Whole-brain Activity Research Articles

Neural correlates of extinguished threat recall underlying the commonality between pediatric anxiety and irritability

BackgroundAnxiety and irritability frequently co-occur in youth and are mediated by aberrant threat responses. However, empirical evidence on neural mechanisms underlying this co-occurrence is limited. To address this, we apply data-driven latent phenotyping to data from a prior report of a well-validated threat extinction recall fMRI paradigm. MethodsParticipants included 59 youth (28 anxiety disorder, 31 healthy volunteers; Mage=13.15 yrs) drawn from a transdiagnostic sample of 331 youth, in which bifactor analysis was conducted to derive latent factors representing shared vs. unique variance of dimensionally-assessed anxiety and irritability. Participants underwent threat conditioning and extinction. Approximately three weeks later, during extinction recall fMRI, participants made threat-safety discriminations under two task conditions: current threat appraisal and explicit recall of threat contingencies. Linear mixed-effects analyses examined associations of a “negative affectivity” factor reflecting shared anxiety and irritability variance with whole-brain activation and task-dependent amygdala connectivity. ResultsDuring recall of threat-safety contingencies, higher negative affectivity was associated with greater prefrontal (ventrolateral/ventromedial, dorsolateral, orbitofrontal), motor, temporal, parietal, and occipital activation. During threat appraisal, higher negative affectivity was associated with greater amygdala-inferior parietal lobule connectivity to threat/safety ambiguity. LimitationsSample included only healthy youth and youth with anxiety disorders. Results may not generalize to other diagnoses for which anxiety and irritability are also common, and our negative affectivity factor should be interpreted as anxiety disorders with elevated irritability. Reliability of some subfactors was poor. ConclusionsAberrant amygdala-prefrontal-parietal circuitry during extinction recall of threat-safety stimuli may be a mechanism underlying the co-occurrence of pediatric anxiety and irritability.

Stimulus Reliability Automatically Biases Temporal Integration of Discrete Perceptual Targets in the Human Brain.

Many decisions, from crossing a busy street to choosing a profession, require integration of discrete sensory events. Previous studies have shown that integrative decision-making favors more reliable stimuli, mimicking statistically optimal integration. It remains unclear, however, whether reliability biases operate even when they lead to suboptimal performance. To address this issue, we asked human observers to reproduce the average motion direction of two suprathreshold coherent motion signals presented successively and with varying levels of reliability, while simultaneously recording whole-brain activity using electroencephalography. By definition, the averaging task should engender equal weighting of the two component motion signals, but instead we found robust behavioral biases in participants' average decisions that favored the more reliable stimulus. Using population-tuning modeling of brain activity we characterized tuning to the average motion direction. In keeping with the behavioral biases, the neural tuning profiles also exhibited reliability biases. A control experiment revealed that observers were able to reproduce motion directions of low and high reliability with equal precision, suggesting that unbiased integration in this task was not only theoretically optimal but demonstrably possible. Our findings reveal that temporal integration of discrete sensory events in the brain is automatically and suboptimally weighted according to stimulus reliability.SIGNIFICANCE STATEMENT Many everyday decisions require integration of several sources of information. To safely cross a busy road, for example, one must consider the movement of vehicles with different speeds and trajectories. Previous research has shown that individual stimuli are weighted according to their reliability. Whereas reliability biases typically yield performance that closely mimics statistically optimal integration, it remains unknown whether such biases arise even when they lead to suboptimal performance. Here we combined a novel integrative decision-making task with concurrent brain recording and modeling to address this question. While unbiased decisions were optimal in the task, observers nevertheless exhibited robust reliability biases in both behavior and brain activity, suggesting that reliability-weighted integration is automatic and dissociable from statistically optimal integration.

Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness.

