An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood.

Human brain networks that encode variation in mood on naturalistic timescales remain largely unexplored. Here we combine multi-site, semi-chronic, intracranial electroencephalography recordings from the human limbic system with machine learning methods to discover a brain subnetwork that correlates with variation in individual subjects' self-reported mood over days. First we defined the subnetworks that influence intrinsic brain dynamics by identifying regions that showed coordinated changes in spectral coherence. The most common subnetwork, found in 13 of 21 subjects, was characterized by ß-frequency coherence (13-30 Hz) between the amygdala and hippocampus. Increased variability of this subnetwork correlated with worsening mood across these 13 subjects. Moreover, these subjects had significantly higher trait anxiety than the 8 of 21 for whom this amygdala-hippocampus subnetwork was absent. These results demonstrate an approach for extracting network-behavior relationships from complex datasets, and they reveal a conserved subnetwork associated with a psychological trait that significantly influences intrinsic brain dynamics and encodes fluctuations in mood.

Pilot Study of An Intracranial Electroencephalography Biomarker of Depressive Symptoms in Epilepsy.

OBJECTIVES: Adult patients with epilepsy have an increased prevalence of major depressive disorder (MDD). Intracranial EEG (iEEG) captured during extended inpatient monitoring of patients with treatment-resistant epilepsy offers a particularly promising method to study MDD networks in epilepsy. METHODS: The authors used 24 hours of resting-state iEEG to examine the neural activity patterns within corticolimbic structures that reflected the presence of depressive symptoms in 13 adults with medication-refractory epilepsy. Principal component analysis was performed on the z-scored mean relative power in five standard frequency bands averaged across electrodes within a region. RESULTS: Principal component 3 was a statistically significant predictor of the presence of depressive symptoms (R2=0.35, p=0.014). A balanced logistic classifier model using principal component 3 alone correctly classified 78% of patients as belonging to the group with a high burden of depressive symptoms or a control group with minimal depressive symptoms (sensitivity, 75%; specificity, 80%; area under the curve=0.8, leave-one-out cross validation). Classification was dependent on beta power throughout the corticolimbic network and low-frequency cingulate power. CONCLUSIONS: These finding suggest, for the first time, that neural features across circuits involved in epilepsy may distinguish patients who have depressive symptoms from those who do not. Larger studies are required to validate these findings and to assess their diagnostic utility in MDD.

Human Anterior Insular Cortex Encodes Multiple Electrophysiological Representations of Anxiety-Related Behaviors.

Anxiety is a common symptom across psychiatric disorders, but the neurophysiological underpinnings of these symptoms remain unclear. This knowledge gap has prevented the development of circuit-based treatments that can target the neural substrates underlying anxiety. Here, we conducted an electrophysiological mapping study to identify neurophysiological activity associated with self-reported state anxiety in 17 subjects implanted with intracranial electrodes for seizure localization. Participants had baseline anxiety traits ranging from minimal to severe. Subjects volunteered to participate in an anxiety induction task in which they were temporarily exposed to the threat of unpredictable shock during intracranial recordings. We found that anterior insular beta oscillatory activity was selectively elevated during epochs when unpredictable aversive stimuli were being delivered, and this enhancement in insular beta was correlated with increases in self-reported anxiety. Beta oscillatory activity within the frontoinsular region was also evoked selectively by cues-predictive of threat, but not safety cues. Anterior insular gamma responses were less selective than gamma, strongly evoked by aversive stimuli and had weaker responses to salient threat and safety cues. On longer timescales, this gamma signal also correlated with increased skin conductance, a measure of autonomic state. Lastly, we found that direct electrical stimulation of the anterior insular cortex in a subset of subjects elicited self-reported increases in anxiety that were accompanied by enhanced frontoinsular beta oscillations. Together, these findings suggest that electrophysiologic representations of anxiety- related states and behaviors exist within anterior insular cortex. The findings also suggest the potential of reducing anterior insular beta activity as a therapeutic target for refractory anxiety-spectrum disorders.

Amygdala-hippocampus somatostatin interneuron beta-synchrony underlies a cross-species biomarker of emotional state.

