Consciousness among delta waves: a paradox?

A common observation in EEG research is that consciousness vanishes with the appearance of delta (1 - 4 Hz) waves, particularly when those waves are high amplitude. High amplitude delta oscillations are very frequently observed in states of diminished consciousness, including slow wave sleep, anaesthesia, generalised epileptic seizures, and disorders of consciousness such as coma and vegetative state. This strong correlation between loss of consciousness and high amplitude delta oscillations is thought to stem from the widespread cortical deactivation that occurs during the "down states" or troughs of these slow oscillations. Recently, however, many studies have reported the presence of prominent delta activity during conscious states, which casts doubt on the hypothesis that high amplitude delta oscillations are an indicator of unconsciousness. These studies include work in Angelman syndrome, epilepsy, behavioural responsiveness during propofol anaesthesia, postoperative delirium, and states of dissociation from the environment such as dreaming and powerful psychedelic states. The foregoing studies complement an older, yet largely unacknowledged, body of literature that has documented awake, conscious patients with high amplitude delta oscillations in clinical reports from Rett syndrome, Lennox-Gastaut syndrome, schizophrenia, mitochondrial diseases, hepatic encephalopathy, and nonconvulsive status epilepticus. At the same time, a largely parallel body of recent work has reported convincing evidence that the complexity or entropy of EEG and magnetoencephalogram or MEG signals strongly relates to an individual's level of consciousness. Having reviewed this literature, we discuss plausible mechanisms that would resolve the seeming contradiction between high amplitude delta oscillations and consciousness. We also consider implications concerning theories of consciousness, such as integrated information theory and the entropic brain hypothesis. Finally, we conclude that false inferences of unconscious states can be best avoided by examining measures of electrophysiological complexity in addition to spectral power.

Longitudinal changes in blood-based biomarkers in chronic moderate to severe traumatic brain injury: preliminary findings

Objectives: This longitudinal study aims at 1) providing preliminary evidence of changes in blood-based biomarkers across time in chronic TBI and 2) relating these changes to outcome measures and cerebral structure and activity.Methods: Eight patients with moderate-to-severe TBI (7 males, 35 ± 7.6 years old, 5 severe TBI, 17.52 ± 3.84 months post-injury) were evaluated at monthly intervals across 6 time-points using: a) Blood-based biomarkers (GFAP, NSE, S100A12, SDBP145, UCH-L1, T-tau, P-tau, P-tau/T-tau ratio); b) Magnetic Resonance Imaging to evaluate changes in brain structure; c) Resting-state electroencephalograms to evaluate changes in brain function; and d) Outcome measures to assess cognition, emotion, and functional recovery (MOCA, RBANS, BDI-II, and DRS).Results: Changes in P-tau levels were found across time [p = .007]. P-tau was positively related to functional [p < .001] and cognitive [p = .006] outcomes, and negatively related to the severity of depression, 6 months later [R = -0.901; p =.006]. P-tau and P-tau/T-tau ratio were also positively correlated to shape change in subcortical areas such as brainstem [T(7) = 4.71, p = .008] and putamen [T(7) = 3.25, p = .012].Conclusions: Our study provides preliminary findings that suggest a positive relationship between P-tau and the recovery of patients with chronic TBI.

Functional Segregation of Human Brain Networks Across the Lifespan: An Exploratory Analysis of Static and Dynamic Resting-State Functional Connectivity