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

Altered Spontaneous Neural Activity and Functional Connectivity in Parkinson’s Disease With Subthalamic Microlesion

BackgroundTransient improvement in motor symptoms are immediately observed in patients with Parkinson’s disease (PD) after an electrode has been implanted into the subthalamic nucleus (STN) for deep brain stimulation (DBS). This phenomenon is known as the microlesion effect (MLE). However, the underlying mechanisms of MLE is poorly understood.PurposeWe utilized resting state functional MRI (rs-fMRI) to evaluate changes in spontaneous brain activity and networks in PD patients during the microlesion period after DBS.MethodOverall, 37 PD patients and 13 gender- and age-matched healthy controls (HCs) were recruited for this study. Rs-MRI information was collected from PD patients three days before DBS and one day after DBS, whereas the HCs group was scanned once. We utilized the amplitude of low-frequency fluctuation (ALFF) method in order to analyze differences in spontaneous whole-brain activity among all subjects. Furthermore, functional connectivity (FC) was applied to investigate connections between other brain regions and brain areas with significantly different ALFF before and after surgery in PD patients.ResultRelative to the PD-Pre-DBS group, the PD-Post-DBS group had higher ALFF in the right putamen, right inferior frontal gyrus, right precentral gyrus and lower ALFF in right angular gyrus, right precuneus, right posterior cingulate gyrus (PCC), left insula, left middle temporal gyrus (MTG), bilateral middle frontal gyrus and bilateral superior frontal gyrus (dorsolateral). Functional connectivity analysis revealed that these brain regions with significantly different ALFF scores demonstrated abnormal FC, largely in the temporal, prefrontal cortices and default mode network (DMN).ConclusionThe subthalamic microlesion caused by DBS in PD was found to not only improve the activity of the basal ganglia-thalamocortical circuit, but also reduce the activity of the DMN and executive control network (ECN) related brain regions. Results from this study provide new insights into the mechanism of MLE.

The structural connectome constrains fast brain dynamics.

Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.

Neural processing of emotional facial stimuli in specific phobia: An fMRI study.

Patients with specific phobia (SP) show altered brain activation when confronted with phobia-specific stimuli. It is unclear whether this pathogenic activation pattern generalizes to other emotional stimuli. This study addresses this question by employing a well-powered sample while implementing an established paradigm using nonspecific aversive facial stimuli. N = 111 patients with SP, spider subtype, and N = 111 healthy controls (HCs) performed a supraliminal emotional face-matching paradigm contrasting aversive faces versus shapes in a 3-Tmagnetic resonance imaging scanner. We performed region of interest (ROI) analyses for the amygdala, the insula, and the anterior cingulate cortex using univariate as well as machine-learning-based multivariate statistics based on this data. Additionally, we investigated functional connectivity by means of psychophysiological interaction (PPI). Althoughthe presentation of emotional faces showed significant activation in all three ROIs across both groups, no group differences emerged in all ROIs. Across both groups and in the HC > SP contrast, PPI analyses showed significant task-related connectivity of brain areas typically linked to higher-order emotion processing with the amygdala. The machine learning approach based on whole-brain activity patterns could significantly differentiate the groups with 73% balanced accuracy. Patients suffering from SP are characterized by differences in the connectivity of the amygdala and areas typically linked to emotional processing in response to aversive facial stimuli (inferior parietal cortex, fusiform gyrus, middle cingulate, postcentral cortex, and insula). This might implicate a subtle difference in the processing of nonspecific emotional stimuli and warrants more research furthering our understanding of neurofunctional alteration in patients with SP.

FMRI lag structure during waking up from early sleep stages

The brain mechanisms by which we transition from sleep to a conscious state remain largely unknown in humans, partly because of methodological challenges. Here we study a pre-existing dataset of waking up participants originally designed for a study of dreaming (Horikawa, Tamaki, Miyawaki, & Kamitani, 2013) and suggest that suddenly awakening from early sleep stages results from a two-stage process that involves a sequence of cortical and subcortical brain activity. First, subcortical and sensorimotor structures seem to be recruited before most cortical regions, followed by fast, ignition-like whole-brain activation—with frontal regions engaging a little after the rest of the brain. Second, a comparably slower and possibly mirror-reversed stage might take place, with cortical regions activating before subcortical structures and the cerebellum. This pattern of activation points to a key role of subcortical structures for the initiation and maintenance of conscious states.