Emotional responses arise from limbic circuits including the hippocampus and amygdala. In the human brain, beta-frequency communication between these structures correlates with self-reported mood and anxiety. However, both the mechanism and significance of this biomarker as a readout vs. driver of emotional state remain unknown. Here, we show that beta-frequency communication between ventral hippocampus and basolateral amygdala also predicts anxiety-related behavior in mice, both on long timescales (∼30 min) and immediately preceding behavioral choices. Genetically encoded voltage indicators reveal that this biomarker reflects synchronization between somatostatin interneurons across both structures. Indeed, synchrony between these neurons dynamically predicts approach-avoidance decisions, and optogenetically shifting the phase of synchronization by just 25 ms is sufficient to bidirectionally modulate anxiety-related behaviors. Thus, back-translation establishes a human biomarker as a causal determinant (not just predictor) of emotional state, revealing a novel mechanism whereby interregional synchronization that is frequency, phase, and cell type specific controls emotional processing.

First-in-human prediction of chronic pain state using intracranial neural biomarkers.

Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.

Decoding naturalistic affective behaviour from spectro-spatial features in multiday human iEEG.

The neurological basis of affective behaviours in everyday life is not well understood. We obtained continuous intracranial electroencephalography recordings from the human mesolimbic network in 11 participants with epilepsy and hand-annotated spontaneous behaviours from 116 h of multiday video recordings. In individual participants, binary random forest models decoded affective behaviours from neutral behaviours with up to 93% accuracy. Both positive and negative affective behaviours were associated with increased high-frequency and decreased low-frequency activity across the mesolimbic network. The insula, amygdala, hippocampus and anterior cingulate cortex made stronger contributions to affective behaviours than the orbitofrontal cortex, but the insula and anterior cingulate cortex were most critical for differentiating behaviours with observable affect from those without. In a subset of participants (N = 3), multiclass decoders distinguished amongst the positive, negative and neutral behaviours. These results suggest that spectro-spatial features of brain activity in the mesolimbic network are associated with affective behaviours of everyday life.

Thin-film microfabrication and intraoperative testing ofµECoG and iEEG depth arrays for sense and stimulation.

Objective.Intracranial neural recordings and electrical stimulation are tools used in an increasing range of applications, including intraoperative clinical mapping and monitoring, therapeutic neuromodulation, and brain computer interface control and feedback. However, many of these applications suffer from a lack of spatial specificity and localization, both in terms of sensed neural signal and applied stimulation. This stems from limited manufacturing processes of commercial-off-the-shelf (COTS) arrays unable to accommodate increased channel density, higher channel count, and smaller contact size.Approach.Here, we describe a manufacturing and assembly approach using thin-film microfabrication for 32-channel high density subdural micro-electrocorticography (µECoG) surface arrays (contacts 1.2 mm diameter, 2 mm pitch) and intracranial electroencephalography (iEEG) depth arrays (contacts 0.5 mm × 1.5 mm, pitch 0.8 mm × 2.5 mm). Crucially, we tackle the translational hurdle and test these arrays during intraoperative studies conducted in four humans under regulatory approval.Main results.We demonstrate that the higher-density contacts provide additional unique information across the recording span compared to the density of COTS arrays which typically have electrode pitch of 8 mm or greater; 4 mm in case of specially ordered arrays. Our intracranial stimulation study results reveal that refined spatial targeting of stimulation elicits evoked potentials with differing spatial spread.Significance.Thin-film,µECoG and iEEG depth arrays offer a promising substrate for advancing a number of clinical and research applications reliant on high-resolution neural sensing and intracranial stimulation.

Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology.

Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.

Prefrontal Physiomarkers of Anxiety and Depression in Parkinson's Disease.

Objective: Anxiety and depression are prominent non-motor symptoms of Parkinson's disease (PD), but their pathophysiology remains unclear. We sought to understand their neurophysiological correlates from chronic invasive recordings of the prefrontal cortex (PFC). Methods: We studied four patients undergoing deep brain stimulation (DBS) for their motor signs, who had comorbid mild to moderate anxiety and/or depressive symptoms. In addition to their basal ganglia leads, we placed a permanent prefrontal subdural 4-contact lead. These electrodes were attached to an investigational pulse generator with the capability to sense and store field potential signals, as well as deliver therapeutic neurostimulation. At regular intervals over 3-5 months, participants paired brief invasive neural recordings with self-ratings of symptoms related to depression and anxiety. Results: Mean age was 61 ± 7 years, mean disease duration was 11 ± 8 years and a mean Unified Parkinson's Disease Rating Scale, with part III (UPDRS-III) off medication score of 37 ± 13. Mean Beck Depression Inventory (BDI) score was 14 ± 5 and Beck Anxiety Index was 16.5 ± 5. Prefrontal cortex spectral power in the beta band correlated with patient self-ratings of symptoms of depression and anxiety, with r-values between 0.31 and 0.48. Mood scores showed negative correlation with beta spectral power in lateral locations, and positive correlation with beta spectral power in a mesial recording location, consistent with the dichotomous organization of reward networks in PFC. Interpretation: These findings suggest a physiological basis for anxiety and depression in PD, which may be useful in the development of neurostimulation paradigms for these non-motor disease features.

