Single Human Neurons Research Articles

Theta-Phase Dependent Neuronal Coding During Sequence Learning in Human Single Neurons

The ability to maintain a sequence of items in short-term memory (STM) is a fundamental cognitive function. Sequential item encoding in STM has been linked to a spike-theta-phase code for sequentially organized spatial locations observed in the rodent hippocampus (phase precession). The timing of neuronal activity relative to different brain oscillations is postulated to play a key role in maintaining the sequence order. We recorded single neuron and local field potential activity in the human brain during a sequence-learning task. Spikes for two consecutive items in the sequence were phase-locked at distinct phases of the theta oscillation. Surprisingly, the order of phases in the sequence-learning task was the opposite of that observed in phase precession during navigation. These results suggest that a spike-phase code is employed in the human brain during sequence learning, but with important differences compared to the rodent spike-theta-phase-dependent coding scheme.

Recollection in the human hippocampal-entorhinal cell circuitry

Imagine how flicking through your photo album and seeing a picture of a beach sunset brings back fond memories of a tasty cocktail you had that night. Computational models suggest that upon receiving a partial memory cue (‘beach’), neurons in the hippocampus coordinate reinstatement of associated memories (‘cocktail’) in cortical target sites. Here, using human single neuron recordings, we show that hippocampal firing rates are elevated from ~ 500–1500 ms after cue onset during successful associative retrieval. Concurrently, the retrieved target object can be decoded from population spike patterns in adjacent entorhinal cortex (EC), with hippocampal firing preceding EC spikes and predicting the fidelity of EC object reinstatement. Prior to orchestrating reinstatement, a separate population of hippocampal neurons distinguishes different scene cues (buildings vs. landscapes). These results elucidate the hippocampal-entorhinal circuit dynamics for memory recall and reconcile disparate views on the role of the hippocampus in scene processing vs. associative memory.

Single-Neuron Correlates of Error Monitoring and Post-Error Adjustments in Human Medial Frontal Cortex

Humans can self-monitor errors without explicit feedback, resulting in behavioral adjustments on subsequent trials such as post-error slowing (PES). The error-related negativity (ERN) is a well-established macroscopic scalp EEG correlate of error self-monitoring, but its neural origins and relationship to PES remain unknown. We recorded in the frontal cortex of patients performing a Stroop task and found neurons that track self-monitored errors and error history in dorsal anterior cingulate cortex (dACC) and pre-supplementary motor area (pre-SMA). Both the intracranial ERN (iERN) and error neuron responses appeared first in pre-SMA, and ∼50ms later in dACC. Error neuron responses were correlated with iERN amplitude on individual trials. In dACC, such error neuron-iERN synchrony and responses of error-history neurons predicted the magnitude of PES. These data reveal a human single-neuron correlate of the ERN and suggest that dACC synthesizes error information to recruit behavioral control through coordinated neural activity.

Patch-Seq Protocol to Analyze the Electrophysiology, Morphology and Transcriptome of Whole Single Neurons Derived From Human Pluripotent Stem Cells.

The human brain is composed of a complex assembly of about 171 billion heterogeneous cellular units (86 billion neurons and 85 billion non-neuronal glia cells). A comprehensive description of brain cells is necessary to understand the nervous system in health and disease. Recently, advances in genomics have permitted the accurate analysis of the full transcriptome of single cells (scRNA-seq). We have built upon such technical progress to combine scRNA-seq with patch-clamping electrophysiological recording and morphological analysis of single human neurons in vitro. This new powerful method, referred to as Patch-seq, enables a thorough, multimodal profiling of neurons and permits us to expose the links between functional properties, morphology, and gene expression. Here, we present a detailed Patch-seq protocol for isolating single neurons from in vitro neuronal cultures. We have validated the Patch-seq whole-transcriptome profiling method with human neurons generated from embryonic and induced pluripotent stem cells (ESCs/iPSCs) derived from healthy subjects, but the procedure may be applied to any kind of cell type in vitro. Patch-seq may be used on neurons in vitro to profile cell types and states in depth to unravel the human molecular basis of neuronal diversity and investigate the cellular mechanisms underlying brain disorders.

Erratum for the Report "Aging and neurodegeneration are associated with increased mutations in single human neurons" by M. A. Lodato, R. E. Rodin, C. L. Bohrson, M. E. Coulter, A. R. Barton, M. Kwon, M. A. Sherman, C. M. Vitzthum, L. J. Luquette, C. N. Yandava, P. Yang, T. W. Chittenden, N. E. Hatem, S. C. Ryu, M. B. Woodworth,

Erratum for the Report "Aging and neurodegeneration are associated with increased mutations in single human neurons" by M. A. Lodato, R. E. Rodin, C. L. Bohrson, M. E. Coulter, A. R. Barton, M. Kwon, M. A. Sherman, C. M. Vitzthum, L. J. Luquette, C. N. Yandava, P. Yang, T. W. Chittenden, N. E. Hatem, S. C. Ryu, M. B. Woodworth,

Phase-tuned neuronal firing encodes human contextual representations for navigational goals.

We previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a virtual navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person's prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.

Human single neuron activity precedes emergence of conscious perception

Identifying the neuronal basis of spontaneous changes in conscious experience in the absence of changes in the external environment is a major challenge. Binocular rivalry, in which two stationary monocular images lead to continuously changing perception, provides a unique opportunity to address this issue. We studied the activity of human single neurons in the medial temporal and frontal lobes while patients were engaged in binocular rivalry. Here we report that internal changes in the content of perception are signaled by very early (~-2000 ms) nonselective medial frontal activity, followed by selective activity of medial temporal lobe neurons that precedes the perceptual change by ~1000 ms. Such early activations are not found for externally driven perceptual changes. These results suggest that a medial fronto-temporal network may be involved in the preconscious internal generation of perceptual transitions.

Dataset of human medial temporal lobe single neuron activity during declarative memory encoding and recognition

We present a dataset of 1,576 single neurons recorded from the human amygdala and hippocampus in 65 sessions from 42 patients undergoing intracranial monitoring for localization of epileptic seizures. Subjects performed a recognition memory task with pictures as stimuli. Subjects were asked to identify whether they had seen a particular image the first time (‘new’) or second time (‘old’) on a 1–6 confidence scale. This comprehensive dataset includes the spike times of all neurons and their extracellular waveforms, behavior, electrode locations determined from post-operative MRI scans, demographics, and the stimuli shown. As technical validation, we provide spike sorting quality metrics and assessment of tuning of cells to verify the presence of visually-and memory selective cells. We also provide analysis code that reproduces key scientific findings published previously on a smaller version of this dataset. Together, this large dataset will facilitate the investigation of the neural mechanism of declarative memory by providing a substantial number of hard to obtain human single-neuron recordings during a well characterized behavioral task.

Aging and neurodegeneration are associated with increased mutations in single human neurons.

It has long been hypothesized that aging and neurodegeneration are associated with somatic mutation in neurons; however, methodological hurdles have prevented testing this hypothesis directly. We used single-cell whole-genome sequencing to perform genome-wide somatic single-nucleotide variant (sSNV) identification on DNA from 161 single neurons from the prefrontal cortex and hippocampus of 15 normal individuals (aged 4 months to 82 years), as well as 9 individuals affected by early-onset neurodegeneration due to genetic disorders of DNA repair (Cockayne syndrome and xeroderma pigmentosum). sSNVs increased approximately linearly with age in both areas (with a higher rate in hippocampus) and were more abundant in neurodegenerative disease. The accumulation of somatic mutations with age-which we term genosenium-shows age-related, region-related, and disease-related molecular signatures and may be important in other human age-associated conditions.

Comparison of combined spike detection and clustering using mutual information

BackgroundSpike sorting techniques involve both detection of spike waveform events and classification of those events into clusters of similar waveform shape. The one existing method of evaluating the combined effects of both detection and classification depends on assignment of cluster correspondence. Other methods of evaluation have focused on either clustering or detection, but not both, although these two steps may interact. New methodThis paper develops an information theoretic measure of agreement between the output of two spike sorting techniques, AMIall, which can be used even when the number of waveform events detected by the two techniques differs. ResultsAMIall is shown to be a useful measure for studying variations of parameters of spike sorting techniques in two examples: comparing outputs for simulated noisy spike sorting and spike sorting of human single neuron recordings.Comparison with existing methods Computing AMIall does not require an explicit assignment of cluster correspondence, thereby eliminating a potential source of variation. By providing a single measure of performance, computing AMIall is very useful when comparing large numbers of algorithmic or parametric variations of spike sorting techniques; prior comparison techniques have often required multiple measures of performance which complicates large scale comparisons. ConclusionsThe use of AMIall to measure agreement between spike sorting techniques facilitates the comparison of the outputs of those techniques, including variations in both spike detection and waveform clustering. This measure should be useful for broad based and large scale comparisons between spike sorting techniques.

Predicting the functional states of human iPSC-derived neurons with single-cell RNA-seq and electrophysiology.