Multiple Functional Brain Networks Related to Pain Perception Revealed by fMRI

The rise of functional magnetic resonance imaging (fMRI) has led to a deeper understanding of cortical processing of pain. Central to these advances has been the identification and analysis of “functional networks”, often derived from groups of pre-selected pain regions. In this study our main objective was to identify functional brain networks related to pain perception by examining whole-brain activation, avoiding the need for a priori selection of regions. We applied a data-driven technique—Constrained Principal Component Analysis for fMRI (fMRI-CPCA)—that identifies networks without assuming their anatomical or temporal properties. Open-source fMRI data collected during a thermal pain task (33 healthy participants) were subjected to fMRI-CPCA for network extraction, and networks were associated with pain perception by modelling subjective pain ratings as a function of network activation intensities. Three functional networks emerged: a sensorimotor response network, a salience-mediated attention network, and the default-mode network. Together, these networks constituted a brain state that explained variability in pain perception, both within and between individuals, demonstrating the potential of data-driven, whole-brain functional network techniques for the analysis of pain imaging data.

Simplicial and topological descriptions of human brain dynamics

While brain imaging tools like functional magnetic resonance imaging (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the data’s apparent self-organization. To clarify how patterns of brain activity support brain function, one might identify metric spaces that optimally distinguish brain states across experimentally defined conditions. Therefore, the present study considers the relative capacities of several metric spaces to disambiguate experimentally defined brain states. One fundamental metric space interprets fMRI data topographically, that is, as the vector of amplitudes of a multivariate signal, changing with time. Another perspective compares the brain’s functional connectivity, that is, the similarity matrix computed between signals from different brain regions. More recently, metric spaces that consider the data’s topology have become available. Such methods treat data as a sample drawn from an abstract geometric object. To recover the structure of that object, topological data analysis detects features that are invariant under continuous deformations (such as coordinate rotation and nodal misalignment). Moreover, the methods explicitly consider features that persist across multiple geometric scales. While, certainly, there are strengths and weaknesses of each brain dynamics metric space, wefind that those that track topological features optimally distinguish experimentally defined brain states.

Mapping functional gradients of the striatal circuit using simultaneous microelectric stimulation and ultrahigh-field fMRI in non-human primates

Advances in functional magnetic resonance imaging (fMRI) have significantly enhanced our understanding of the striatal system of both humans and non-human primates (NHP) over the last few decades. However, its circuit-level functional anatomy remains poorly understood, partly because in-vivo fMRI cannot directly perturb a brain system and map its casual input-output relationship. Also, routine 3T fMRI has an insufficient spatial resolution. We performed electrical microstimulation (EM) of the striatum in lightly-anesthetized NHPs while simultaneously mapping whole-brain activation, using contrast-enhanced fMRI at ultra-high-field 7T. By stimulating multiple positions along the striatum's main (dorsal-to-ventral) axis, we revealed its complex functional circuit concerning mutually connected subsystems in both cortical and subcortical areas. Indeed, within the striatum, there were distinct brain activation patterns across different stimulation sites. Specifically, dorsal stimulation revealed a medial-to-lateral elongated shape of activation in upper caudate and putamen areas, whereas ventral stimulation evoked areas confined to the medial and lower caudate. Such dorsoventral gradients also appeared in neocortical and thalamic activations, indicating consistent embedding profiles of the striatal system across the whole brain. These findings reflect different forms of within-circuit and inter-regional neuronal connectivity between the dorsal and ventromedial striatum. These patterns both shared and contrasted with previous anatomical tract-tracing and in-vivo resting-state fMRI studies. Our approach of combining microstimulation and whole-brain fMRI mapping in NHPs provides a unique opportunity to integrate our understanding of a targeted brain area's meso- and macro-scale functional systems.

