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The left supramarginal gyrus (LSMG) may mediate attention to memory, and gauge memory state and performance. We performed a secondary analysis of 142 verbal delayed free recall experiments, in patients with medically-refractory epilepsy with electrode contacts implanted in the LSMG. In 14 of 142 experiments (in 14 of 113 patients), the cross-validated convolutional neural networks (CNNs) that used 1-dimensional(1-D) pairs of convolved high-gamma and beta tensors, derived from the LSMG recordings, could label recalled words with an area under the receiver operating curve (AUROC) of greater than 60% [range: 60-90%]. These 14 patients were distinguished by: 1) higher amplitudes of high-gamma bursts; 2) distinct electrode placement within the LSMG; and 3) superior performance compared with a CNN that used a 1-D tensor of the broadband recordings in the LSMG. In a pilot study of 7 of these patients, we also cross-validated CNNs using paired 1-D convolved high-gamma and beta tensors, from the LSMG, to: a) distinguish word encoding epochs from free recall epochs [AUC 0.6-1]; and distinguish better performance from poor performance during delayed free recall [AUC 0.5-0.86]. These experiments show that bursts of high-gamma and beta generated in the LSMG are biomarkers of verbal memory state and performance., Competing Interests: S.A.W, N.S, J.K, and D.S are inventors on a related provisional patent 63/621,314. S.A.W and M.S. are inventors of granted patent US20240090817A1. M.R.S. is a consultant/advisor for Medtronic (fee to institution); has received research support (to institution) from Eisai, Engage Therapeutics, Medtronic, Neurelis, Pfizer, SK Life Science, Takeda, UCB, Cerevel, and Xenon; and has been a speaker for Eisai, Medscape, NeurologyLive, UCB, and Projects in Knowledge. K.W.M is the CEO of Soenia®.

Endogenous variation in brain state and stimulus-specific evoked activity can both contribute to successful encoding. Previous studies, however, have not clearly distinguished among these components. We address this question by analysing intracranial EEG recorded from epilepsy patients as they studied and subsequently recalled lists of words. We first trained classifiers to predict recall of either single items or entire lists and found that both classifiers exhibited similar performance. We found that list-level classifier output-a biomarker of successful encoding-tracked item presentation and recall events, despite having no information about the trial structure. Across widespread brain regions, decreased low- and increased high-frequency activity (HFA) marked successful encoding of both items and lists. We found regional differences in the hippocampus and prefrontal cortex, where in the hippocampus HFA correlated more strongly with item recall, whereas, in the prefrontal cortex, HFA correlated more strongly with list performance. Despite subtle differences in item- and list-level features, the similarity in overall classification performance, spectral signatures of successful recall and fluctuations of spectral activity across the encoding period argue for a shared endogenous process that causally impacts the brain's ability to learn new information., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Hypothesis: We used the uniquely high combined spatial and temporal resolution of magnetoencephalography to characterize working memory (WM)-related modulation of beta band activity in neuroleptic-free patients with schizophrenia in comparison to a large sample of performance-matched healthy controls. We also tested for effects of antipsychotic medication on identified differences in these same patients., Study Design: Inpatients with schizophrenia (n = 21) or psychotic disorder not otherwise specified (n = 4) completed N-back and control tasks during magnetoencephalography while on placebo and during antipsychotic medication treatment, in a blinded, randomized, counterbalanced manner. Healthy, performance-matched controls (N = 100) completed the same tasks. WM-related neural activation was estimated as beta band (14-30 Hz) desynchronization throughout the brain in successive 400 ms time windows. Voxel-wise statistical comparisons were performed between controls and patients while off-medication at each time window. Significant clusters resulting from this between-groups analysis were then used as regions-of-interest, the activations of which were compared between on- and off-medication conditions in patients., Study Results: Controls showed beta-band desynchronization (activation) of a fronto-parietal network immediately preceding correct button press responses-the time associated with WM updating and task execution. Altered activation in medication-free patients occurred largely during this time, in prefrontal, parietal, and visual cortices. Medication altered patients' neural responses such that the activation time courses in these regions-of-interest more closely resembled those of controls., Conclusions: These findings demonstrate that WM-related beta band alterations in schizophrenia are time-specific and associated with neural systems targeted by antipsychotic medications. Future studies may investigate this association by examining its potential neurochemical basis., (Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center 2023.)