Prior research has shown that during development, there is increased segregation between, and increased integration within, prototypical resting-state functional brain networks. Functional networks are typically defined by static functional connectivity over extended periods of rest. However, little is known about how time-varying properties of functional networks change with age. Likewise, a comparison of standard approaches to functional connectivity may provide a nuanced view of how network integration and segregation are reflected across the lifespan. Therefore, this exploratory study evaluated common approaches to static and dynamic functional network connectivity in a publicly available dataset of subjects ranging from 8 to 75 years of age. Analyses evaluated relationships between age and static resting-state functional connectivity, variability (standard deviation) of connectivity, and mean dwell time of functional network states defined by recurring patterns of whole-brain connectivity. Results showed that older age was associated with decreased static connectivity between nodes of different canonical networks, particularly between the visual system and nodes in other networks. Age was not significantly related to variability of connectivity. Mean dwell time of a network state reflecting high connectivity between visual regions decreased with age, but older age was also associated with increased mean dwell time of a network state reflecting high connectivity within and between canonical sensorimotor and visual networks. Results support a model of increased network segregation over the lifespan and also highlight potential pathways of top-down regulation among networks.

Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients

Traumatic brain injury (TBI) may cause secondary debilitating problems, such as post-traumatic epilepsy (PTE), which occurs with unprovoked recurrent seizures, months or even years after TBI. Currently, the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) has been enrolling moderate-severe TBI patients with the goal to identify biomarkers of epileptogenesis that may help to prevent seizure occurrence and better understand the mechanism underlying PTE. In this work, we used a novel complex network approach based on segmenting T1-weighted Magnetic Resonance Imaging (MRI) scans in patches of the same dimension (network nodes) and measured pairwise patch similarities using Pearson's correlation (network connections). This network model allowed us to obtain a series of single and multiplex network metrics to comprehensively analyze the different interactions between brain components and capture structural MRI alterations related to seizure development. We used these complex network features to train a Random Forest (RF) classifier and predict, with an accuracy of 70 and a 95% confidence interval of [67, 73%], which subjects from EpiBioS4Rx have had at least one seizure after a TBI. This complex network approach also allowed the identification of the most informative scales and brain areas for the discrimination between the two clinical groups: seizure-free and seizure-affected subjects, demonstrating to be a promising pilot study which, in the future, may serve to identify and validate biomarkers of PTE.

Early brain biomarkers of post-traumatic seizures: initial report of the multicentre epilepsy bioinformatics study for antiepileptogenic therapy (EpiBioS4Rx) prospective study

BACKGROUND

Traumatic brain injury (TBI) causes early seizures and is the leading cause of post-traumatic epilepsy. We prospectively assessed structural imaging biomarkers differentiating patients who develop seizures secondary to TBI from patients who do not.

DESIGN

Multicentre prospective cohort study starting in 2018. Imaging data are acquired around day 14 post-injury, detection of seizure events occurred early (within 1 week) and late (up to 90 days post-TBI).

RESULTS

From a sample of 96 patients surviving moderate-to-severe TBI, we performed shape analysis of local volume deficits in subcortical areas (analysable sample: 57 patients; 35 no seizure, 14 early, 8 late) and cortical ribbon thinning (analysable sample: 46 patients; 29 no seizure, 10 early, 7 late). Right hippocampal volume deficit and inferior temporal cortex thinning demonstrated a significant effect across groups. Additionally, the degree of left frontal and temporal pole thinning, and clinical score at the time of the MRI, could differentiate patients experiencing early seizures from patients not experiencing them with 89% accuracy.

CONCLUSIONS AND RELEVANCE

Although this is an initial report, these data show that specific areas of localised volume deficit, as visible on routine imaging data, are associated with the emergence of seizures after TBI.

Memory in repeat sports-related concussive injury and single-impact traumatic brain injury