Changes in neural reward processing following Amplification of Positivity treatment for depression and anxiety: Preliminary findings from a randomized waitlist controlled trial

Positive valence system (PVS) deficits are increasingly recognized as important treatment targets for depression and anxiety. Emerging behavioral treatments designed to upregulate the PVS show initial promise; however, neural mechanisms underlying these approaches remain unknown. This study investigated neural reward-processing-related changes following Amplification of Positivity (AMP)—a treatment designed to enhance positive thinking, emotions and behaviors through positive activity interventions (Clinicaltrials.gov: NCT02330627).Individuals with depression and/or anxiety (N = 29) were randomized to 10 sessions of AMP (n = 16) or waitlist (WL; n = 13). Participants completed a monetary incentive delay task during fMRI at baseline and post-assessment. Hypothesis-driven region of interest (ventral striatum, insula, anterior cingulate) and exploratory whole-brain activation and connectivity analyses evaluated pre-to-post changes for AMP vs. WL when anticipating potential monetary gain or loss.No between-group brain activation changes emerged in regions of interest or whole-brain analyses. Increased neural connectivity from pre-to-post-treatment was observed in AMP vs. WL, including ventral striatum, anterior insula, and anterior cingulate connectivity with prefrontal, limbic, occipital and parietal regions—predominantly during loss anticipation.This preliminary study is the first to examine neural mechanisms of positive activity interventions in depression and anxiety and suggests that AMP may strengthen brain connectivity in reward processing, attention, and emotion regulation networks.

Attentive brain states in infants with and without later autism

Early difficulties in engaging attentive brain states in social settings could affect learning and have cascading effects on social development. We investigated this possibility using multichannel electroencephalography during a face/non-face paradigm in 8-month-old infants with (FH, n = 91) and without (noFH, n = 40) a family history of autism spectrum disorder (ASD). An event-related potential component reflecting attention engagement, the Nc, was compared between FH infants who received a diagnosis of ASD at 3 years of age (FH-ASD; n = 19), FH infants who did not (FH-noASD; n = 72) and noFH infants (who also did not, hereafter noFH-noASD; n = 40). ‘Prototypical’ microstates during social attention were extracted from the noFH-noASD group and examined in relation to later categorical and dimensional outcome. Machine-learning was used to identify the microstate features that best predicted ASD and social adaptive skills at three years. Results suggested that whilst measures of brain state timing were related to categorical ASD outcome, brain state strength was related to dimensional measures of social functioning. Specifically, the FH-ASD group showed shorter Nc latency relative to other groups, and duration of the attentive microstate responses to faces was informative for categorical outcome prediction. Reduced Nc amplitude difference between faces with direct gaze and a non-social control stimulus and strength of the attentive microstate to faces contributed to the prediction of dimensional variation in social skills. Taken together, this provides consistent evidence that atypical attention engagement precedes the emergence of difficulties in socialization and indicates that using the spatio-temporal characteristics of whole-brain activation to define brain states in infancy provides an important new approach to understanding of the neurodevelopmental mechanisms that lead to ASD.

Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models.

Segregation and integration are two fundamental principles of brain structural and functional organization. Neuroimaging studies have shown that the brain transits between different functionally segregated and integrated states, and neuromodulatory systems have been proposed as key to facilitate these transitions. Although whole-brain computational models have reproduced this neuromodulatory effect, the role of local inhibitory circuits and their cholinergic modulation has not been studied. In this article, we consider a Jansen & Rit whole-brain model in a network interconnected using a human connectome, and study the influence of the cholinergic and noradrenergic neuromodulatory systems on the segregation/integration balance. In our model, we introduce a local inhibitory feedback as a plausible biophysical mechanism that enables the integration of whole-brain activity, and that interacts with the other neuromodulatory influences to facilitate the transition between different functional segregation/integration regimes in the brain.

Disrupted Neural Activity in Individuals With Iridocyclitis Using Regional Homogeneity: A Resting-State Functional Magnetic Resonance Imaging Study.