Human neural progenitors derived from pluripotent stem cells develop into electrophysiologically active neurons at heterogeneous rates, which can confound disease-relevant discoveries in neurology and psychiatry. By combining patch clamping, morphological and transcriptome analysis on single-human neurons in vitro, we defined a continuum of poor to highly functional electrophysiological states of differentiated neurons. The strong correlations between action potentials, synaptic activity, dendritic complexity and gene expression highlight the importance of methods for isolating functionally comparable neurons for in vitro investigations of brain disorders. Although whole-cell electrophysiology is the gold standard for functional evaluation, it often lacks the scalability required for disease modeling studies. Here, we demonstrate a multimodal machine-learning strategy to identify new molecular features that predict the physiological states of single neurons, independently of the time spent in vitro. As further proof of concept, we selected one of the potential neurophysiological biomarkers identified in this study-GDAP1L1-to isolate highly functional live human neurons in vitro.

Coupled electrophysiological recording and single cell transcriptome analyses revealed molecular mechanisms underlying neuronal maturation.

The mammalian brain is heterogeneous, containing billions of neurons and trillions of synapses forming various neural circuitries, through which sense, movement, thought, and emotion arise. The cellular heterogeneity of the brain has made it difficult to study the molecular logic of neural circuitry wiring, pruning, activation, and plasticity, until recently, transcriptome analyses with single cell resolution makes decoding of gene regulatory networks underlying aforementioned circuitry properties possible. Here we report success in performing both electrophysiological and whole-genome transcriptome analyses on single human neurons in culture. Using Weighted Gene Coexpression Network Analyses (WGCNA), we identified gene clusters highly correlated with neuronal maturation judged by electrophysiological characteristics. A tight link between neuronal maturation and genes involved in ubiquitination and mitochondrial function was revealed. Moreover, we identified a list of candidate genes, which could potentially serve as biomarkers for neuronal maturation. Coupled electrophysiological recording and single cell transcriptome analysis will serve as powerful tools in the future to unveil molecular logics for neural circuitry functions.Electronic supplementary materialThe online version of this article (doi:10.1007/s13238-016-0247-8) contains supplementary material, which is available to authorized users.

Single-Cell Detection of Secreted Aβ and sAPPα from Human IPSC-Derived Neurons and Astrocytes.

We have established a technology that, for the first time, detects secreted analytes from single human neurons and astrocytes. We examine secretion of the Alzheimer's disease-relevant factors amyloid β (Aβ) and soluble amyloid precursor protein-alpha (sAPPα) and present novel findings that could not have been observed without a single-cell analytical platform. First, we identify a previously unappreciated subpopulation that secretes high levels of Aβ in the absence of detectable sAPPα. Further, we show that multiple cell types secrete high levels of Aβ and sAPPα, but cells expressing GABAergic neuronal markers are overrepresented. Finally, we show that astrocytes are competent to secrete high levels of Aβ and therefore may be a significant contributor to Aβ accumulation in the brain.

Somatic mutation in single human neurons tracks developmental and transcriptional history.

Neurons live for decades in a postmitotic state, their genomes susceptible to DNA damage. Here we survey the landscape of somatic single-nucleotide variants (SNVs) in the human brain. We identified thousands of somatic SNVs by single-cell sequencing of 36 neurons from the cerebral cortex of three normal individuals. Unlike germline and cancer SNVs, which are often caused by errors in DNA replication, neuronal mutations appear to reflect damage during active transcription. Somatic mutations create nested lineage trees, allowing them to be dated relative to developmental landmarks and revealing a polyclonal architecture of the human cerebral cortex. Thus, somatic mutations in the brain represent a durable and ongoing record of neuronal life history, from development through postmitotic function.

Gender differences in human single neuron responses to male emotional faces.

Well-documented differences in the psychology and behavior of men and women have spurred extensive exploration of gender's role within the brain, particularly regarding emotional processing. While neuroanatomical studies clearly show differences between the sexes, the functional effects of these differences are less understood. Neuroimaging studies have shown inconsistent locations and magnitudes of gender differences in brain hemodynamic responses to emotion. To better understand the neurophysiology of these gender differences, we analyzed recordings of single neuron activity in the human brain as subjects of both genders viewed emotional expressions. This study included recordings of single-neuron activity of 14 (6 male) epileptic patients in four brain areas: amygdala (236 neurons), hippocampus (n = 270), anterior cingulate cortex (n = 256), and ventromedial prefrontal cortex (n = 174). Neural activity was recorded while participants viewed a series of avatar male faces portraying positive, negative or neutral expressions. Significant gender differences were found in the left amygdala, where 23% (n = 15∕66) of neurons in men were significantly affected by facial emotion, vs. 8% (n = 6∕76) of neurons in women. A Fisher's exact test comparing the two ratios found a highly significant difference between the two (p < 0.01). These results show specific differences between genders at the single-neuron level in the human amygdala. These differences may reflect gender-based distinctions in evolved capacities for emotional processing and also demonstrate the importance of including subject gender as an independent factor in future studies of emotional processing by single neurons in the human amygdala.