The role of the left ventral lateral parietal cortex (VPC) in episodic memory is hypothesized to include bottom-up attentional orienting to recalled items, according to the dual-attention model (Cabeza et al., 2008). However, its role in memory encoding could be further clarified, with studies showing both positive and negative subsequent memory effects (SMEs). Furthermore, few studies have compared the relative contributions of sub-regions in this functionally heterogeneous area, specifically the anterior VPC (supramarginal gyrus/BA40) and the posterior VPC (angular gyrus/BA39), on a within-subject basis. To elucidate the role of the VPC in episodic encoding, we compared SMEs in the intracranial EEG across multiple frequency bands in the supramarginal gyrus (SmG) and angular gyrus (AnG), as twenty-four epilepsy patients with indwelling electrodes performed a free recall task. We found a significant SME of decreased theta power and increased high gamma power in the VPC overall, and specifically in the SmG. Furthermore, SmG exhibited significantly greater spectral tilt SME from 0.5 to 1.6 s post-stimulus, in which power spectra slope differences between recalled and unrecalled words were greater than in the AnG (p = 0.04). These results affirm the contribution of VPC to episodic memory encoding, and suggest an anterior-posterior dissociation within VPC with respect to its electrophysiological underpinnings., Competing Interests: Declaration of Competing Interest MRS is a consultant/advisor for Medtronic (fee to institution); is a consultant for NeurologyLive; received research support (to institution) from Eisai, Engage, Medtronic, Neurelis, Pfizer, SK Life Science, Inc., Takeda, UCB, and Xenon; and has been a speaker for Eisai., (Copyright © 2020. Published by Elsevier Inc.)

A single-nucleotide polymorphism in the promoter region of the Matrix Metalloproteinase-9 (MMP9) gene, rs3918242, has been shown to affect MMP9 expression in macrophages and was associated with schizophrenia by two independent groups. However, rs3918242's effects on MMP9 expression were not replicable in cell lines or brain tissue. Additionally, publically available data indicate that rs3918242 genotype is related not to MMP9 expression, but rather to expression of SLC12A5, a nearby gene coding for a K+/Cl- cotransporter, whose expression has also been related to schizophrenia. Here, we studied brain structure and function in healthy participants stratified by rs3918242 genotype using structural MRI (N = 298), functional MRI during an N-back working memory task (N = 554), and magnetoencephalography (MEG) during the same task (N = 190). We found rs3918242 was associated with gray matter volume (GMV) in the insula and dorsolateral prefrontal cortex bilaterally, closely replicated in discovery and replication samples; and with inferior parietal lobule (IPL) GMV when the samples were meta-analytically combined. Additionally, using both fMRI and MEG, rs3918242 was associated with right IPL working memory-related activation, replicated in two cohorts and across imaging modalities. These convergent results provide further impetus for examinations of the relationship of SLC12A5 with brain structure and function in neuropsychiatric disease., (Published by Oxford University Press 2019.)

In human electrophysiology research, the high gamma part of the power spectrum (~>60 Hz) is a relatively new area of investigation. Despite a low signal-to-noise ratio, evidence exists that it contains significant information about activity in local cortical networks. Here, using magnetoencephalography (MEG), we found high gamma activity when comparing data from an n-back working memory task to resting data in a large sample of normal volunteers. Initial analysis of power spectra from 0-back, 2-back, and rest trials showed three frequency bands exhibiting task-related differences: alpha, beta, and high gamma. Unlike alpha and beta, the high gamma spectrum was broad, without a peak at a single frequency. In addition, power in high gamma was highest for the 2-back and lowest during rest, while the opposite pattern occurred in the other bands. Beamformer source localization of each of the three frequency bands revealed a distinct set of sources for high gamma. These included several regions of prefrontal cortex that exhibited greater power when both n-back conditions were compared to rest. A subset of these regions had more power when the 2-back was compared to 0-back, which indicates a role in working memory performance. Our results show that high gamma will be important for understanding cortical processing during cognitive and other tasks. Furthermore, data from human intracortical recordings suggest that high gamma is the aggregate of spiking in local cortical networks, which implies that MEG could serve to bridge experimental modalities by noninvasively observing task-related modulation of spiking rates., (© 2018 Wiley Periodicals, Inc.)

Standard-of-care biological treatment of schizophrenia remains dependent upon antipsychotic medications, which demonstrate D 2 receptor affinity and elicit variable, partial clinical responses via neural mechanisms that are not entirely understood. In the striatum, where D 2 receptors are abundant, antipsychotic medications may affect neural function in studies of animals, healthy volunteers, and patients, yet the relevance of this to pharmacotherapeutic actions remains unresolved. In this same brain region, some individuals with schizophrenia may demonstrate phenotypes consistent with exaggerated dopaminergic signaling, including alterations in dopamine synthesis capacity; however, the hypothesis that dopamine system characteristics underlie variance in medication-induced regional blood flow changes has not been directly tested. We therefore studied a cohort of 30 individuals with schizophrenia using longitudinal, multi-session [ 15 O]-water and [ 18 F]-FDOPA positron emission tomography to determine striatal blood flow during active atypical antipsychotic medication treatment and after at least 3 weeks of placebo treatment, along with presynaptic dopamine synthesis capacity (ie, DOPA decarboxylase activity). Regional striatal blood flow was significantly higher during active treatment than during the placebo condition. Furthermore, medication-related increases in ventral striatal blood flow were associated with more robust amelioration of excited factor symptoms during active medication and with higher dopamine synthesis capacity. These data indicate that atypical medications enact measureable physiological alterations in limbic striatal circuitry that vary as a function of dopaminergic tone and may have relevance to aspects of therapeutic responses.

The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1-dog1, bird2-dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1-dog_picture1, bird_picture2-dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation., (Copyright © 2016 Elsevier B.V. All rights reserved.)