Background: Repeat sports-related concussive/subconcussive injury (RC/SCI) is related to memory impairment. Objective & Methods: We sought to determine memory differences between persons with RC/SCI, moderate-to-severe single-impact traumatic brain injury (SI-TBI), and healthy controls. MRI scans from a subsample of participants with SI-TBI were used to identify the neuroanatomical correlates of observed memory process differences between the brain injury groups. Results: Both brain injury groups evidenced worse learning and recall in contrast to controls, although SI-TBI group had poorer memory than the RC/SCI group. Regarding memory process differences, in contrast to controls, the SI-TBI group evidenced difficulties with encoding, consolidation, and retrieval, while the RC/SCI group showed deficits in consolidation and retrieval. Delayed recall was predicted by encoding, with consolidation as a secondary predictor in the SI-TBI group. In the RC/SCI group, delayed recall was only predicted by consolidation. MRI data showed that the consolidation index we used mapped onto hippocampal atrophy. Conclusions: RC/SCI is primarily associated with consolidation deficits, which differs from SI-TBI. Given the role of the hippocampus in memory consolidation and the fact that hyperphosphorylated tau tends to accumulate in the medial temporal lobe in RC/SCI, consolidation deficits may be a cognitive marker of chronic traumatic encephalopathy in athletes.

The subcortical basis of outcome and cognitive impairment in TBI: A longitudinal cohort study

OBJECTIVE

To understand how, biologically, the acute event of traumatic brain injury gives rise to a long-term disease, we address the relationship between evolving cortical and subcortical brain damage and measures of functional outcome and cognitive functioning at 6 months after injury.

METHODS

For this longitudinal analysis, clinical and MRI data were collected in a tertiary neurointensive care setting in a continuous sample of 157 patients surviving moderate to severe traumatic brain injury between 2000 and 2018. For each patient, we collected T1- and T2-weighted MRI data acutely and at the 6-month follow-up, as well as acute measures of injury severity (Glasgow Coma Scale), follow-up measures of functional impairment (Glasgow Outcome Scale-extended), and, in a subset of patients, neuropsychological measures of attention, executive functions, and episodic memory.

RESULTS

In the final cohort of 113 subcortical and 92 cortical datasets that survived (blind) quality control, extensive atrophy was observed over the first 6 months after injury across the brain. However, only atrophy within subcortical regions, particularly in the left thalamus, was associated with functional outcome and neuropsychological measures of attention, executive functions, and episodic memory. Furthermore, when brought together in an analytical model, longitudinal brain measurements could distinguish good from bad outcome with 90% accuracy, whereas acute brain and clinical measurements alone could achieve only 20% accuracy.

CONCLUSION

Despite great injury heterogeneity, secondary thalamic pathology is a measurable minimum common denominator mechanism directly relating biology to clinical measures of outcome and cognitive functioning, potentially linking the acute event and the longer-term disease of traumatic brain injury.

Relational Integration in the Human Brain: A Review and Synthesis

Relational integration is required when multiple explicit representations of relations between entities must be jointly considered to make inferences. We provide an overview of the neural substrate of relational integration in humans and the processes that support it, focusing on work on analogical and deductive reasoning. In addition to neural evidence, we consider behavioral and computational work that has informed neural investigations of the representations of individual relations and of relational integration. In very general terms, evidence from neuroimaging, neuropsychological, and neuromodulatory studies points to a small set of regions (generally left lateralized) that appear to constitute key substrates for component processes of relational integration. These include posterior parietal cortex, implicated in the representation of first-order relations (e.g., A:B); rostrolateral pFC, apparently central in integrating first-order relations so as to generate and/or evaluate higher-order relations (e.g., A:B::C:D); dorsolateral pFC, involved in maintaining relations in working memory; and ventrolateral pFC, implicated in interference control (e.g., inhibiting salient information that competes with relevant relations). Recent work has begun to link computational models of relational representation and reasoning with patterns of neural activity within these brain areas.

Distributed Code for Semantic Relations Predicts Neural Similarity during Analogical Reasoning

The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coarsely coded as links in a semantic network or finely coded as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations are yet to be empirically established. Using sequential presentation of verbal analogies, we compared neural activities in making analogy judgments with predictions derived from alternative computational models of relational dissimilarity to adjudicate among rival accounts of how semantic relations are coded and compared in the brain. We found that a frontoparietal network encodes the three relation types included in the design. A computational model based on semantic relations coded as distributed representations over a pool of abstract relations predicted neural activities for individual relations within the left superior parietal cortex and for second-order comparisons of relations within a broader left-lateralized network.