Closed-loop neuromodulation in an individual with treatment-resistant depression.

Deep brain stimulation is a promising treatment for neuropsychiatric conditions such as major depression. It could be optimized by identifying neural biomarkers that trigger therapy selectively when symptom severity is elevated. We developed an approach that first used multi-day intracranial electrophysiology and focal electrical stimulation to identify a personalized symptom-specific biomarker and a treatment location where stimulation improved symptoms. We then implanted a chronic deep brain sensing and stimulation device and implemented a biomarker-driven closed-loop therapy in an individual with depression. Closed-loop therapy resulted in a rapid and sustained improvement in depression. Future work is required to determine if the results and approach of this n-of-1 study generalize to a broader population.

Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson's disease.

Neural recordings using invasive devices in humans can elucidate the circuits underlying brain disorders, but have so far been limited to short recordings from externalized brain leads in a hospital setting or from implanted sensing devices that provide only intermittent, brief streaming of time series data. Here, we report the use of an implantable two-way neural interface for wireless, multichannel streaming of field potentials in five individuals with Parkinson's disease (PD) for up to 15 months after implantation. Bilateral four-channel motor cortex and basal ganglia field potentials streamed at home for over 2,600 h were paired with behavioral data from wearable monitors for the neural decoding of states of inadequate or excessive movement. We validated individual-specific neurophysiological biomarkers during normal daily activities and used those patterns for adaptive deep brain stimulation (DBS). This technological approach may be widely applicable to brain disorders treatable by invasive neuromodulation.

Developing Collaborative Platforms to Advance Neurotechnology and Its Translation.

Neurotechnological devices are failing to deliver on their therapeutic promise because of the time it takes to translate them from bench to clinic. In this Perspective, we reflect on lessons learned from medical device successes and failures and consider how such lessons might shape a strategic vision for translating neurotechnologies in the future. We articulate how the intentional design and deployment of "scientific platforms," from the technology stack of hardware and software through the supporting ecosystem, could catalyze a new wave of innovation, discovery, and therapy. We also identify specific actions that could promote future neurotechnology roadmaps and industrial-academic-government collaborative activities. We believe that community-supported neurotechnology platforms will prove to be transformational in accelerating ideas from bench to bedside, maximizing scientific discovery and improving patient care.

Biases in processing of mood-congruent facial expressions in depression.

Cognitive models of depression suggest that depressed individuals exhibit a tendency to attribute negative meaning to neutral stimuli, and enhanced processing of mood-congruent stimuli. However, evidence thus far has been inconsistent. In this study, we sought to identify both differential interpretation of neutral information as well as emotion processing biases associated with depression. Fifty adult participants completed standardized mood-related questionnaires, a novel immediate mood scale questionnaire (IMS-12), and a novel task, Emotion Matcher, in which they were required to indicate whether pairs of emotional faces show the same expression or not. We found that overall success rate and reaction time on the Emotion Matcher task did not differ as a function of severity of depression. However, more depressed participants had significantly worse performance when presented with sad-neutral face pairs, as well as increased reaction times to happy-happy pairs. In addition, accuracy of the sad-neutral pairs was found to be significantly associated with depression severity in a regression model. Our study provides partial support for the mood-congruent hypothesis, revealing only a potential bias in interpretation of sad and neutral expressions, but not a general deficit in processing of facial expressions. The potential of such bias in serving as a predictor for depression should be further examined in future studies.

Closed-Loop Deep Brain Stimulation for Refractory Chronic Pain.

