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.

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.

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.

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.

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.

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.

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.

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.

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.

A molecular link between gene-specific and chromosome-wide transcriptional repression

Gene-specific and chromosome-wide mechanisms of transcriptional regulation control development in multicellular organisms. SDC-2, the determinant of hermaphrodite fate in Caenorhabditis elegans, is a paradigm for both modes of regulation. SDC-2 represses transcription of X chromosomes to achieve dosage compensation, and it also represses the male sex-determination gene her-1 to elicit hermaphrodite differentiation. We show here that SDC-2 recruits the entire dosage compensation complex to her-1, directing this X-chromosome repression machinery to silence an individual, autosomal gene. Functional dissection of her-1 in vivo revealed DNA recognition elements required for SDC-2 binding, recruitment of the dosage compensation complex, and transcriptional repression. Elements within her-1 differed in location, sequence, and strength of repression, implying that the dosage compensation complex may regulate transcription along the X chromosome using diverse recognition elements that play distinct roles in repression.

Dosage compensation proteins targeted to X chromosomes by a determinant of hermaphrodite fate

In many organisms, master control genes coordinately regulate sex-specific aspects of development. SDC-2 was shown to induce hermaphrodite sexual differentiation and activate X chromosome dosage compensation in Caenorhabditis elegans. To control these distinct processes, SDC-2 acts as a strong gene-specific repressor and a weaker chromosome-wide repressor. To initiate hermaphrodite development, SDC-2 associates with the promoter of the male sex-determining gene her-1 to repress its transcription. To activate dosage compensation, SDC-2 triggers assembly of a specialized protein complex exclusively on hermaphrodite X chromosomes to reduce gene expression by half. SDC-2 can localize to X chromosomes without other components of the dosage compensation complex, suggesting that SDC-2 targets dosage compensation machinery to X chromosomes.