Abstract The human posterior cingulate cortex (PCC) is routinely implicated in cognition and disease, yet its specific functional contributions remain unclear. Historically, human neuroimaging has linked the region to episodic memory and the default mode network, while a distinct non-human primate electrophysiology literature has focused on economic decision-making. Integrating anatomical evidence with these literatures, it has recently been proposed that this divergence reflects subregional organization, with dorsal PCC as a potential convergence site for value-based and memory-based decisions. Here, we recorded local field potentials (LFPs) and single units from human PCC while the same participants performed matched value- and memory-based decision tasks. LFPs in dorsal but not ventral PCC showed sustained engagement across both tasks, with risk sensitivity emerging only after the decision. In contrast, single-unit activity was more temporally circumscribed and could be grouped into response profiles active before or after the decision. Dorsal but not ventral PCC engagement further extended to memory encoding, recognition, and confidence judgments. Together, these findings reveal a consistent functional dissociation, identifying dorsal PCC as a domain-general interface between evaluative and mnemonic systems. In doing so, they align human and non-human primate accounts of PCC function and help orient future targeted studies of its role in cognition and disease.

Research on the neural basis of major depressive disorder suggests that it is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neuronal systems lead to diminished emotion regulation and intrusive rumination. Subregions of the prefrontal cortex are thought to be sources of this disinhibition. However, due to limited opportunities for intracranial recordings from humans with major depression, this hypothesis has not been directly tested. Here, we use intracranial recordings from the dorsolateral prefrontal, orbitofrontal, and anterior cingulate cortices from patients with major depression to measure daily fluctuations in self-reported depression symptom severity. Results indicate that directed connectivity within the delta frequency band, which has been linked to cortical inhibition, transiently increases intensity during negative mood. Symptom severity also shifts as connectivity patterns within the left and right prefrontal cortices become imbalanced. Our findings support the overarching hypothesis that depression worsens with prefrontal disinhibition and functional imbalance between hemispheres.

In subjects implanted with intracranial electrodes, we use two different stories involving the same person (or place) to evaluate whether and to what extent context modulates human single-neuron responses. Nearly all neurons (97% during encoding and 100% during recall) initially responding to a person/place do not modulate their response with context. Likewise, nearly none (<1%) of the initially non-responsive neurons show conjunctive coding, responding to particular persons/places in a particular context during the tasks. In line with these findings, taking all neurons together it is possible to decode the person/place being depicted in each story, but not the particular story. Moreover, the neurons show consistent results across encoding and recall of the stories and during passive viewing of pictures. These results suggest a context invariant, non-conjunctive coding of memories at the single-neuron level in the human hippocampus and amygdala, in contrast to what has been described in other species.

Recent evidence indicates that the intraparietal sulcus (IPS) may play a causal role in action stopping, potentially representing a novel neuromodulation target for inhibitory control dysfunctions. Here, we leverage intracranial recordings in human subjects to establish the timing and directionality of information flow between IPS and prefrontal and cingulate regions during action stopping. Prior to successful inhibition, information flows primarily from the inferior frontal gyrus (IFG), a critical inhibitory control node, to IPS. In contrast, during stopping errors the communication between IPS and IFG is lacking, and IPS is engaged by posterior cingulate cortex, an area outside of the classical inhibition network and typically associated with default mode. Anterior cingulate and orbitofrontal cortex also display performance-dependent connectivity with IPS. Our functional connectivity results provide direct electrophysiological evidence that IPS is recruited by frontal and anterior cingulate areas to support action plan monitoring and updating, and by posterior cingulate during control failures.

Efficient and accurate spike sorting is critical for isolating single neurons from extracellular recordings to distinguish neural activity of interest. However, while the electrodes and acquisition systems for non-human electrophysiology have been enhanced over the past decades to enable higher-yield single-neuron detections, those advances have not been translated into human electrophysiology. Single-wire electrodes are still ubiquitously used, and although acquisition systems have augmented their signal-to-noise ratio over the last 15 years, we are still limited by their low electrode count. Moreover, unlike non-human recordings, human recordings often take place in hospitals where different noise sources and subject breaks can compromise the recording quality during experimental sessions. To bridge this gap, this work presents an automatic, open-source spike sorting pipeline that leverages contemporary computational capabilities and is tailored to single-neuron recordings from humans acquired via microwires. The pipeline is implemented in both MATLAB and Python, ensuring accessibility and compatibility across computational environments. Its modular and comprehensive structure supports customization and even opportunities for new developments as per the requirements of the user and the application. One feature is a data-driven automatic module to remove narrow-band interference, besides electrical line noise, which can be an essential tool while recording in clinical settings, particularly for online processing implementations. Following spike detection, the pipeline implements an artifact rejection module that separates waveforms that are unlikely to be associated with actual spikes. Additionally, we introduce a configurable feature-extraction, clustering, and benchmarking framework that not only allows flexibility in employing user-defined or conventional algorithms, such as wavelet transform with superparamagnetic clustering, but can also evaluate multi-method agreement among the different sorters. The pipeline also utilizes established and novel quality metrics to support semiautomatic curation of isolated clusters. Furthermore, we can integrate the customized pipeline with experimental tasks by removing task-unrelated waveforms (e.g., during a break in a task), and prevent over-clustering with the aid of metrics for comparing response profiles. Thus, the presented pipeline addresses the three-pronged objectives of algorithm-adaptability, rigorous validation, and human single-neuron recording optimization to support clinical and cognitive neuroscience applications. ### Competing Interest Statement The authors have declared no competing interest. Medical College of Wisconsin, Milwaukee, US Clinical and Translational Science Institute, Milwaukee, US

