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1. Variable but not random: temporal pattern coding in a songbird brain area necessary for song modification

   https://doi.org/10.1152/jn.00034.2019  

   Palmer, S. E.; Wright, B. D.; Doupe, A. J.; Kao, M. H. (February 2021, Journal of Neurophysiology)

   null (Ed.)

   Practice of a complex motor gesture involves motor exploration to attain a better match to target, but little is known about the neural code for such exploration. We examine spiking in a premotor area of the songbird brain critical for song modification and quantify correlations between spiking and time in the motor sequence. While isolated spikes code for time in song during performance of song to a female bird, extended strings of spiking and silence, particularly bursts, code for time in song during undirected (solo) singing, or “practice.” Bursts code for particular times in song with more information than individual spikes, and this spike-spike synergy is significantly higher during undirected singing. The observed pattern information cannot be accounted for by a Poisson model with a matched time-varying rate, indicating that the precise timing of spikes in both bursts in undirected singing and isolated spikes in directed singing code for song with a temporal code. Temporal coding during practice supports the hypothesis that lateral magnocellular nucleus of the anterior nidopallium neurons actively guide song modification at local instances in time. NEW & NOTEWORTHY This paper shows that bursts of spikes in the songbird brain during practice carry information about the output motor pattern. The brain’s code for song changes with social context, in performance versus practice. Synergistic combinations of spiking and silence code for time in the bird’s song. This is one of the first uses of information theory to quantify neural information about a motor output. This activity may guide changes to the song. 

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2. Interictal epileptiform discharges contribute to word‐finding difficulty in epilepsy through multiple cognitive mechanisms

   https://doi.org/10.1111/epi.17781  

   Silva, Alexander_B; Leonard, Matthew_K; Oganian, Yulia; D'Esopo, Emma; Krish, Devon; Kopald, Brandon; Tran, Edwina_B; Chang, Edward_F; Kleen, Jonathan_K (October 2023, Epilepsia)

   Abstract ObjectiveCognitive impairment often impacts quality of life in epilepsy even if seizures are controlled. Word‐finding difficulty is particularly prevalent and often attributed to etiological (static, baseline) circuit alterations. We sought to determine whether interictal discharges convey significant superimposed contributions to word‐finding difficulty in patients, and if so, through which cognitive mechanism(s). MethodsTwenty‐three patients undergoing intracranial monitoring for drug‐resistant epilepsy participated in multiple tasks involving word production (auditory naming, short‐term verbal free recall, repetition) to probe word‐finding difficulty across different cognitive domains. We compared behavioral performance between trials with versus without interictal discharges across six major brain areas and adjusted for intersubject differences using mixed‐effects models. We also evaluated for subjective word‐finding difficulties through retrospective chart review. ResultsSubjective word‐finding difficulty was reported by the majority (79%) of studied patients preoperatively. During intracranial recordings, interictal epileptiform discharges (IEDs) in the medial temporal lobe were associated with long‐term lexicosemantic memory impairments as indexed by auditory naming (p = .009), in addition to their established impact on short‐term verbal memory as indexed by free recall (p = .004). Interictal discharges involving the lateral temporal cortex and lateral frontal cortex were associated with delayed reaction time in the auditory naming task (p = .016 andp = .018), as well as phonological working memory impairments as indexed by repetition reaction time (p = .002). Effects of IEDs across anatomical regions were strongly dependent on their precise timing within the task. SignificanceIEDs appear to act through multiple cognitive mechanisms to form a convergent basis for the debilitating clinical word‐finding difficulty reported by patients with epilepsy. This was particularly notable for medial temporal spikes, which are quite common in adult focal epilepsy. In parallel with the treatment of seizures, the modulation of interictal discharges through emerging pharmacological means and neurostimulation approaches may be an opportunity to help address devastating memory and language impairments in epilepsy. 

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3. Integrating Temporal Information to Spatial Information in a Neural Circuit

   Wang, Mien Brabeea; Lynch, Nancy (January 2019, 7th Workshop on Biological Distributed Algorithms (BDA))

   In this paper, we consider a network of spiking neurons with a deterministic synchronous firing rule at discrete time. We propose three problems – “first consecutive spikes counting”,“total spikes counting” and “k-spikes temporal to spatial encoding” – to model how brains extract temporal information into spatial information from different neural codings. For a max input length T, we design three networks that solve these three problems with matching lower bounds in bothtime O(T) and number of neurons O(logT) in all three questions. 

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4. Flexible, high‐resolution cortical arrays with large coverage capture microscale high‐frequency oscillations in patients with epilepsy

   https://doi.org/10.1111/epi.17642  

   Barth, Katrina J; Sun, James; Chiang, Chia‐Han; Qiao, Shaoyu; Wang, Charles; Rahimpour, Shervin; Trumpis, Michael; Duraivel, Suseendrakumar; Dubey, Agrita; Wingel, Katie E; et al (July 2023, Epilepsia)

   Abstract ObjectiveEffective surgical treatment of drug‐resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High‐frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (μECoG) arrays, which provide high spatial resolution over large cortical surface areas. MethodsWe used novel liquid crystal polymer thin‐film μECoG arrays (.76–1.72‐mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80–250 Hz) and fast ripple (250–600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal‐to‐noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data. ResultsWe found visually delineable subregions of elevated HFO activity within each μECoG recording. Forty‐seven percent of HFOs occurred on single 200‐μm‐diameter recording contacts, with minimal high‐frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a .95‐mm radius. Through spatial averaging, we estimated that macrocontacts with 2–3‐mm diameter would only capture 44% of the HFOs detected in our μECoG recordings. SignificanceThese results demonstrate that thin‐film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug‐resistant epilepsy. 

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5. Calibrating Bayesian decoders of neural spiking activity

   https://doi.org/10.1523/JNEUROSCI.2158-23.2024  

   Wei, Ganchao; Tajik_Mansouri, Zeinab; Wang, Xiaojing; Stevenson, Ian H (March 2024, The Journal of Neuroscience)

   Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine: 1) decoding the direction of grating stimuli from spike recordings in primary visual cortex in monkeys, 2) decoding movement direction from recordings in primary motor cortex in monkeys, 3) decoding natural images from multi-region recordings in mice, and 4) decoding position from hippocampal recordings in rats. For each setting we characterize the overconfidence, and we describe a possible method to correct miscalibration post-hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain machine interfaces that more accurately reflect confidence levels when identifying external variables. Significance Statement Bayesian decoding is a statistical technique for making probabilistic predictions about external stimuli or movements based on recordings of neural activity. These predictions may be useful for robust brain machine interfaces or for understanding perceptual or behavioral confidence. However, the probabilities produced by these models do not always match the observed outcomes. Just as a weather forecast predicting a 50% chance of rain may not accurately correspond to an outcome of rain 50% of the time, Bayesian decoders of neural activity can be miscalibrated as well. Here we identify and measure miscalibration of Bayesian decoders for neural spiking activity in a range of experimental settings. We compare multiple statistical models and demonstrate how overconfidence can be corrected. 

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