Classic lesion case-control studies suggest minimal involvement of the medial temporal lobe (MTL) in visual working memory (VWM), particularly for simple stimulus features like colour or orientation. However, recent intracranial recordings implicate the MTL-especially the hippocampus-in supporting VWM precision by distinguishing similar visual features to reduce representational variability during short retention intervals. Meanwhile, reports that MTL activity scales with VWM set size have raised the possibility that the MTL contributes not only to the quality but also the quantity of retained VWM content-an idea motivated by models positing a unitary memory strength metric to account for behavioural expressions of both VWM quantity and quality. To clarify the extent to which MTL lesions affect VWM quality, quantity or both, we examined VWM recall performance in 40 neurological cases with drug-resistant epilepsy before and after their brain surgery for seizure treatment. Of these, 19 had lesions involving the hippocampus, while 21 had either no lesions or lesions outside the hippocampus. Using a controlled VWM task with fixed set size and minimal non-target recall errors, we modelled participants' recall responses to estimate recall variability as an inverse measure of VWM precision and the probability of recall success as the proportion of trials not attributable to failed, uniform recall responses. We found that lesions affecting the hippocampus in the MTL led to a significant increase in recall variability, indicating reduced VWM precision after surgery. Voxel-based lesion-symptom mapping further revealed a robust association between hippocampal damage and increased recall variability, even after controlling for overall brain lesion volume. In contrast, total lesion volume-but not hippocampal lesion extent-predicted reduced recall success rate, suggesting that broader lesion burden constrains how much content is retained, resulting in more failed recall responses. An alternative model assuming a unitary memory strength metric captured the overall performance decline with increasing total lesion volume but could not account for the MTL-specific effects. Together, these findings highlight the MTL's role in preserving the fidelity-rather than the mere presence-of VWM representations. They challenge models that treat VWM quality and quantity as interchangeable consequences of a single underlying memory strength parameter. By identifying distinct neural correlates for each component, our results point to VWM precision as a sensitive behavioural marker-one that may be useful for tracking functional changes in individuals with memory impairment, including those with focal brain lesions.

Despite promising results, it remains unclear how to optimally target and personalize closed-loop stimulation to ameliorate deficits in memory and other cognitive functions. We hypothesized that evoked connectivity, the measurement of neural pathway activation using single pulses of electrical stimulation, can guide patient specific selection of stimulation location and parameters for memory. We characterized brain wide evoked connectivity profiles of stimulation in memory-related brain regions recorded from 81 patients undergoing intracranial monitoring for epilepsy, showing that greater evoked connectivity between the lateral temporal cortex and the broader memory network (including the mesial temporal lobe, limbic regions and prefrontal cortex) corroborates observations of memory improvement by lateral temporal cortex stimulation. We first found that the lateral temporal cortex, compared to other stimulated regions, evokes the greatest and most distributed connectivity response throughout other memory related regions. Evoked connectivity in downstream regions is greatest when stimulating a previously identified optimal target for memory improvement, bordering white matter at the rostrocaudal center of the middle temporal gyrus. Evoked connectivity corroborates other biomarkers of memory improvement by closed loop stimulation, including increased resting state functional connectivity between stimulated and recorded sites and increased modulation of oscillatory power. These results provide insight into the network mechanisms of stimulation for memory and suggest that evoked connectivity can more broadly predict the functional effects of closed loop stimulation to prospectively guide targeting and parameter selection. ### Competing Interest Statement R.E.G. holds a less than 5% equity interest in Nia Therapeutics, Inc, a company intended to develop and commercialize brain stimulation therapies for memory restoration. M.J.K. holds a greater than 5% equity interest in Nia Therapeutics, Inc. ### Funding Statement This work was supported by the DARPA Restoring Active Memory (RAM) program (Cooperative Agreement N66001-14-2-4032). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All participating patients provided informed consent prior to research under a protocol approved by the University of Pennsylvania institutional review board (IRB). Each participating hospital was sanctioned for research under a reliance agreement with the University of Pennsylvania IRB. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data in the present work are available online upon request at: https://memory.psych.upenn.edu/RAM\_Public\_Data [https://memory.psych.upenn.edu/RAM\_Public\_Data][1] [1]: https://memory.psych.upenn.edu/RAM_Public_Data