Faces are critical for social interactions and their recognition constitutes one of the most important and challenging functions of the human brain. While neurons responding selectively to faces have been recorded for decades in the monkey brain, face-selective neural activations have been reported with neuroimaging primarily in the human midfusiform gyrus. Yet, the cellular mechanisms producing selective responses to faces in this hominoid neuroanatomical structure remain unknown. Here we report single neuron recordings performed in 5 human subjects (1 male, 4 females) implanted with intracerebral microelectrodes in the face-selective midfusiform gyrus, while they viewed pictures of familiar and unknown faces and places. We observed similar responses to faces and places at the single cell level, but a significantly higher number of neurons responding to faces, thus offering a mechanistic account for the face-selective activations observed in this region. Although individual neurons did not respond preferentially to familiar faces, a population level analysis could consistently determine whether or not the faces (but not the places) were familiar, only about 50ms after the initial recognition of the stimuli as faces. These results provide insights into the neural mechanisms of face processing in the human brain.

INTRODUCTION: Major depression is associated with widespread dysfunction throughout the limbic system and neocortical regions such as the orbitofrontal cortex (OFC) and the dorsolateral prefrontal cortex (dlPFC) (4–7). The brain regions comprising the limbic system, including the amygdala, ventral striatum, and anterior cingulate cortex (ACC), show altered connectivity in MDD patients (5, 7). As part of an ongoing NIH-funded trial of DBS for depression, we implanted 3 patients with both DBS leads and stereo-EEG (sEEG) electrodes to measure neural oscillations in the dlPFC, OFC, and ACC. METHODS: Three depression patients were implanted with sEEG electrodes in areas including OFC, dlPFC, and ACC. We frequently measured depression severity throughout a 9-day inpatient monitoring period using a validated adaptive severity scale (8). We measured directed connectivity using multivariate vector autoregressive models that measure information flow between the right dlPFC, OFC, and ACC during resting state). Then we examined the relationship between depression severity and GC within the delta band (1-3 Hz). RESULTS: Information flow within the delta band was positively correlated with depression severity in each patient. Each showed distinct patterns of pathophysiological connectivity. Across all patients, directed connectivity from the OFC to the ACC predicted depression severity (p < 0.05). CONCLUSIONS: Information flow within the prefrontal cortex correlates closely with depression severity. Increased OFC -> ACC connectivity may relate to increased self-appraisals of mood and diminished control of emotional state during depressive episodes.

Posterior cingulate cortex (PCC) is an enigmatic region implicated in psychiatric and neurological disease, yet its role in cognition remains unclear. Human studies link PCC to episodic memory and default mode network (DMN), while findings from the non-human primate emphasize executive processes more associated with the cognitive control network (CCN) in humans. We hypothesized this difference reflects an important functional division between dorsal (executive) and ventral (episodic) PCC. To test this, we utilized human intracranial recordings of population and single unit activity targeting dorsal PCC during an alternated executive/episodic processing task. Dorsal PCC population responses were significantly enhanced for executive, compared to episodic, task conditions, consistent with the CCN. Single unit recordings, however, revealed four distinct functional types with unique executive (CCN) or episodic (DMN) response profiles. Our findings provide critical electrophysiological data from human PCC, bridging incongruent views within and across species, furthering our understanding of PCC function.