Accounting for Changing Structure in Functional Network Analysis of TBI Patients

Over the last 15 years, network analysis approaches based on MR data have allowed a renewed understanding of the relationship between brain function architecture and consciousness. Application of this approach to Disorders of Consciousness (DOC) highlights the relationship between specific aspects of network topology and levels of consciousness. Nonetheless, such applications do not acknowledge that DOC patients present with a dramatic level of heterogeneity in structural connectivity (SC) across groups (e.g., etiology, diagnostic categories) and within individual patients (e.g., over time), which possibly affects the level and quality of functional connectivity (FC) patterns that can be expressed. In addition, it is rarely acknowledged that the most frequently employed outcome metrics in the study of brain connectivity (e.g., degree distribution, inter- or intra-resting state network connectivity, and clustering coefficient) are interrelated and cannot be assumed to be independent of each other. We present empirical data showing that, when the two points above are not taken into consideration with an appropriate analytic model, it can lead to a misinterpretation of the role of each outcome metric in the graph's structure and thus misinterpretation of FC results. We show that failing to account for either SC or the inter-relation between outcome measures can lead to inflated false positives (FP) and/or false negatives (FN) in inter- or intra-resting state network connectivity results (defined, respectively, as a positive or negative result in network connectivity that is present when not accounting for SC and/or outcome measure inter-relation, but becomes not significant when accounting for all variables). Overall, we find that unconscious patients have lower rates of FP and FN for within cortical connectivity, lower rates of FN for cortico-subcortical connectivity, and lower rates of FP for within subcortical connectivity. These lower rates in unconscious patients may reflect differences in their triadic closure and SC metrics, which bias the interpretations of the inter- or intra-resting state network connectivity if the SC metrics and triadic closure are not modeled. We suggest that future studies of functional connectivity in DOC patients (i) incorporate where possible SC metrics and (ii) properly account for the intercorrelated nature of outcome variables.

The Curing Coma Campaign: Framing Initial Scientific Challenges-Proceedings of the First Curing Coma Campaign Scientific Advisory Council Meeting

Coma and disordered consciousness are common manifestations of acute neurological conditions and are among the most pervasive and challenging aspects of treatment in neurocritical care. Gaps exist in patient assessment, outcome prognostication, and treatment directed specifically at improving consciousness and cognitive recovery. In 2019, the Neurocritical Care Society (NCS) launched the Curing Coma Campaign in order to address the "grand challenge" of improving the management of patients with coma and decreased consciousness. One of the first steps was to bring together a Scientific Advisory Council including coma scientists, neurointensivists, neurorehabilitationists, and implementation experts in order to address the current scientific landscape and begin to develop a framework on how to move forward. This manuscript describes the proceedings of the first Curing Coma Campaign Scientific Advisory Council meeting which occurred in conjunction with the NCS Annual Meeting in October 2019 in Vancouver. Specifically, three major pillars were identified which should be considered: endotyping of coma and disorders of consciousness, biomarkers, and proof-of-concept clinical trials. Each is summarized with regard to current approach, benefits to the patient, family, and clinicians, and next steps. Integration of these three pillars will be essential to the success of the Curing Coma Campaign as will expanding the "curing coma community" to ensure broad participation of clinicians, scientists, and patient advocates with the goal of identifying and implementing treatments to fundamentally improve the outcome of patients.

