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.

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.

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.

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.

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.

Temporal Dynamics of Large-Scale Networks Predict Neural Cue Reactivity and Cue-Induced Craving.

BACKGROUND: Cue reactivity, a core characteristic of substance use disorders, commonly recruits brain regions that are key nodes in neurocognitive networks, including the default mode network (DMN) and salience network (SN). Whether resting-state temporal dynamic properties of these networks relate to subsequent cue reactivity and cue-induced craving is unknown. METHODS: The resting-state data of 46 nicotine-dependent participants were assessed to define temporal dynamic properties of DMN and SN states. Temporal dynamics focused on the total time across the scan session that brain activity resides in these specific states. Using regression models, we examined how the total time in each state related to neural reactivity to smoking cues within key DMN (posterior cingulate cortex, medial prefrontal cortex) or SN (anterior insula, dorsal anterior cingulate cortex) nodes. Mediation analyses were subsequently conducted to study how neural cue reactivity mediates the relationship between total time in state at rest and subjective cue-induced craving. RESULTS: Increased time spent in the DMN state and decreased time spent in the SN state predicted subsequent cue-induced increases in the anterior insula and dorsal anterior cingulate cortex, respectively. Cue-induced anterior insula and dorsal anterior cingulate cortex activity significantly mediated the relationship between time spent in DMN/SN and cue-induced subjective craving. CONCLUSIONS: Our findings showed a significant relationship between resting-state dynamics of the DMN/SN and task-activated SN nodes that together predicted cue-induced craving changes in nicotine-dependent individuals. These findings propose a neurobiological pathway for cue-induced craving that begins with resting-state temporal dynamics, suggesting that brain responding to external stimuli is driven by resting temporal dynamics.

Dynamic functioning of transient resting-state coactivation networks in the Human Connectome Project.

Resting-state analyses evaluating large-scale brain networks have largely focused on static correlations in brain activity over extended time periods, however emerging approaches capture time-varying or dynamic patterns of transient functional networks. In light of these new approaches, there is a need to classify common transient network states (TNS) in terms of their spatial and dynamic properties. To fill this gap, two independent resting state scans collected in 462 healthy adults from the Human Connectome Project were evaluated using coactivation pattern analysis to identify (eight) TNS that recurred across participants and over time. These TNS spatially overlapped with prototypical resting state networks, but also diverged in notable ways. In particular, analyses revealed three TNS that shared cortical midline overlap with the default mode network (DMN), but these "complex" DMN states also encompassed distinct regions that fall beyond the prototypical DMN, suggesting that the DMN defined using static methods may represent the average of distinct complex-DMN states. Of note, dwell time was higher in "complex" DMN states, challenging the idea that the prototypical DMN, as a single unit, is the dominant resting-state network as typically defined by static resting state methods. In comparing the two resting state scans, we also found high reliability in the spatial organization and dynamic activities of network states involving DMN or sensorimotor regions. Future work will determine whether these TNS defined by coactivation patterns are in other samples, and are linked to fundamental cognitive properties.

Craving and Cue Reactivity in Nicotine-Dependent Tobacco Smokers Is Associated With Different Insula Networks.

BACKGROUND: The insula has a well-established role in nicotine dependence and is a node of the salience network, which integrates internal and external information to guide behavior. Recent findings reveal that internal and external processing occurs in the ventral anterior insula (vAI) and dorsal anterior insula (dAI), respectively. Whether vAI/dAI network connectivity differentially reflects internally generated craving and externally triggered smoking cue reactivity was tested. METHODS: Thirty-six male and female nicotine-dependent individuals smoked 1 hour before functional magnetic resonance imaging. Baseline craving was measured, followed by resting-state and smoking cue reactivity scans and then another assessment of craving. Craving and cue reactivity interactions were measured by focusing on specific nodes of the salience network: the vAI/dAI and anterior cingulate cortex. RESULTS: Resting-state vAI/dAI networks overlapped with the prototypical salience network, yet they possessed distinct patterns, linking the vAI with nodes of the internally focused default mode network and the dAI with nodes of the external, goal-related frontoparietal network. Internally generated baseline craving was associated with enhanced vAI connectivity, whereas rostral anterior cingulate cortex reactivity to external smoking cues was associated with greater dAI connectivity. We also found that cue reactivity in the rostral anterior cingulate cortex was associated with a rise in subjective cue-induced craving, whereas baseline subjective craving did not influence brain cue reactivity. CONCLUSIONS: These data show that brain reactivity to smoking cues is associated with a subsequent increase in craving. In addition, separate insula networks have a role in an individual's vulnerability to internally related craving and externally triggered cue reactivity, which could guide the development of new, neurobiologically targeted therapies.

Low Frequency Systemic Hemodynamic "Noise" in Resting State BOLD fMRI: Characteristics, Causes, Implications, Mitigation Strategies, and Applications.

