presurgical functional mapping is the most prevalent clinical application of functional magnetic resonance imaging (fMRI). Signal dropouts and distortions caused by susceptibility effects in the current standard echo planar imaging (EPI)-based fMRI images are well-known problems and pose a major hurdle for the application of fMRI in several brain regions, many of which are related to language mapping in presurgical planning. Such artifacts are particularly problematic in patients with previous surgical resection cavities, craniotomy hardware, hemorrhage, and vascular malformation. A recently developed T2-prepared (T2prep) fMRI approach showed negligible distortion and dropouts in the entire brain even in the presence of large susceptibility effects. Here, we present initial results comparing T2prep- and multiband EPI-fMRI scans for presurgical language mapping using a sentence completion task in patients with brain tumor and epilepsy. In all patients scanned, T2prep-fMRI showed minimal image artifacts (distortion and dropout) and greater functional sensitivity than EPI-fMRI around the lesions containing blood products and in air-filled cavities. This enhanced sensitivity in T2prep-fMRI was also evidenced by the fact that functional activation during the sentence completion task was detected with T2prep-fMRI but not with EPI-fMRI in the affected areas with the same statistical threshold, whereas cerebrovascular reactivity during a breath-hold task was preserved in these same regions, implying intact neurovascular coupling in these patients. Although further investigations are required to validate these findings with invasive methods such as direct cortical stimulation mapping as the gold standard, this approach provides an alternative method for performing fMRI in brain regions with large susceptibility effects.

Comparing test-retest reliability of dynamic functional connectivity methods.

Due to the dynamic, condition-dependent nature of brain activity, interest in estimating rapid functional connectivity (FC) changes that occur during resting-state functional magnetic resonance imaging (rs-fMRI) has recently soared. However, studying dynamic FC is methodologically challenging, due to the low signal-to-noise ratio of the blood oxygen level dependent (BOLD) signal in fMRI and the massive number of data points generated during the analysis. Thus, it is important to establish methods and summary measures that maximize reliability and the utility of dynamic FC to provide insight into brain function. In this study, we investigated the reliability of dynamic FC summary measures derived using three commonly used estimation methods - sliding window (SW), tapered sliding window (TSW), and dynamic conditional correlations (DCC) methods. We applied each of these techniques to two publicly available rs-fMRI test-retest data sets - the Multi-Modal MRI Reproducibility Resource (Kirby Data) and the Human Connectome Project (HCP Data). The reliability of two categories of dynamic FC summary measures were assessed, specifically basic summary statistics of the dynamic correlations and summary measures derived from recurring whole-brain patterns of FC ("brain states"). The results provide evidence that dynamic correlations are reliably detected in both test-retest data sets, and the DCC method outperforms SW methods in terms of the reliability of summary statistics. However, across all estimation methods, reliability of the brain state-derived measures was low. Notably, the results also show that the DCC-derived dynamic correlation variances are significantly more reliable than those derived using the non-parametric estimation methods. This is important, as the fluctuations of dynamic FC (i.e., its variance) has a strong potential to provide summary measures that can be used to find meaningful individual differences in dynamic FC. We therefore conclude that utilizing the variance of the dynamic connectivity is an important component in any dynamic FC-derived summary measure.

Subject-specific regional measures of water diffusion are associated with impairment in chronic spinal cord injury.

PURPOSE: We aimed to identify non-invasive imaging parameters that can serve as biomarkers for the integrity of the spinal cord, which is paramount to neurological function. Diffusion tensor imaging (DTI) indices are sensitive to axonal and myelin damage, and have strong potential to serve as such biomarkers. However, averaging DTI indices over large regions of interest (ROIs), a common approach to analyzing the images of injured spinal cord, leads to loss of subject-specific information. We investigated if DTI-tractography-driven, subject-specific demarcation approach can yield measures that are more specific to impairment. METHODS: In 18 individuals with chronic spinal cord injury (SCI), subject-specific demarcation of the injury region was performed using DTI tractography, which yielded three regions relative to injury (RRI; regions superior to, at, and below injury epicenter). DTI indices averaged over each RRI were correlated with measures of residual motor and sensory function, obtained using the International Standard of Neurological Classification for Spinal Cord Injury (ISNCSCI). RESULTS: Total ISNCSCI score (ISNCSCI-tot; sum of ISNCSCI motor and sensory scores) was significantly (p < 0.05) correlated with fractional anisotropy and axial and radial diffusivities. ISNCSCI-tot showed strongest correlation with indices measured from the region inferior to the injury epicenter (IRRI), the degree of which exceeded that of those measured from the entire cervical cord-suggesting contribution from Wallerian degeneration. CONCLUSION: DTI tractography-driven, subject-specific injury demarcation approach provided measures that were more specific to impairment. Notably, DTI indices obtained from the IRRI region showed the highest specificity to impairment, demonstrating their strong potential as biomarkers for the SCI severity.

Parallel group independent component analysis for massive fMRI data sets.

Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.

A three-dimensional single-scan approach for the measurement of changes in cerebral blood volume, blood flow, and blood oxygenation-weighted signals during functional stimulation.

The blood-oxygenation-level-dependent (BOLD) effect reflects ensemble changes in several physiological parameters such as cerebral blood volume (CBV), blood flow (CBF), and cerebral metabolic rate of oxygen (CMRO2). Quantitative BOLD approaches have been developed to estimate CMRO2 dynamics from BOLD, CBF and CBV responses, generally using separate scans. The ability to detect changes in these parameters in a single scan would shorten the total scan time and reduce temporal variations in physiology or neuronal responses. Here, an acquisition strategy, named 3D TRiple-acquisition after Inversion Preparation (3D-TRIP), is demonstrated for 3D acquisition of CBV, CBF, and BOLD signal changes in a single scan by incorporating VASO, FAIR-ASL and T2-prepared BOLD fMRI methods. Using a visual stimulation paradigm, we demonstrate that the activation patterns, relative signal changes, temporal signal-to-noise ratio (tSNR), contrast-to-noise ratio (CNR), and estimated CMRO2 changes during visual stimulation are all comparable between the concurrent imaging proposed here and the separate scans conventionally applied. This approach is expected to provide a useful alternative for quantitative BOLD fMRI studies where information about oxygen metabolism alterations can be extracted from changes in hemodynamic signals associated with CBV, CBF, and blood oxygenation.

Decoupling of reaction time-related default mode network activity with cognitive demand.

Reaction Time (RT) is associated with increased amplitude of the Blood Oxygen-Level Dependent (BOLD) response in task positive regions. Few studies have focused on whether opposing RT-related suppression of task activity also occurs. The current study used two Go/No-go tasks with different cognitive demands to examine regions that showed greater BOLD suppression for longer RT trials. These RT-related suppression effects occurred within the DMN and were task-specific, localizing to separate regions for the two tasks. In the task requiring working memory, RT-related de-coupling of the DMN occurred. This was reflected by opposing RT-BOLD effects for different DMN regions, as well as by reduced positive RT-related Psycho-Physiological Interaction (PPI) connectivity within the DMN and a lack of negative RT-related PPI connectivity between DMN and task positive regions. The results suggest that RT-related DMN suppression is task-specific. RT-related de-coupling of the DMN with more complex task demands may contribute to lapses of attention and performance decrements that occur during cognitively-demanding tasks.