Pain is a subjective experience that alerts an individual to actual or potential tissue damage. Through mechanisms that are still unclear, normal physiological pain can lose its adaptive value and evolve into pathological chronic neuropathic pain. Chronic pain is a multifaceted experience that can be understood in terms of somatosensory, affective, and cognitive dimensions, each with associated symptoms and neural signals. While there have been many attempts to treat chronic pain, in this article we will argue that feedback-controlled 'closed-loop' deep brain stimulation (DBS) offers an urgent and promising route for treatment. Contemporary DBS trials for chronic pain use "open-loop" approaches in which tonic stimulation is delivered with fixed parameters to a single brain region. The impact of key variables such as the target brain region and the stimulation waveform is unclear, and long-term efficacy has mixed results. We hypothesize that chronic pain is due to abnormal synchronization between brain networks encoding the somatosensory, affective and cognitive dimensions of pain, and that multisite, closed-loop DBS provides an intuitive mechanism for disrupting that synchrony. By (1) identifying biomarkers of the subjective pain experience and (2) integrating these signals into a state-space representation of pain, we can create a predictive model of each patient's pain experience. Then, by establishing how stimulation in different brain regions influences individual neural signals, we can design real-time, closed-loop therapies tailored to each patient. While chronic pain is a complex disorder that has eluded modern therapies, rich historical data and state-of-the-art technology can now be used to develop a promising treatment.

Mood variations decoded from multi-site intracranial human brain activity.

The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated, partly owing to the difficulty of modeling distributed mood-relevant neural dynamics while dealing with the sparsity of mood state measurements. Here we develop a modeling framework to decode mood state variations from multi-site intracranial recordings in seven human subjects with epilepsy who self-reported their mood state intermittently over multiple days. We built dynamic neural encoding models of mood state and corresponding decoders for each individual and demonstrated that mood state variations over time can be decoded from neural activity. Across subjects, the decoders largely recruited neural signals from limbic regions, whose spectro-spatial features were tuned to mood variations. The dynamic models also provided an analytical tool to compute the timescales of the decoded mood state. These results provide an initial line of evidence indicating the feasibility of mood state decoding.

Direct Electrical Stimulation of Lateral Orbitofrontal Cortex Acutely Improves Mood in Individuals with Symptoms of Depression.

Mood disorders cause significant morbidity and mortality, and existing therapies fail 20%-30% of patients. Deep brain stimulation (DBS) is an emerging treatment for refractory mood disorders, but its success depends critically on target selection. DBS focused on known targets within mood-related frontostriatal and limbic circuits has been variably efficacious. Here, we examine the effects of stimulation in orbitofrontal cortex (OFC), a key hub for mood-related circuitry that has not been well characterized as a stimulation target. We studied 25 subjects with epilepsy who were implanted with intracranial electrodes for seizure localization. Baseline depression traits ranged from mild to severe. We serially assayed mood state over several days using a validated questionnaire. Continuous electrocorticography enabled investigation of neurophysiological correlates of mood-state changes. We used implanted electrodes to stimulate OFC and other brain regions while collecting verbal mood reports and questionnaire scores. We found that unilateral stimulation of the lateral OFC produced acute, dose-dependent mood-state improvement in subjects with moderate-to-severe baseline depression. Stimulation suppressed low-frequency power in OFC, mirroring neurophysiological features that were associated with positive mood states during natural mood fluctuation. Stimulation potentiated single-pulse-evoked responses in OFC and modulated activity within distributed structures implicated in mood regulation. Behavioral responses to stimulation did not include hypomania and indicated an acute restoration to non-depressed mood state. Together, these findings indicate that lateral OFC stimulation broadly modulates mood-related circuitry to improve mood state in depressed patients, revealing lateral OFC as a promising new target for therapeutic brain stimulation in mood disorders.

Widening the net on genome sequencing.

GENOMES: With the DNA sequencing of most key model organisms finished or well on the way, one genomics institute is filling an important niche, using novel criteria to take on species from organisms that occupy a unique place in the evolutionary tree to others that have an ability to digest explosives.

The quiet revolution.

Sixty years have passed since Avery, MacLeod and McCarty published their landmark paper revealing DNA as the genetic material, writes Heather Dawes.

Specialist nurse supported discharge in gynaecology: a randomised comparison and economic evaluation.

