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Objective: Mind-wandering is a pervasive human brain state and, when in excess, may promote negative affect and neuropsychiatric conditions. Mindfulness meditation may promote alternate brain states, improving affect and reducing stress. An understanding of the neural basis between these brain states could thus advance treatment of neuropsychiatric conditions, including those associated with epilepsy., Methods: To explore the neural basis of mindfulness meditation versus mind-wandering, we enrolled eight patients in a trial of structured mindfulness meditation and open mind-wandering who underwent stereo electroencephalography (sEEG) within the mesial temporal lobe for seizure localization. Electrophysiology was compared between mind-wandering and mindfulness separately for epileptic and non-epileptic MTL. Using fitting-one-over-f modeling, periodic components of electrophysiology were compared in canonical frequency bands of theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-55 Hz). Aperiodic components of the power spectra were assessed by the model offset, knee, and exponent., Results: We found a significant reduction in gamma power (30-55 Hz) within the mesial temporal lobe (MTL) during mindfulness meditation compared with mind-wandering in non-epileptic (p = 1.20E-4) but not in epileptic MTL (p = 0.352).There was also a significant difference between epileptic versus non-epileptic MTL in gamma power between conditions (p = 0.011). There were no significant changes in power across any frequency band within epileptic mesial temporal MTL between brain states. Conversely, there were significant differences between mind-wandering and mindfulness within epileptic MTL in aperiodic components (offset, knee, and exponent, all p < 0.05), while no differences in aperiodic components were seen in non-epileptic MTL (all p > 0.70)., Significance: Intracranial electrophysiologic modulations between brain state (mind-wandering versus mindfulness) may differ between epileptic and non-epileptic MTL. Modulations in gamma activity in non-epileptic MTL may represent functional changes in brain state, while aperiodic changes in epileptic MTL may modulate propensity for seizures., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Fedor Panov, MD is a paid speaker for NeuroPace. The remaining authors have no conflicts of interest. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Flexible behavior requires gating mechanisms that encode only task-relevant information in working memory. Extant literature supports a theoretical division of labor whereby lateral frontoparietal interactions underlie information maintenance and the striatum enacts the gate. Here, we reveal neocortical gating mechanisms in intracranial EEG patients by identifying rapid, within-trial changes in regional and inter-regional activities that predict subsequent behavioral outputs. Results first demonstrate information accumulation mechanisms that extend prior fMRI (i.e., regional high-frequency activity) and EEG evidence (inter-regional theta synchrony) of distributed neocortical networks in working memory. Second, results demonstrate that rapid changes in theta synchrony, reflected in changing patterns of default mode network connectivity, support filtering. Graph theoretical analyses further linked filtering in task-relevant information and filtering out irrelevant information to dorsal and ventral attention networks, respectively. Results establish a rapid neocortical theta network mechanism for flexible information encoding, a role previously attributed to the striatum., (© 2023. The Author(s).)

Background: Deep brain stimulation (DBS) is an effective therapy for a variety of refractory movement disorders. Accurate lead placement in the target nucleus is critical to ensure therapeutic effects and to minimize side effects, and intraoperative computed tomography (iCT) scan has been used to target and confirm lead position. The objective of this study is to compare the accuracy of determining the x, y, and z coordinates of final lead placement using iCT scan relative to postoperative magnetic resonance imaging (MRI)., Methods: We conducted a retrospective study on 83 patients who underwent insertion of 145 DBS leads from 2015 to 2017 at a single institution. iCT scan was merged with the preoperative MRI to determine lead coordinates on both magnetic resonance and computed tomography images independently, and the absolute differences between the x, y, and z coordinates between the 2 scans along with the Euclidean vectors were calculated., Results: The mean absolute differences ± standard error of the mean between iCT scan and postoperative MRI coordinates were as follows: x = 0.01 ± 0.09 mm (P = 0.89), y = 1.67 ± 0.14 mm (P < 0.001), and z = 2.75 ± 0.15 mm (P < 0.001). The average Euclidean vector difference was 3.21 ± 0.15 mm (P < 0.001)., Conclusions: Significant differences exist between iCT scan and postoperative MRI DBS y and z lead coordinates, but not with x coordinates. Based on this series, iCT scan is more accurate when confirming x coordinates, and less accurate for confirming y and z coordinates during DBS operations., (Crown Copyright © 2020. Published by Elsevier Inc. All rights reserved.)

Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.

Layer 4 (L4) of primary visual cortex (V1) is the main recipient of thalamocortical fibers from the dorsal lateral geniculate nucleus (LGNd). Thus, it is considered the main entry point of visual information into the neocortex and the first anatomical opportunity for intracortical visual processing before information leaves L4 and reaches supra- and infragranular cortical layers. The strength of monosynaptic connections from individual L4 excitatory cells onto adjacent L4 cells (unitary connections) is highly malleable, demonstrating that the initial stage of intracortical synaptic transmission of thalamocortical information can be altered by previous activity. However, the inhibitory network within L4 of V1 may act as an internal gate for induction of excitatory synaptic plasticity, thus providing either high fidelity throughput to supragranular layers or transmittal of a modified signal subject to recent activity-dependent plasticity. To evaluate this possibility, we compared the induction of synaptic plasticity using classical extracellular stimulation protocols that recruit a combination of excitatory and inhibitory synapses with stimulation of a single excitatory neuron onto a L4 cell. In order to induce plasticity, we paired pre- and postsynaptic activity (with the onset of postsynaptic spiking leading the presynaptic activation by 10ms) using extracellular stimulation (ECS) in acute slices of primary visual cortex and comparing the outcomes with our previously published results in which an identical protocol was used to induce synaptic plasticity between individual pre- and postsynaptic L4 excitatory neurons. Our results indicate that pairing of ECS with spiking in a L4 neuron fails to induce plasticity in L4-L4 connections if synaptic inhibition is intact. However, application of a similar pairing protocol under GABAARs inhibition by bath application of 2μM bicuculline does induce robust synaptic plasticity, long term potentiation (LTP) or long term depression (LTD), similar to our results with pairing of pre- and postsynaptic activation between individual excitatory L4 neurons in which inhibitory connections are not activated. These results are consistent with the well-established observation that inhibition limits the capacity for induction of plasticity at excitatory synapses and that pre- and postsynaptic activation at a fixed time interval can result in a variable range of plasticity outcomes. However, in the current study by virtue of having two sets of experimental data, we have provided a new insight into these processes. By randomly mixing the assorting of individual L4 neurons according to the frequency distribution of the experimentally determined plasticity outcome distribution based on the calculated convergence of multiple individual L4 neurons onto a single postsynaptic L4 neuron, we were able to compare then actual ECS plasticity outcomes to those predicted by randomly mixing individual pairs of neurons. Interestingly, the observed plasticity profiles with ECS cannot account for the random assortment of plasticity behaviors of synaptic connections between individual cell pairs. These results suggest that connections impinging onto a single postsynaptic cell may be grouped according to plasticity states. [ABSTRACT FROM AUTHOR]