Experimental findings show the ubiquitous presence of graded responses and tuning curves in the neocortex, particularly in visual areas [1-15]. Among these, inferotemporal-cortex (IT) neurons respond to complex visual stimuli, but differences in the neurons' responses can be used to distinguish the stimuli eliciting the responses [8, 9, 16-18]. The IT projects directly to the medial temporal lobe (MTL) [19], where neurons respond selectively to different pictures of specific persons and even to their written and spoken names [20-22]. However, it is not clear whether this is done through a graded coding, as in the neocortex, or a truly invariant code, in which the response-eliciting stimuli cannot be distinguished from each other. To address this issue, we recorded single neurons during the repeated presentation of different stimuli (pictures and written and spoken names) corresponding to the same persons. Using statistical tests and a decoding approach, we found that only in a minority of cases can the different pictures of a given person be distinguished from the neurons' responses and that in a larger proportion of cases, the responses to the pictures were different to the ones to the written and spoken names. We argue that MTL neurons tend to lack a representation of sensory features (particularly within a sensory modality), which can be advantageous for the memory function attributed to this area [23-25], and that a full representation of memories is given by a combination of mostly invariant coding in the MTL with a representation of sensory features in the neocortex.

The recent advances in the chronic implantation of electrodes have allowed the collection of extracellular activity from neurons over long periods of time. To fully take advantage of these recordings, it is necessary to track single neurons continuously, particularly when their associated waveform changes with time. Multiple spike sorting algorithms can track drifting neurons but they do not perform well in conditions like a temporary increase in the noise level, sparsely firing neurons, and changes in the number of detectable neurons. In this work, we present Spikes_Link, a general framework to track neurons under these conditions. Spikes_Link can be implemented with different spike sorting algorithms, allowing the experimenter to use the algorithm best fitted to their recording setup. The main idea behind Spikes_Link is the blockwise analysis of the recording using overlapping sets of spikes to equally represent all the putative neurons being tracked on a given block. This way, we can link classes with clusters obtained in a new block based on an overlapping metric. Moreover, the algorithm can fix temporary sorting errors (splits and merges). We compared an implementation of Spikes_Link with other algorithms using long-term simulations and obtained superior performance in all the metrics. In general, the Spikes_Link framework could be used for other clustering problems with concept drift and class imbalance.

The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings. NEW & NOTEWORTHY We propose a new fully automatic spike-sorting algorithm, including several steps that allow the selection of multiple clusters of different sizes and densities. Moreover, it defines the dimensionality of the feature space in an unsupervised way. We evaluated the performance of the algorithm with real and simulated data, from both single-channel and tetrode recordings. The proposed algorithm was able to outperform manual sorting from experts and other recent unsupervised algorithms.

Besides decades of research showing the role of the medial temporal lobe (MTL) in memory and the encoding of associations, the neural substrates underlying these functions remain unknown. We identified single neurons in the human MTL that responded to multiple and, in most cases, associated stimuli. We observed that most of these neurons exhibit no differences in their spike and local field potential (LFP) activity associated with the individual response-eliciting stimuli. In addition, LFP responses in the theta band preceded single neuron responses by ~70 ms, with the single trial phase providing fine tuning of the spike response onset. We postulate that the finding of similar neuronal responses to associated items provides a simple and flexible way of encoding memories in the human MTL, increasing the effective capacity for memory storage and successful retrieval.

Background: Over 90 years after its first recording, scalp electroencephalography (EEG) remains one of the most widely used techniques in human neuroscience research, in particular for the study of event-related potentials (ERPs). However, because of its low signal-to-noise ratio, extracting useful information from these signals continues to be a hard-technical challenge. Many studies focus on simple properties of the ERPs such as peaks, latencies, and slopes of signal deflections. New method: To overcome these limitations, we developed the Wavelet-Information method which uses wavelet decomposition, information theory, and a quantification based on single-trial decoding performance to extract information from evoked responses. Results: Using simulations and real data from four experiments, we show that the proposed approach outperforms standard supervised analyses based on peak amplitude estimation. Moreover, the method can extract information using the raw data from all recorded channels using no a priori knowledge or pre-processing steps. Comparison with existing method(s): We show that traditional approaches often disregard important features of the signal such as the shape of EEG waveforms. Also, other approaches often require some form of a priori knowledge for feature selection and lead to problems of multiple comparisons. Conclusions: This approach offers a new and complementary framework to design experiments that go beyond the traditional analyses of ERPs. Potentially, it allows a wide usage beyond basic research; such as for clinical diagnosis, brain-machine interfaces, and neurofeedback applications requiring single-trial analyses. (C) 2018 The Authors. Published by Elsevier B.V.

We propose a new fully automatic spike sorting algorithm that is able to match, or even improve, the performance of semiautomatic solutions with supervised intervention from expert users. We achieved this by incorporating: 1) a set of heuristic criteria inspired by the expert actions following the solution from semiautomatic algorithms, and 2) an improved feature selection method that increases the number of units that can be isolated from a single electrode recording. We evaluated the performance of the proposed method with real and simulated data.