Methods for implantation of micro-wire bundles and optimization of single/multi-unit recordings from human mesial temporal lobe

Objective. The authors report methods developed for the implantation of micro-wire bundles into mesial temporal lobe structures and subsequent single neuron recording in epileptic patients undergoing in-patient diagnostic monitoring. This is done with the intention of lowering the perceived barriers to routine single neuron recording from deep brain structures in the clinical setting. Approach. Over a 15 month period, 11 patients were implanted with platinum micro-wire bundles into mesial temporal structures. Protocols were developed for (A) monitoring electrode integrity through impedance testing, (B) ensuring continuous 24-7 recording, (C) localizing micro-wire position and ‘splay’ pattern and (D) monitoring grounding and referencing to maintain the quality of recordings. Main results. Five common modes of failure were identified: (1) broken micro-wires from acute tensile force, (2) broken micro-wires from cyclic fatigue at stress points, (3) poor in vivo micro-electrode separation, (4) motion artifact and (5) deteriorating ground connection and subsequent drop in common mode noise rejection. Single neurons have been observed up to 14 days post-implantation and on 40% of micro-wires. Significance. Long-term success requires detailed review of each implant by both the clinical and research teams to identify failure modes, and appropriate refinement of techniques while moving forward. This approach leads to reliable unit recordings without prolonging operative times, which will help increase the availability and clinical viability of human single neuron data.

Deep brain stimulation: a mechanistic and clinical update

Deep brain stimulation (DBS), the practice of placing electrodes deep into the brain to stimulate subcortical structures with electrical current, has been increasing as a neurosurgical procedure over the past 15 years. Originally a treatment for essential tremor, DBS is now used and under investigation across a wide spectrum of neurological and psychiatric disorders. In addition to applying electrical stimulation for clinical symptomatic relief, the electrodes implanted can also be used to record local electrical activity in the brain, making DBS a useful research tool. Human single-neuron recordings and local field potentials are now often recorded intraoperatively as electrodes are implanted. Thus, the increasing scope of DBS clinical applications is being matched by an increase in investigational use, leading to a rapidly evolving understanding of cortical and subcortical neurocircuitry. In this review, the authors discuss recent innovations in the clinical use of DBS, both in approved indications as well as in indications under investigation. Deep brain stimulation as an investigational tool is also reviewed, paying special attention to evolving models of basal ganglia and cortical function in health and disease. Finally, the authors look to the future across several indications, highlighting gaps in knowledge and possible future directions of DBS treatment.

Human Temporal Cortical Single Neuron Activity during Language: A Review.

Findings from recordings of human temporal cortical single neuron activity during several measures of language, including object naming and word reading are reviewed and related to changes in activity in the same neurons during recent verbal memory and verbal associative learning measures, in studies conducted during awake neurosurgery for the treatment of epilepsy. The proportion of neurons changing activity with language tasks was similar in either hemisphere. Dominant hemisphere activity was characterized by relative inhibition, some of which occurred during overt speech, possibly to block perception of one’s own voice. However, the majority seems to represent a dynamic network becoming active with verbal memory encoding and especially verbal learning, but inhibited during performance of overlearned language tasks. Individual neurons are involved in different networks for different aspects of language, including naming or reading and naming in different languages. The majority of the changes in activity were tonic sustained shifts in firing. Patterned phasic activity for specific language items was very infrequently recorded. Human single neuron recordings provide a unique perspective on the biologic substrate for language, for these findings are in contrast to many of the findings from other techniques for investigating this.

A statistical method for predicting seizure onset zones from human single-neuron recordings

Objective. Clinicians often use depth-electrode recordings to localize human epileptogenic foci. To advance the diagnostic value of these recordings, we applied logistic regression models to single-neuron recordings from depth-electrode microwires to predict seizure onset zones (SOZs). Approach. We collected data from 17 epilepsy patients at the Barrow Neurological Institute and developed logistic regression models to calculate the odds of observing SOZs in the hippocampus, amygdala and ventromedial prefrontal cortex, based on statistics such as the burst interspike interval (ISI). Main results. Analysis of these models showed that, for a single-unit increase in burst ISI ratio, the left hippocampus was approximately 12 times more likely to contain a SOZ; and the right amygdala, 14.5 times more likely. Our models were most accurate for the hippocampus bilaterally (at 85% average sensitivity), and performance was comparable with current diagnostics such as electroencephalography. Significance. Logistic regression models can be combined with single-neuron recording to predict likely SOZs in epilepsy patients being evaluated for resective surgery, providing an automated source of clinically useful information.

What is a Seizure? Insights from Human Single-Neuron Recordings

What is a Seizure? Insights from Human Single-Neuron Recordings