The genomic locus that encodes the Leucine-rich repeat kinase 2 (LRRK2) gene is highly pleiotropic and associated with Parkinson’s disease (PD). Coding variants associated with risk of PD act as gain of function kinase mutations increasing phosphorylation of RAB substrates, and non-coding variants in the promoter region of LRRK2 increase expression of the gene, notably in immune cells. If regulation of LRRK2 expression is a causal contributor to PD, it is important to understand the mechanism(s) by which LRRK2 is regulated, particularly in the context of inflammation. Here, we show that interferon-ɣ exposure induces robust LRRK2 activation in human iPSC-derived microglia through signaling of the Janus-activated Kinase complex to phosphorylate STAT1, which then binds to the LRRK2 promoter and is associated with remodeling of chromatin structure in this genomic locus. Additional regulatory mechanisms include the stress-induced transcription factor and long non-coding RNA encoded at the same locus, resulting in increased LRRK2 mRNA levels. We also show evidence of the same effect in acutely cultured human brain slices. While we were unable to demonstrate any induction of Lrrk2 mRNA in the mouse brain, the introduction of a human bacterial artificial chromosome transgene into the mouse genome recapitulated sensitivity to interferon-ɣ in microglia. A comparative genomic analysis across mammals suggests that these species differences are driven by regulatory regions upstream of LRRK2 that are specific to anthropoid primates. These results demonstrate that there are differences between species in how genes associated with human diseases are regulated and provide important information that should be incorporated in disease modeling.

Attention aids in prioritizing information relevant to our present goals. For example, attention may augment sensory processing by modulating neural activity for low-level features of the attended items. Attention can also prioritize the contents of memory, facilitating our ability to remember some pieces of information while ignoring others. Here, we examine how using attention to modulate the contents of memory affects temporally organized sequences of neuronal spiking in the human anterior temporal lobe. These spike sequences represent higher-level semantic information and occur repeatedly and consistently as participants process and encode words into memory. Our results demonstrate that attention deployed to prioritize words for memory increases the consistency of these spike sequences. Further, retroactively cueing words elicits the replay of these sequences. Our data, therefore, suggest that paying attention to prioritizing semantic content for memory may improve the temporal organization of neural spiking representations of semantic information in the anterior temporal lobe.

(A) Phase contrast images showed the cytotoxicity of TMZ (400 mM)/ipatasertib (Ipa, 2 mM) combination treatment in the brain tumor-initiating cells (BTICs). A ferroptosis inhibitor, Ferrstain-1 (Ferr-1, 5 mM), was used to rescue the ferroptotic cell death. Bar = 100 mM. (B) Representative flow cytometric analysis of live/dead staining in BTICs in response to TMZ/Ipa treatment. (C) Statistical analysis of dead/live cell ratio in BTICs. (D) The limiting dilution assay evaluated the frequency of generating spheres in GSC827 and TSC603 after receiving TMZ/Ipa treatment. **p < 0.01.

Introduction:More than 50 million people worldwide suffer from epilepsy. Approximately 30% of epileptic patients suffer from medically refractory epilepsy (MRE), which means that over 15 million people must seek extensive treatment. One such treatment involves surgical removal of the epileptogenic zone (EZ) of the brain. However, because there is no clinically validated biomarker of the EZ, surgical success rates vary between 30%-70%. The current standard for EZ localization often requires invasive monitoring of patients for several weeks in the hospital during which intracranial EEG (iEEG) data is captured. This process is time-consuming as the clinical team must wait for seizures and visually interpret the iEEG during these events. Hence, an iEEG biomarker that does not rely on seizure observations is desirable to improve EZ localization and surgical success rates. Recently, the source-sink index (SSI) was proposed as an interictal (between seizure) biomarker of the EZ, which captures regional interactions in the brain and in particular identifies the EZ as regions being inhibited ("sinks") by neighbors ("sources") when patients are not seizing. The SSI only requires 5-min snapshots of interictal iEEG recordings. However, one limitation of the SSI is that it is computed heuristically from the parameters of dynamical network models (DNMs). Methods:In this work, we propose a formal method for detecting sink regions from DNMs, which has a strong foundation in linear systems theory. In particular, the steady-state solution of the DNM highlights the sinks and is characterized by the leading eigenvector of the state-transition matrix of the DNM. To test this, we build patient-specific DNMs from interictal iEEG data collected from 65 patients treated across 6 centers. From each DNM, we compute the average leading eigenvectors and evaluate their potential as a biomarker to accurately predict EZ and surgical success. Results:Our findings show the ability of the leading eigenvector to accurately predict EZ (average accuracy 66.81% ± 0.19%) and surgical success (average accuracy 71.9% ± 0.22%) with data from 65 patients across 6 centers from 5 min of data, which we show is comparable with the current method of localizing the EZ over several weeks. Discussion:This eigenvector biomarker has the potential to assist clinicians in localizing the EZ quickly and thus increase surgical success in patients with MRE, resulting in an improvement in patient care and quality of life.