Nao importa o quao util, complexa ou surpreendente seja uma descoberta relacionada com o cerebro, ela afeta magicamente a opiniao publica. Para alem do entendimento dos mecanismos neurais estao a cura de doencas neurologicas e psiquiatricas e, ainda mais atraente, o poder de compreender e modificar o comportamento das pessoas. Enquanto os avancos tem sido informados a comunidade cientifica atraves de meios tradicionais, o publico em geral receber estas noticias atraves da midia. Neste trabalho, analisamos diferentes casos paradigmaticos em que estrategias inadequadas de comunicacao e suas consequencias tiveram um impacto negativo na sociedade. Junto com a apresentacao desses casos, aconselhamos sobre a necessidade de incorporar os jornalistas ao circulo de descoberta e comunicacao, a fim de garantir a compreensao, pelo publico em geral, das descobertas e progresso da neurociencia

We present results from two experiments, in which subjects watched continuous videos of a professional magician repeatedly performing a maneuver in which a ball could "magically" appear under a cup. In all cases, subjects were asked to predict whether the ball would appear under the cup or not, while scalp EEG recordings were performed. Both experiments elicited strong and consistent behavioral and neural responses. In the first experiment, we used two blocks of videos with different probabilities of the ball appearing in the cup and found that, first, based on the behavioral responses, the subjects could track this probability change; and second, the different probabilities modulated the neural responses. In the second experiment, we introduced a control condition in which the magician performed the maneuver under the table, out of subjects' view. Comparing the two conditions (i.e., performing the maneuver within or out of the subjects' view), we found that, first, the magic trick dramatically biased the subjects' behavioral responses; and second, the two conditions led to differential neural responses, in spite of the fact that the stimulus triggering the evoked responses (seeing the ball in the cup) was exactly the same. Altogether, our results show how new insights into sensory and cognitive processing can be obtained using adapted magic tricks. Moreover, the approach of analyzing responses to continuous video presentations offers a more ecological setting compared to classic evoked potential paradigms, which are typically based on presenting static images flashed at the center of the screen.

To compare neuronal firing rates from the Globus Pallidus interna (GPi) and externa (GPe) in children undergoing DBS for different types of dystonia and to relate findings to outcome. Microelectrode data were obtained to guide electrode position in 44 children with dystonia (14 Primary, 22 Secondary Static, 8 Secondary Progressive due to Neurodegeneration and Brain Iron Accumulation (NBIA); age 3.3–18.1 years). Digitised spike trains were analysed off-line, blind to clinical data. We identified 267 GPi and 87 GPe cells. Median GPi firing frequency was higher in Primary (13.5 Hz) than Secondary Static dystonia (9.6 Hz) (p = 0.002) and higher in NBIA (25 Hz) than either Primary (p = 0.006) or Secondary Static dystonia (p = 0.00004). Median GPe firing frequency was higher in NBIA (15.9 Hz) than Secondary Static dystonia (7 Hz) (p = 0.013). The proportion of regular versus irregularly firing cells also varied significantly across groups (p < 0.001). GPi firing frequency showed a positive correlation with 1-year outcome from DBS (percentage improvement in Burke–Fahn–Marsden Dystonia Rating Scale motor score), for the group overall (p = 0.040) and particularly for the non-progressive patients (p = 0.006). Pallidal firing rates differ with dystonia type and correlate with DBS outcome. This information could guide future target selection in a more individualized approach to neuromodulation.

The analysis of single trial responses of field potentials is an important tool to study brain signals. Single trial analyses can indeed provide additional information that is obscured or simply not available in the average responses The importance of studying single trial responses is reinforced by the fact that different brain processes are correlated with trial-by-trial variation of the responses. Here, we review key studies implementing single trial analyses of field potentials using methods such as single trial latency, amplitude and power changes, spike and LFP relationships, correlations between areas, cross frequency coupling, decoding of the presented stimuli that bring light into the neural basis of perception, learning and memory.

Recordings from individual neurons in patients who are implanted with depth electrodes for clinical reasons have opened the possibility to narrow down the gap between neurophysiological studies in animals and non-invasive (e.g. functional magnetic resonance imaging, electroencephalogram, magnetoencephalography) investigations in humans. Here we provide a description of the main procedures for electrode implantation and recordings, the experimental paradigms used and the main steps for processing the data. We also present key characteristics of the so-called concept cells', neurons in the human medial temporal lobe with selective and invariant responses that represent the meaning of the stimulus, and discuss their proposed role in declarative memory. Finally, we present novel results dealing with the stability of the representation given by these neurons, by studying the effect of stimulus repetition in the strength of the responses. In particular, we show that, after an initial decay, the response strength reaches an asymptotic value after approximately 15 presentations that remains above baseline for the whole duration of the experiment.