Objective: This study used the regional homogeneity (ReHo) technique to explore whether spontaneous brain activity is altered in patients with iridocyclitis.Methods: Twenty-six patients with iridocyclitis (14 men and 12 women) and 26 healthy volunteers (15 men and 11 women) matched for sex and age were enrolled in this study. The ReHo technique was used to comprehensively assess changes in whole-brain synchronous neuronal activity. The diagnostic ability of the ReHo method was evaluated by means of receive operating characteristic (ROC) curve analysis. Moreover, associations of average ReHo values in different brain areas and clinical characteristics were analyzed using correlation analysis.Result: Compared with healthy volunteers, reduced ReHo values were observed in patients with iridocyclitis in the following brain regions: the right inferior occipital gyrus, bilateral calcarine, right middle temporal gyrus, right postcentral gyrus, left superior occipital gyrus, and left precuneus. In contrast, ReHo values were significantly enhanced in the right cerebellum, left putamen, left supplementary motor area, and left inferior frontal gyrus in patients with iridocyclitis, compared with healthy volunteers (false discovery rate correction, P < 0.05).Conclusion: Patients with iridocyclitis exhibited disturbed synchronous neural activities in specific brain areas, including the visual, motor, and somatosensory regions, as well as the default mode network. These findings offer a novel image-guided research strategy that might aid in exploration of neuropathological or compensatory mechanisms in patients with iridocyclitis.

Noise-driven multistability vs deterministic chaos in phenomenological semi-empirical models of whole-brain activity.

An outstanding open problem in neuroscience is to understand how neural systems are capable of producing and sustaining complex spatiotemporal dynamics. Computational models that combine local dynamics with in vivo measurements of anatomical and functional connectivity can be used to test potential mechanisms underlying this complexity. We compared two conceptually different mechanisms: noise-driven switching between equilibrium solutions (modeled by coupled Stuart-Landau oscillators) and deterministic chaos (modeled by coupled Rossler oscillators). We found that both models struggled to simultaneously reproduce multiple observables computed from the empirical data. This issue was especially manifested in the case of noise-driven dynamics close to a bifurcation, which imposed overly strong constraints on the optimal model parameters. In contrast, the chaotic model could produce complex behavior over a range of parameters, thus being capable of capturing multiple observables at the same time with good performance. Our observations support the view of the brain as a non-equilibrium system able to produce endogenous variability. We presented a simple model capable of jointly reproducing functional connectivity computed at different temporal scales. Besides adding to our conceptual understanding of brain complexity, our results inform and constrain the future development of biophysically realistic large-scale models.

Whole-brain activation signatures of weight-lowering drugs

ObjectiveThe development of effective anti-obesity therapeutics relies heavily on the ability to target specific brain homeostatic and hedonic mechanisms controlling body weight. To obtain further insight into neurocircuits recruited by anti-obesity drug treatment, the present study aimed to determine whole-brain activation signatures of six different weight-lowering drug classes. MethodsChow-fed C57BL/6J mice (n = 8 per group) received acute treatment with lorcaserin (7 mg/kg; i.p.), rimonabant (10 mg/kg; i.p.), bromocriptine (10 mg/kg; i.p.), sibutramine (10 mg/kg; p.o.), semaglutide (0.04 mg/kg; s.c.) or setmelanotide (4 mg/kg; s.c.). Brains were sampled two hours post-dosing and whole-brain neuronal activation patterns were analysed at single-cell resolution using c-Fos immunohistochemistry and automated quantitative three-dimensional (3D) imaging. ResultsThe whole-brain analysis comprised 308 atlas-defined mouse brain areas. To enable fast and efficient data mining, a web-based 3D imaging data viewer was developed. All weight-lowering drugs demonstrated brain-wide responses with notable similarities in c-Fos expression signatures. Overlapping c-Fos responses were detected in discrete homeostatic and non-homeostatic feeding centres located in the dorsal vagal complex and hypothalamus with concurrent activation of several limbic structures as well as the dopaminergic system. ConclusionsWhole-brain c-Fos expression signatures of various weight-lowering drug classes point to a discrete set of brain regions and neurocircuits which could represent key neuroanatomical targets for future anti-obesity therapeutics.

Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome.

Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed "connectome harmonics", have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.

Replay bursts in humans coincide with activation of the default mode and parietal alpha networks

SummaryOur brains at rest spontaneously replay recently acquired information, but how this process is orchestrated to avoid interference with ongoing cognition is an open question. Here we investigated whether replay coincided with spontaneous patterns of whole-brain activity. We found, in two separate datasets, that replay sequences were packaged into transient bursts occurring selectively during activation of the default mode network (DMN) and parietal alpha networks. These networks are believed to support inwardly oriented attention and inhibit bottom-up sensory processing and were characterized by widespread synchronized oscillations coupled to increases in high frequency power, mechanisms thought to coordinate information flow between disparate cortical areas. Our data reveal a tight correspondence between two widely studied phenomena in neural physiology and suggest that the DMN may coordinate replay bursts in a manner that minimizes interference with ongoing cognition.