AIM: To determine the effect on quality of life and cost effectiveness of specialist nurse early supported discharge for women undergoing major abdominal and/or pelvic surgery for benign gynaecological disease compared with routine care. STUDY DESIGN: Randomised controlled trial comparing specialist nurse supported discharge with routine hospital care in gynaecology. The SF-36, a generic health status questionnaire, was used to measure women's evaluation of their health state before surgery and at 6 weeks after surgery. A further questionnaire scoring patient symptoms, milestones of recovery, information given and satisfaction, was administered prior to discharge from hospital and at 6 weeks thereafter. SETTING: Gynaecology service at the Western Infirmary Glasgow, part of North Glasgow University, NHS Trust. PARTICIPANTS: One hundred and eleven women scheduled for major abdominal or pelvic surgery for benign gynaecological disease. MAIN OUTCOME MEASURES: SF-36 health survey questionnaire baseline scores were reported before surgery and at 6 weeks follow-up. Complications, length of hospital stay, readmission, information on discharge support and satisfaction of women were recorded at discharge from hospital and at 6 weeks follow-up. A cost consequence analysis was conducted based on the perspective of the NHS. RESULTS: The addition of a specialist nurse to routine hospital care in gynaecology significantly reduced the post-operative length of hospital stay p = 0.001, improved information delivery and satisfaction of women. The specialist nurse supported discharge group was associated with significantly lower total costs to the NHS than routine care resulting principally from the difference in the cost of the post-operative length of stay. CONCLUSIONS: Women undergoing major abdominal and pelvic surgery were discharged home earlier with provision of support from a specialist gynaecology nurse. The results of this study suggest that duration of hospital stay can be shortened by the introduction of a specialist nurse without introducing any adverse physical and psychological effects. This process of care is associated with receipt of information on health and lifestyle issues and maintenance of high levels of patient satisfaction and demonstrates the effectiveness of the specialist nurse role in the provision of health information for women. Earlier hospital discharge at 48 h after major abdominal and pelvic surgery is an acceptable, cost effective alternative to current routine practice in the absence of further randomised evidence.

Immediate Mood Scaler: Tracking Symptoms of Depression and Anxiety Using a Novel Mobile Mood Scale.

BACKGROUND: Mood disorders are dynamic disorders characterized by multimodal symptoms. Clinical assessment of symptoms is currently limited to relatively sparse, routine clinic visits, requiring retrospective recollection of symptoms present in the weeks preceding the visit. Novel advances in mobile tools now support ecological momentary assessment of mood, conducted frequently using mobile devices, outside the clinical setting. Such mood assessment may help circumvent problems associated with infrequent reporting and better characterize the dynamic presentation of mood symptoms, informing the delivery of novel treatment options. OBJECTIVES: The aim of our study was to validate the Immediate Mood Scaler (IMS), a newly developed, iPad-deliverable 22-item self-report tool designed to capture current mood states. METHODS: A total of 110 individuals completed standardized questionnaires (Patient Health Questionnaire, 9-item [PHQ-9]; generalized anxiety disorder, 7-Item [GAD-7]; and rumination scale) and IMS at baseline. Of the total, 56 completed at least one additional session of IMS, and 17 completed one additional administration of PHQ-9 and GAD-7. We conducted exploratory Principal Axis Factor Analysis to assess dimensionality of IMS, and computed zero-order correlations to investigate associations between IMS and standardized scales. Linear Mixed Model (LMM) was used to assess IMS stability across time and to test predictability of PHQ-9 and GAD-7 score by IMS. RESULTS: Strong correlations were found between standard mood scales and the IMS at baseline (r=.57-.59, P<.001). A factor analysis revealed a 12-item IMS ("IMS-12") with two factors: a "depression" factor and an "anxiety" factor. IMS-12 depression subscale was more strongly correlated with PHQ-9 than with GAD-7 (z=1.88, P=.03), but the reverse pattern was not found for IMS-12 anxiety subscale. IMS-12 showed less stability over time compared with PHQ-9 and GAD-7 (.65 vs .91), potentially reflecting more sensitivity to mood dynamics. In addition, IMS-12 ratings indicated that individuals with mild to moderate depression had greater mood fluctuations compared with individuals with severe depression (.42 vs .79; P=.04). Finally, IMS-12 significantly contributed to the prediction of subsequent PHQ-9 (beta=1.03, P=.02) and GAD-7 scores (beta =.93, P=.01). CONCLUSIONS: Collectively, these data suggest that the 12-item IMS (IMS-12) is a valid tool to assess momentary mood symptoms related to anxiety and depression. Although IMS-12 shows good correlation with standardized scales, it further captures mood fluctuations better and significantly adds to the prediction of the scales. Results are discussed in the context of providing continuous symptom quantification that may inform novel treatment options and support personalized treatment plans.