(A) Gene expression profiling showed the expression of GPX4 and SLC7A11 in IDH-mutated NHA cells (B) ROS-Glo assay measured the oxidative stress in IDH1WT and IDH1Mut NHA cells in response to ipatasertib (Ipa, 1 mM) treatment. (C) GSH, and GSSG level was measured in IDHWT and IDH1Mut NHA cells in response to Ipa (1 mM) treatment. GSH/GSSG ratio was calculated. (D) Immunoblotting analysis evaluated cleaved PARP in IDH1WT and IDH1Mut NHA cells with TMZ (200 mM) and/or Ipa (1mM) treatment. b-actin was used as an internal control. (E) Cell viability analysis on NHA IDHWT cells under TMZ (200 mM) and Ipa (1mM) treatment. **p < 0.01.

(A) Immunoblotting analysis evaluated the siRNA knockdown efficiency of AKT in U87 cells. (B) Phase contrast imaging showed the cytotoxicity of ipatasertib (Ipa) in IDH1WT and IDH1Mut NHA cells. Bar = 100 mM. (C) Representative flow cytometric analysis of live/dead staining in IDH1WT and IDH1Mut NHA cells in response to Ipa (0, 1, and 2 mM) treatment. (D) Statistical analysis of dead/live cell ratio in IDH1WT and IDHMut NHA cells after Ipa treatment. **p < 0.01.

Within adult rodent hippocampus (HPC), opioids suppress inhibitory parvalbumin-expressing interneurons (PV-INs), disinhibiting local microcircuits. However, it is unknown whether this disinhibitory motif is conserved across cortical regions, species, or development. We observed that PV-IN-mediated inhibition is robustly suppressed by opioids in HPC proper but not primary neocortex in mice and non-human primates, with spontaneous inhibitory tone in resected human tissue also following a consistent dichotomy. This hippocampal disinhibitory motif is established in early development when PV-INs and opioids regulate early population activity. Morphine pretreatment partially occludes this acute opioid-mediated suppression, with implications for the effects of opioids on hippocampal network activity important for learning and memory. Our findings demonstrate that PV-INs exhibit divergent opioid sensitivity across brain regions, which is remarkably conserved over evolution, and highlight the underappreciated role of opioids acting through immature PV-INs in shaping hippocampal development.

(A) Heatmap of gene expression profile for glioma specimen. The leading edge of the KEGG_MTOR_SIGNALING_PATHWAY geneset was shown. (B) Dose-response curve measured the cell viability in IDH1WT NHA cells in response to TMZ/Ipa combination treatment. (C) isobologram analysis showed the synergistic effect of TMZ and Ipa in IDH1WT NHA cells. (D) The combination index (C.I.) was calculated to show the additive effect of TMZ and Ipa.

The mammalian dentate gyrus contributes to mnemonic function by parsing similar events and places. The disparate activity patterns of mossy cells and granule cells are believed to enable this function yet the mechanisms that drive this circuit dynamic remain elusive. We identified a novel inhibitory interneuron subtype, characterized by VGluT3 expression, with overwhelming target selectivity for mossy cells while also revealing that CCK, PV, SST and VIP interneurons preferentially innervate granule cells. Leveraging pharmacology and novel enhancer viruses, we find that this target-specific inhibitory innervation pattern is evolutionarily conserved in non-human primates and humans. In addition, in vivo chemogenetic manipulation of VGluT3+ interneurons selectively alters the activity and functional properties of mossy cells. These findings establish that mossy cells and granule cells have unique, evolutionarily conserved inhibitory innervation patterns and suggest selective inhibitory circuits may be necessary to maintain DG circuit dynamics and enable pattern separation across species.

OBJECTIVE:Magnetoencephalography (MEG) is an important adjunctive method used to localize interictal epileptiform discharges (IEDs). The equivalent current dipole (ECD) method, the current gold standard modeling approach, identifies a single point source of activity. However, IEDs propagate widely and may be better represented by distributed source modeling approaches. Here, we investigate how areas of maximal IED-related activity estimated using dynamic statistical parametric mapping (dSPM) compare to dipoles and surgical resections. METHODS:We analyzed resting-state MEG recordings from 38 NIH patients. We localized areas of IED-related activity along the IED rising phase and peak using spatial clustering of the top 5% of dSPM activations, comparing localizations to ECD and surgical resection areas in seizure free patients. RESULTS:We identified dominant primary activation clusters in all patients and non-primary clusters in 24/38 patients. Dipoles localized closer to primary than non-primary clusters. In 12 post-operative seizure-free patients, the primary cluster center of mass was significantly closer to the resected area than dipoles and more stable over time. CONCLUSIONS:Distributed source modeling adds to our understanding of IED propagation patterns and may be useful in IED localization for epilepsy surgery planning. SIGNIFICANCE:dSPM enhances pre-surgical planning by refining IED localization, potentially improving surgical outcomes.