Novel polygenic risk score as a translational tool linking depression-related changes in the corticolimbic transcriptome with neural face processing and anhedonic symptoms

Convergent data from imaging and postmortem brain transcriptome studies implicate corticolimbic circuit (CLC) dysregulation in the pathophysiology of depression. To more directly bridge these lines of work, we generated a novel transcriptome-based polygenic risk score (T-PRS), capturing subtle shifts toward depression-like gene expression patterns in key CLC regions, and mapped this T-PRS onto brain function and related depressive symptoms in a nonclinical sample of 478 young adults (225 men; age 19.79 +/− 1.24) from the Duke Neurogenetics Study. First, T-PRS was generated based on common functional SNPs shifting CLC gene expression toward a depression-like state. Next, we used multivariate partial least squares regression to map T-PRS onto whole-brain activity patterns during perceptual processing of social stimuli (i.e., human faces). For validation, we conducted a comparative analysis with a PRS summarizing depression risk variants identified by the Psychiatric Genomics Consortium (PGC-PRS). Sex was modeled as moderating factor. We showed that T-PRS was associated with widespread reductions in neural response to neutral faces in women and increases in neural response to emotional faces and shapes in men (multivariate p < 0.01). This female-specific reductions in neural response to neutral faces was also associated with PGC-PRS (multivariate p < 0.03). Reduced reactivity to neutral faces was further associated with increased self-reported anhedonia. We conclude that women with functional alleles mimicking the postmortem transcriptomic CLC signature of depression have blunted neural activity to social stimuli, which may be expressed as higher anhedonia.

Maturation of Auditory Cortex Neural Activity in Children and Implications for Auditory Clinical Markers in Diagnosis.

Functional brain markers that can inform research on brain abnormalities, and especially those ready to facilitate clinical work on such abnormalities, will need to show not only considerable sensitivity and specificity but enough consistency with respect to developmental course that their validity in individual cases can be trusted. A challenge to establishing such markers may be individual differences in developmental course. The present study examined auditory cortex activity in children at an age when developmental changes to the auditory cortex 50 ms (M50) and 100 ms (M100) components are prominent to better understand the use of auditory markers in pediatric clinical research. MEG auditory encoding measures (auditory evoked fields in response to pure tone stimuli) were obtained from 15 typically developing children 6–8 years old, with measures repeated 18 and 36 months after the initial exam. MEG analyses were conducted in source space (i.e., brain location), with M50 and M100 sources identified in left and right primary/secondary auditory cortex (Heschl's gyrus). A left and right M50 response was observed at all times (Time 1, Time 2, Time 3), with M50 latency (collapsing across hemisphere) at Time 3 (77 ms) 10 ms earlier than Time 1 (87 ms; p < 0.001) and with M50 responses on average (collapsing across time) 5 ms earlier in the right (80 ms) than left hemisphere (85 ms; p < 0.05). In the majority of children, however, M50 latency changes were not constant across the three-year period; for example, whereas in some children a ~10 ms latency reduction was observed from Time 1 to Time 2, in other children a ~10 ms latency reduction was observed from Time 2 to Time 3. M100 responses were defined by a significant “peak” of detected power with magnetic field topography opposite M50 and occurring 50–100 ms later than the M50. Although M100s were observed in a few children at Time 1 and Time 2 (and more often in the right than left hemisphere), M100s were not observed in the majority of children except in the right hemisphere at Time 3. In sum, longitudinal findings showed large between- and within-subject variability in rate of change as well as time to reach neural developmental milestones (e.g., presence of a detectable M100 response). Findings also demonstrated the need to examine whole-brain activity, given hemisphere differences in the rate of auditory cortex maturation. Pediatric research will need to take such normal variability into account when seeking clinical auditory markers.