Advances in functional magnetic resonance imaging (fMRI) acquisition have improved signal to noise to the point where the physiology of the subject is the dominant noise source in resting state fMRI data (rsfMRI). Among these systemic, non-neuronal physiological signals, respiration and to some degree cardiac fluctuations can be removed through modeling, or in the case of newer, faster acquisitions such as simultaneous multislice acquisition, simple spectral filtering. However, significant low frequency physiological oscillation (∼0.01-0.15 Hz) remains in the signal. This is problematic, as it is the precise frequency band occupied by the neuronally modulated hemodynamic responses used to study brain connectivity, precluding its removal by spectral filtering. The source of this signal, and its method of production and propagation in the body, have not been conclusively determined. Here, we summarize the defining characteristics of the systemic low frequency noise signal, and review some current theories about the signal source and the evidence supporting them. The strength and distribution of the systemic LFO signal make characterizing and removing it essential for accurate quantification, especially for resting state connectivity, when no stimulation can be compared with the signal. Widespread correlated non-neuronal signals obscure and distort the more localized patterns of neuronal correlations between interacting brain regions; they may even cause apparent connectivity between regions with no neuronal interaction. Here, we discuss a simple method we have developed to parse the global, moving, blood-borne signal from the stationary, neuronal connectivity signals, substantially reducing the negative correlations that result from global signal regression. Finally, we will discuss some of the uses to which the moving systemic low frequency oscillation can be put if we consider it a "signal" carrying information, rather than simply "noise" complicating the interpretation of resting state connectivity. Properly utilizing this signal may offer insights into subtle hemodynamic alterations that can be used as early indicators of circulatory dysfunction in a number of neuropsychiatric conditions, such as prodromal stroke, moyamoya, and Alzheimer's disease.

Resting-state functional connectivity, cortical GABA, and neuroactive steroids in peripartum and peripartum depressed women: a functional magnetic resonance imaging and spectroscopy study.

Postpartum depression (PPD) is associated with abnormalities in resting-state functional connectivity (RSFC) but the underlying neurochemistry is unclear. We hypothesized that peripartum GABAergic neuroactive steroids (NAS) are related to cortical GABA concentrations and RSFC in PPD as compared to healthy comparison women (HCW). To test this, we measured RSFC with fMRI and GABA+/Creatine (Cr) concentrations with proton magnetic resonance spectroscopy (1H MRS) in the pregenual anterior cingulate (pgACC) and occipital cortices (OCC) and quantified peripartum plasma NAS. We examined between-group differences in RSFC and the relationship between cortical GABA+/Cr concentrations with RSFC. We investigated the relationship between NAS, RSFC and cortical GABA+/Cr concentrations. Within the default mode network (DMN) an area of the dorsomedial prefrontal cortex (DMPFC) had greater connectivity with the rest of the DMN in PPD (peak voxel: MNI coordinates (2, 58, 32), p = 0.002) and was correlated to depression scores (peak HAM-D17 voxel: MNI coordinates (0, 60, 34), p = 0.008). pgACC GABA+/Cr correlated positively with DMPFC RSFC in a region spanning the right anterior/posterior insula and right temporal pole (r = +0.661, p = 0.000). OCC GABA+/Cr correlated positively with regions spanning both amygdalae (right amygdala: r = +0.522, p = 0.000; left amygdala: r = +0.651, p = 0.000) as well as superior parietal areas. Plasma allopregnanolone was higher in PPD (p = 0.03) and positively correlated with intra DMPFC connectivity (r = +0.548, p = 0.000) but not GABA+/Cr. These results provide initial evidence that PPD is associated with altered DMN connectivity; cortical GABA+/Cr concentrations are associated with postpartum RSFC and allopregnanolone is associated with postpartum intra-DMPFC connectivity.

Memory retrieval of smoking-related images induce greater insula activation as revealed by an fMRI-based delayed matching to sample task.

Nicotine dependence is a chronic and difficult to treat disorder. While environmental stimuli associated with smoking precipitate craving and relapse, it is unknown whether smoking cues are cognitively processed differently than neutral stimuli. To evaluate working memory differences between smoking-related and neutral stimuli, we conducted a delay-match-to-sample (DMS) task concurrently with functional magnetic resonance imaging (fMRI) in nicotine-dependent participants. The DMS task evaluates brain activation during the encoding, maintenance and retrieval phases of working memory. Smoking images induced significantly more subjective craving, and greater midline cortical activation during encoding in comparison to neutral stimuli that were similar in content yet lacked a smoking component. The insula, which is involved in maintaining nicotine dependence, was active during the successful retrieval of previously viewed smoking versus neutral images. In contrast, neutral images required more prefrontal cortex-mediated active maintenance during the maintenance period. These findings indicate that distinct brain regions are involved in the different phases of working memory for smoking-related versus neutral images. Importantly, the results implicate the insula in the retrieval of smoking-related stimuli, which is relevant given the insula's emerging role in addiction.

Functional neuroimaging of reward circuitry responsivity to monetary gains and losses in posttraumatic stress disorder.