OBJECTIVE:Whereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of routine EEGs show interictal epileptiform discharges (IEDs) and overall misdiagnosis rates of epilepsy are 20% to 30%. We aim to demonstrate how network properties in EEG recordings can be used to improve the speed and accuracy differentiating epilepsy from mimics, such as functional seizures - even in the absence of IEDs. METHODS:In this multicenter study, we analyzed routine scalp EEGs from 218 patients with suspected epilepsy and normal initial EEGs. The patients' diagnoses were later confirmed based on an epilepsy monitoring unit (EMU) admission. About 46% ultimately being diagnosed with epilepsy and 54% with non-epileptic conditions. A logistic regression model was trained using spectral and network-derived EEG features to differentiate between epilepsy and non-epilepsy. Of the 218 patients, 90% were used for training and 10% were held out for testing. Within the training set, 10-fold cross validation was performed. The resulting tool was named "EpiScalp." RESULTS:EpiScalp achieved an area under the curve (AUC) of 0.940, an accuracy of 0.904, a sensitivity of 0.835, and a specificity of 0.963 in classifying patients as having epilepsy or not. INTERPRETATION:EpiScalp provides an accurate diagnostic aid from a single initial EEG recording, even in more challenging epilepsy cases with normal initial EEGs. This may represent a paradigm shift in epilepsy diagnosis by deriving an objective measure of epilepsy likelihood from previously uninformative EEGs. ANN NEUROL 2025;97:907-918.

Episodic memory depends upon activity distributed across the brain. However, the activity underlying memory has largely been examined within single tasks in isolation. Thus it is unclear to what extent prior findings reflect task-general rather than memory-specific cognitive processes. Here we address this question using data from 371 patients recorded intracranially who performed a free recall task with encoding and retrieval phases alongside an arithmetic distractor phase. We ask whether neural decoders fit to predict behavior from one phase transfer to the others. Encoding-retrieval transfer exceeds both arithmetic-encoding and arithmetic-retrieval transfer and therefore cannot be explained solely by processes supporting arithmetic. We further detect transfer between arithmetic and retrieval but not between arithmetic and encoding. The brain-behavioral relations observed in these tasks thus do not merely reflect a single task-general factor of activity. We propose cross-task decoding as a method for identifying the neural factor structure underlying distinct cognitive processes.

Classic models propose that forming lasting visual memories involves coordinated interactions between visually selective neocortical structures and the hippocampus during memory consolidation. However, the precise role of visually selective neocortical structures in memory consolidation remains elusive, given their potential contributions spanning from initial perceptual encoding to subsequent memory reactivation. We capitalized on a unique opportunity, involving direct recording from the posterior parahippocampus and its subsequent resection in a neurological patient, to investigate the impact of scene-selective neocortical lesions on visual memory consolidation. First, with intracranial EEG, we confirmed the functional relevance of the patient's resected tissues in representing a specific visual category, in this case, scene images. Subsequently, we identified disruption of memory for scenes relative to faces and objects during the participant's postoperative visit. This finding prompted a comprehensive analysis of visual memory across different visual categories in this participant, as well as an examination of similar functions in other neurological patients with intact parahippocampi and a cohort of online participants. Through these within- and between-participant comparisons, we identified greater time-dependent reduction in visual memory for scene images following the resection of the posterior parahippocampus. Importantly, these changes in memory retention could not be attributed to a general reduction in initial memory encoding following neocortical lesions. Our findings, therefore, suggest that reactivating scene-selective neocortical areas is essential for converting transient visual perceptual experiences into lasting long-term scene memories.

Memories shape our sense of self and enable adaptive behaviour based on prior experiences, yet the neural mechanisms underlying memory formation and retrieval are not fully understood. Building on work in animal models and the unique opportunities afforded by intracranial recordings, a growing number of studies have focused on the contributions of awake ripples (transient neural oscillations 20–100 ms long in the 80–150 Hz range) to human memory. Here, we review the body of evidence linking awake ripples to human memory and highlight relevant insights as well as unresolved discrepancies between studies. On the basis of previous evidence from work in animals that ripples may provide a biomarker for bursts of underlying population spiking activity, we suggest that examining the underlying spike content of ripples may help clarify their role in human memory and resolve these discrepancies. Recent support for this notion comes from human studies that, similarly to the prior animal work, relate patterns of neuronal spiking activity to ripples. Thus, our ability to understand the role of ripples in human memory may benefit from fully understanding these spiking events. In animal models, transient high-frequency oscillations in synchronized neural activity, known as ripples, have been linked to memory. Reithler et al. assess the current evidence for a contribution of ripples to human memory processes and suggest that examining the underlying spike content of ripples could enable researchers to decipher their functions.