A systematic investigation of the association between network dynamics in the human brain and the state of consciousness

An increasing amount of studies suggest that brain dynamics measured with resting-state functional magnetic resonance imaging (fMRI) are related to the state of consciousness. However, the challenge of investigating neuronal correlates of consciousness is the confounding interference between (recovery of) consciousness and behavioral responsiveness. To address this issue, and validate the interpretation of prior work linking brain dynamics and consciousness, we performed a longitudinal fMRI study in patients recovering from coma. Patients were assessed twice, 6 months apart, and assigned to one of two groups. One group included patients who were unconscious at the first assessment but regained consciousness and improved behavioral responsiveness by the second assessment. The other group included patients who were already conscious and improved only behavioral responsiveness. While the two groups were matched in terms of the average increase in behavioral responsiveness, only one group experienced a categorical change in their state of consciousness allowing us to partially dissociate consciousness and behavioral responsiveness. We find the variance in network metrics to be systematically different across states of consciousness, both within and across groups. Specifically, at the first assessment, conscious patients exhibited significantly greater variance in network metrics than unconscious patients, a difference that disappeared once all patients had recovered consciousness. Furthermore, we find a significant increase in dynamics for patients who regained consciousness over time, but not for patients who only improved responsiveness. These findings suggest that changes in brain dynamics are indeed linked to the state of consciousness and not just to a general level of behavioral responsiveness.

High-voltage, diffuse delta rhythms coincide with wakeful consciousness and complexity in Angelman syndrome

Abundant evidence from slow wave sleep, anesthesia, coma, and epileptic seizures links high-voltage, slow electroencephalogram (EEG) activity to loss of consciousness. This well-established correlation is challenged by the observation that children with Angelman syndrome (AS), while fully awake and displaying volitional behavior, display a hypersynchronous delta (1–4 Hz) frequency EEG phenotype typical of unconsciousness. Because the trough of the delta oscillation is associated with down-states in which cortical neurons are silenced, the presence of volitional behavior and wakefulness in AS amidst diffuse delta rhythms presents a paradox. Moreover, high-voltage, slow EEG activity is generally assumed to lack complexity, yet many theories view functional brain complexity as necessary for consciousness. Here, we use abnormal cortical dynamics in AS to assess whether EEG complexity may scale with the relative level of consciousness despite a background of hypersynchronous delta activity. As characterized by multiscale metrics, EEGs from 35 children with AS feature significantly greater complexity during wakefulness compared with sleep, even when comparing the most pathological segments of wakeful EEG to the segments of sleep EEG least likely to contain conscious mentation and when factoring out delta power differences across states. These findings (i) warn against reverse inferring an absence of consciousness solely on the basis of high-amplitude EEG delta oscillations, (ii) corroborate rare observations of preserved consciousness under hypersynchronization in other conditions, (iii) identify biomarkers of consciousness that have been validated under conditions of abnormal cortical dynamics, and (iv) lend credence to theories linking consciousness with complexity.

Imaging biomarkers of posttraumatic epileptogenesis

Traumatic brain injury (TBI) affects 2.5 million people annually within the United States alone, with over 300 000 severe injuries resulting in emergency room visits and hospital admissions. Severe TBI can result in long-term disability. Posttraumatic epilepsy (PTE) is one of the most debilitating consequences of TBI, with an estimated incidence that ranges from 2% to 50% based on severity of injury. Conducting studies of PTE poses many challenges, because many subjects with TBI never develop epilepsy, and it can be more than 10 years after TBI before seizures begin. One of the unmet needs in the study of PTE is an accurate biomarker of epileptogenesis, or a panel of biomarkers, which could provide early insights into which TBI patients are most susceptible to PTE, providing an opportunity for prophylactic anticonvulsant therapy and enabling more efficient large-scale PTE studies. Several recent reviews have provided a comprehensive overview of this subject (Neurobiol Dis, 123, 2019, 3; Neurotherapeutics, 11, 2014, 231). In this review, we describe acute and chronic imaging methods that detect biomarkers for PTE and potential mechanisms of epileptogenesis. We also describe shortcomings in current acquisition methods, analysis, and interpretation that limit ongoing investigations that may be mitigated with advancements in imaging techniques and analysis.

