Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation

While experience unfolds continuously, memories are organized as a set of discrete events that bind together the “where”, “when”, and “what” of episodic memory. This segmentation of continuous experience is thought to be facilitated by the detection of salient environmental or cognitive events. However, the underlying neural mechanisms and how such segmentation shapes episodic memory representations remain unclear. We recorded from single neurons in the human medial temporal lobe while subjects watched videos with different types of embedded boundaries and were subsequently evaluated for memories of the video contents. Here we show neurons that signal the presence of cognitive boundaries between subevents from the same episode and neurons that detect the abstract separation between different episodes. The firing rate and spike timing of these boundary-responsive neurons were predictive of later memory retrieval accuracy. At the population level, abrupt neural state changes following boundaries predicted enhanced memory strength but impaired order memory, capturing the behavioral tradeoff subjects exhibited when recalling episodic content versus temporal order. Successful retrieval was associated with reinstatement of the neural state present following boundaries, indicating that boundaries structure memory search. These findings reveal a neuronal substrate for detecting cognitive boundaries and show that cognitive boundary signals facilitate the mnemonic organization of continuous experience as a set of discrete episodic events

Action boosts episodic memory encoding in humans via engagement of a noradrenergic system

We are constantly interacting with our environment whilst we encode memories. However, how actions influence memory formation remains poorly understood. Goal-directed movement engages the locus coeruleus (LC), the main source of noradrenaline in the brain. Noradrenaline is also known to enhance episodic encoding, suggesting that action could improve memory via LC engagement. Here we demonstrate, across seven experiments, that action (Go-response) enhances episodic encoding for stimuli unrelated to the action itself, compared to action inhibition (NoGo). Functional magnetic resonance imaging, and pupil diameter as a proxy measure for LC-noradrenaline transmission, indicate increased encodingrelated LC activity during action. A final experiment, replicated in two independent samples, confirmed a novel prediction derived from these data that emotionally aversive stimuli, which recruit the noradrenergic system, modulate the mnemonic advantage conferred by Go-responses relative to neutral stimuli. We therefore provide converging evidence that action boosts episodic memory encoding via a noradrenergic mechanism

Aversive memory formation in humans involves an amygdala-hippocampus phase code

Memory for aversive events is central to survival but can become maladaptive in psychiatric disorders. Memory enhancement for emotional events is thought to depend on amygdala modulation of hippocampal activity. However, the neural dynamics of amygdala-hippocampal communication during emotional memory encoding remain unknown. Using simultaneous intracranial recordings from both structures in human patients, here we show that successful emotional memory encoding depends on the amygdala theta phase to which hippocampal gamma activity and neuronal firing couple. The phase difference between subsequently remembered vs. not-remembered emotional stimuli translates to a time period that enables lagged coherence between amygdala and downstream hippocampal gamma. These results reveal a mechanism whereby amygdala theta phase coordinates transient amygdala -hippocampal gamma coherence to facilitate aversive memory encoding. Pacing of lagged gamma coherence via amygdala theta phase may represent a general mechanism through which the amygdala relays emotional content to distant brain regions to modulate other aspects of cognition, such as attention and decision-making

Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS images

Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data in a 10 × 10 km grid basis were used. The percentage of fire events met the variations suffered by some of the spectral indices, following a linear regression in both Galicia and Asturias. The Enhanced Vegetation Index (EVI) was the index leading to the best results. Based on these results, a simple fire danger model was established, using logistic regression, by combining the EVI variation with other variables, such as fire history in each cell and period of the year. A seventy percent overall concordance was obtained between estimated and observed fire frequency

Markov Influence Diagrams.

Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analyses. Using a causal graph that may contain several variables per cycle, MIDs can model various patient characteristics without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs-including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis-with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs; i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable

Contribution of the Microbial Communities Detected on an Oil Painting on Canvas to Its Biodeterioration

In this study, we investigated the microbial community (bacteria and fungi) colonising an oil painting on canvas, which showed visible signs of biodeterioration. A combined strategy, comprising culture-dependent and -independent techniques, was selected. The results derived from the two techniques were disparate. Most of the isolated bacterial strains belonged to related species of the phylum Firmicutes, as Bacillus sp. and Paenisporosarcina sp., whereas the majority of the non-cultivable members of the bacterial community were shown to be related to species of the phylum Proteobacteria, as Stenotrophomonas sp. Fungal communities also showed discrepancies: the isolated fungal strains belonged to different genera of the order Eurotiales, as Penicillium and Eurotium, and the non-cultivable belonged to species of the order Pleosporales and Saccharomycetales. The cultivable microorganisms, which exhibited enzymatic activities related to the deterioration processes, were selected to evaluate their biodeteriorative potential on canvas paintings; namely Arthrobacter sp. as the representative bacterium and Penicillium sp. as the representative fungus. With this aim, a sample taken from the painting studied in this work was examined to determine the stratigraphic sequence of its cross-section. From this information, “mock paintings,” simulating the structure of the original painting, were prepared, inoculated with the selected bacterial and fungal strains, and subsequently examined by micro-Fourier Transform Infrared spectroscopy, in order to determine their potential susceptibility to microbial degradation. The FTIR-spectra revealed that neither Arthrobacter sp. nor Penicillium sp. alone, were able to induce chemical changes on the various materials used to prepare “mock paintings.” Only when inoculated together, could a synergistic effect on the FTIR-spectra be observed, in the form of a variation in band position on the spectrum.The FTIR analyses performed in this study were financed by the Junta de Andalucía (RNM-325 group). The molecular analyses performed in this study were financed by the Austrian Science Fund (FWF) project ‘Hertha-Firnberg T137’ and the Spanish Ministry of Science and Innovation (Project CTQ2008-06727-C03-03). G. Piñar also thanks the “Elise-Richter V194-B20” projects

How can we process microelectrode data to isolate single neurons in humans?

Extracellular recordings of single neurons are a commonly used method to study the neural mechanisms of cognition. While extensively used in animal models, rare clinical cases also allow such recordings from the human brain using high-impedance microwires. These recordings allow the study of the activity of individual human neurons during cognitive tasks at single-spike resolution. Here, we discuss one such clinical scenario: microwires embedded in depth electrodes implanted in epilepsy patients. We outline the three main processing steps to derive well isolated putative single neurons from such recordings: signal processing, spike detection, and spike sorting. We provide an overview of the state of the art in the acquisition and processing of extracellular recordings with microwires, review a typical experimental setup, spike sorting and detection algorithms. We conclude by providing a step-by-step example, visualizing each intermediate processing step. Together, this chapter provides a practical guide on how to utilize signal processing, spike detection, and spike sorting to derive high-quality single-neuron recordings