BACKGROUND: Clinical impressions and preclinical work suggest that posttraumatic stress disorder (PTSD) might be associated with dysfunctional reward processing. To pursue this issue, we administered a validated passive-viewing monetary reward task during functional magnetic resonance imaging (fMRI) to subjects with chronic PTSD and to mentally healthy individuals. METHODS: The protocol evaluated fMRI signal changes that anticipated or accompanied monetary gains and losses under varying conditions of controlled expectation. The "expectancy phase" entailed presentation of a promising, unpromising, or intermediate Wheel of Fortune-type spinner, whereas the "outcome phase" was defined by the arrow landing on one of three sectors of that spinner, thereby determining the subjects' gain or loss for that trial. RESULTS: Neuroimaging data from 20 PTSD and 26 healthy subjects withstood quality control procedures and were included. In voxelwise and anatomically defined region-of-interest analyses, when gains were contrasted to losses, between-group comparison revealed smaller bilateral striatal activations in the PTSD subjects. In the PTSD group, less striatal activation to gains versus losses was associated with more self-reported motivational and social deficits. CONCLUSIONS: The present data support the hypothesis that PTSD is associated with abnormal processing of monetary outcomes and that this alteration might be related to some aspects of emotional numbing.

Activation of suprachiasmatic nuclei and primary visual cortex depends upon time of day.

The human suprachiasmatic nucleus (SCN), the master biological clock, is a small (approximately 2 mm(3)) and deep structure located in the anterior hypothalamus. Previous methods do not allow in vivo study of the human SCN in a non-invasive manner. Therefore, we explored blood oxygen level-dependent (BOLD)-functional magnetic resonance imaging (fMRI) with OFF-ON-OFF block-designed visual stimuli to record the activities in the 'SCN and peri SCN in the anterior hypothalamus' (SCN+) and the primary visual area V1 using a 3T Siemens scanner and six normal subjects. We found that: (i) the BOLD-fMRI response to light and the mean of percentage activation in the SCN+ at midday was significantly less than that at night; and (ii) the number of activated voxels in most of the visual area V1 at midday was significantly higher than that at night. We conclude that BOLD-fMRI responses to light in the SCN+ and the V1 areas vary with time of day. This conclusion is consistent with: (i) the previously measured phase-response curve to light [J. Physiol., 549.3 (2003) 945] for the SCN activity at critical intensity threshold; and (ii) the interaction of the melanopsin-based signals with the rod-cone signals at the 'giant' retinal ganglion cells [Nature, 433 (2005) 749] for the V1 activity.

Brain, skull, and cerebrospinal fluid volumes in adult posttraumatic stress disorder.

Children and adolescents with maltreatment-related posttraumatic stress disorder (PTSD) exhibit smaller intracranial tissue volume than controls. Linear relationships have also been observed between intracranial tissue volume and the age of maltreatment onset. The authors explored associations among adult PTSD, early trauma, and cerebral volumes in 99 combat veterans. A bone-based estimate of cranial volume was developed to adjust for variation in body size. Posttraumatic stress disorder was not associated with smaller cerebral tissue volume, but rather with smaller cerebrospinal fluid (CSF) and cranial volumes. These findings co-occurred with expected effects of alcoholism and aging on cerebral tissue and CSF volumes. The results point to early developmental divergences between groups with and without PTSD following adult trauma.

Brain changes to hypocapnia using rapidly interleaved phosphorus-proton magnetic resonance spectroscopy at 4 T.

Substantial controversy persists in the literature concerning the physiologic consequences hypocapnia, or low partial pressure of carbon dioxide (PaCO(2)). Invasive animal studies have demonstrated large pH increases (>0.25 U), phosphocreatine (PCr) decreases (>30%), and adenosine triphosphate (ATP) decreases (>10%) after hyperventilation (HV) (20 mm Hg PaCO(2)). However, using magnetic resonance spectroscopy, HV studies in awake humans have demonstrated only small pH changes ( approximately 0.05 U) and no changes in PCr or ATP. It remains important to ascertain whether this failure to detect PCr changes in human studies reflects a true absence of changes, or a limitation in data fidelity. The present study used a rapidly interleaved phosphorus-proton spectroscopy acquisition from large samples at high magnetic field (4 T), to measure pH, PCr, inorganic phosphate, beta-ATP, and lactate changes with high temporal and signal sensitivity. Five of six subjects had usable data. During 20 mins HV, PaCO(2) reached a minimum at 16 mins (17 mm Hg); however, the maximum pH change (+0.047) peaked earlier (14 mins). Maximal lactate increases were measured at 15 mins. By 10 mins, maximum changes were observed for PCr (-3.4%) and inorganic phosphate (+6.4%). No changes in beta-ATP were observed. The peak in pH, despite continued decreases in PaCO(2), suggests active buffering during HV. These data, and the small magnitude of early PCr and inorganic phosphate changes, do not support substantial energy compromise during HV. Other mitigating factors, such as anesthesia-induced deregulation of the cerebrovasculature, might have contributed to the exaggerated metabolic changes observed in previous animal investigations.