pH-weighted molecular MRI in human traumatic brain injury (TBI) using amine proton chemical exchange saturation transfer echoplanar imaging (CEST EPI)

Cerebral acidosis is a consequence of secondary injury mechanisms following traumatic brain injury (TBI), including excitotoxicity and ischemia, with potentially significant clinical implications. However, there remains an unmet clinical need for technology for non-invasive, high resolution pH imaging of human TBI for studying metabolic changes following injury. The current study examined 17 patients with TBI and 20 healthy controls using amine chemical exchange saturation transfer echoplanar imaging (CEST EPI), a novel pH-weighted molecular MR imaging technique, on a clinical 3T MR scanner. Results showed significantly elevated pH-weighted image contrast (MTRasym at 3 ppm) in areas of T2 hyperintensity or edema (P < 0.0001), and a strong negative correlation with Glasgow Coma Scale (GCS) at the time of the MRI exam (R2 = 0.4777, P = 0.0021), Glasgow Outcome Scale - Extended (GOSE) at 6 months from injury (R2 = 0.5334, P = 0.0107), and a non-linear correlation with the time from injury to MRI exam (R2 = 0.6317, P = 0.0004). This evidence suggests clinical feasibility and potential value of pH-weighted amine CEST EPI as a high-resolution imaging tool for identifying tissue most at risk for long-term damage due to cerebral acidosis.

Do Sensory Stimulation Programs Have an Impact on Consciousness Recovery?

Objectives: Considering sensory stimulation programs (SSP) as a treatment for disorders of consciousness is still debated today. Previous studies investigating its efficacy were affected by various biases among which small sample size and spontaneous recovery. In this study, treatment-related changes were assessed using time-series design in patients with disorders of consciousness (i.e., vegetative state-VS and minimally conscious state-MCS). Methods: A withdrawal design (ABAB) was used. During B phases, patients underwent a SSP (3 days a week, including auditory, visual, tactile, olfactory, and gustatory stimulation). The program was not applied during A phases. To assess behavioral changes, the Coma Recovery Scale-Revised (CRS-R) was administered by an independent rater on a weekly basis, across all phases. Each phase lasted 4 weeks. In a subset of patients, resting state functional magnetic resonance imaging (fMRI) data were collected at the end of each phase. Results: Twenty nine patients (48 ± 19 years old; 15 traumatic; 21 > a year post-injury; 11 VS and 18 MCS) were included in our study. Higher CRS-R total scores (medium effect size) as well as higher arousal and oromotor subscores were observed in the B phases (treatment) as compared to A phases (no treatment), in the MCS group but not in the VS group. In the three patients who underwent fMRI analyses, a modulation of metabolic activity related to treatment was observed in middle frontal gyrus, superior temporal gyrus as well as ventro-anterior thalamic nucleus. Conclusion: Our results suggest that SSP may not be sufficient to restore consciousness. SSP might nevertheless lead to improved behavioral responsiveness in MCS patients. Our results show higher CRS-R total scores when treatment is applied, and more exactly, increased arousal and oromotor functions.

The language of music: Common neural codes for structured sequences in music and natural language

The ability to process structured sequences is a central feature of natural language but also characterizes many other domains of human cognition. In this fMRI study, we measured brain metabolic response in musicians as they generated structured and non-structured sequences in language and music. We employed a univariate and multivariate cross-classification approach to provide evidence that a common neural code underlies the production of structured sequences across the two domains. Crucially, the common substrate includes Broca's area, a region well known for processing structured sequences in language. These findings have several implications. First, they directly support the hypothesis that language and music share syntactic integration mechanisms. Second, they show that Broca's area is capable of operating supramodally across these two domains. Finally, these results dismiss the recent hypothesis that domain general processes of neighboring neural substrates explain the previously observed "overlap" between neuroimaging activations across the two domains.

Network Analysis in Disorders of Consciousness: Four Problems and One Proposed Solution (Exponential Random Graph Models)

In recent years, the study of the neural basis of consciousness, particularly in the context of patients recovering from severe brain injury, has greatly benefited from the application of sophisticated network analysis techniques to functional brain data. Yet, current graph theoretic approaches, as employed in the neuroimaging literature, suffer from four important shortcomings. First, they require arbitrary fixing of the number of connections (i.e., density) across networks which are likely to have different "natural" (i.e., stable) density (e.g., patients vs. controls, vegetative state vs. minimally conscious state patients). Second, when describing networks, they do not control for the fact that many characteristics are interrelated, particularly some of the most popular metrics employed (e.g., nodal degree, clustering coefficient)-which can lead to spurious results. Third, in the clinical domain of disorders of consciousness, there currently are no methods for incorporating structural connectivity in the characterization of functional networks which clouds the interpretation of functional differences across groups with different underlying pathology as well as in longitudinal approaches where structural reorganization processes might be operating. Finally, current methods do not allow assessing the dynamics of network change over time. We present a different framework for network analysis, based on Exponential Random Graph Models, which overcomes the above limitations and is thus particularly well suited for clinical populations with disorders of consciousness. We demonstrate this approach in the context of the longitudinal study of recovery from coma. First, our data show that throughout recovery from coma, brain graphs vary in their natural level of connectivity (from 10.4 to 14.5%), which conflicts with the standard approach of imposing arbitrary and equal density thresholds across networks (e.g., time-points, subjects, groups). Second, we show that failure to consider the interrelation between network measures does lead to spurious characterization of both inter- and intra-regional brain connectivity. Finally, we show that Separable Temporal ERGM can be employed to describe network dynamics over time revealing the specific pattern of formation and dissolution of connectivity that accompany recovery from coma.

Sleep quality and adolescent default mode network connectivity

Sleep suffers during adolescence and is related to academic, emotional and social behaviors. How this normative change relates to ongoing brain development remains unresolved. The default mode network (DMN), a large-scale brain network important for complex cognition and socioemotional processing, undergoes intra-network integration and inter-network segregation during adolescence. Using resting state functional connectivity and actigraphy over 14 days, we examined correlates of naturalistic individual differences in sleep duration and quality in the DMN at rest in 45 human adolescents (ages 14-18). Variation in sleep quality, but not duration, was related to weaker intrinsic DMN connectivity, such that those with worse quality sleep evinced weaker intra-network connectivity at rest. These novel findings suggest sleep quality, a relatively unexplored sleep index, is related to adolescent brain function in a network that contributes to behavioral maturation and undergoes development during adolescence.

At the core of reasoning: Dissociating deductive and non-deductive load

In recent years, neuroimaging methods have been used to investigate how the human mind carries out deductive reasoning. According to some, the neural substrate of language is integral to deductive reasoning. According to others, deductive reasoning is supported by a language-independent distributed network including left frontopolar and frontomedial cortices. However, it has been suggested that activity in these frontal regions might instead reflect non-deductive factors such as working memory load and general cognitive difficulty. To address this issue, 20 healthy volunteers participated in an fMRI experiment in which they evaluated matched simple and complex deductive and non-deductive arguments in a 2 × 2 design. The contrast of complex versus simple deductive trials resulted in a pattern of activation closely matching previous work, including frontopolar and frontomedial "core" areas of deduction as well as other "cognitive support" areas in frontoparietal cortices. Conversely, the contrast of complex and simple non-deductive trials resulted in a pattern of activation that does not include any of the aforementioned "core" areas. Direct comparison of the load effect across deductive and non-deductive trials further supports the view that activity in the regions previously interpreted as "core" to deductive reasoning cannot merely reflect non-deductive load, but instead might reflect processes specific to the deductive calculus. Finally, consistent with previous reports, the classical language areas in left inferior frontal gyrus and posterior temporal cortex do not appear to participate in deductive inference beyond their role in encoding stimuli presented in linguistic format.