BACKGROUND AND OBJECTIVES:Patients with epilepsy (PWE), especially temporal lobe epilepsy (TLE), experience impaired memory for personally experienced events. However, current assessments of episodic memory are limited in their ecological validity with a potential to miss detection of subtle cognitive decline. We conducted an exploratory study to determine whether a naturalistic film-viewing task with open-ended spoken recall could detect memory differences between TLE patients and healthy controls (HCs). METHODS:TLE patients (ages 18-60, fluent in English, not legally blind) were recruited from a Level 4 Epilepsy Center (2018-2024). TLE diagnosis was based on seizure semiology, MRI Brain, and EEG. TLE patients scored 22/30 on the Montreal Cognitive Assessment (MOCA); HCs scored 26/30. Subjects watched 6 short films and then freely recalled film details. Spoken responses were recorded, transcribed, segmented, and scored for film- and event-level recall. Recall order was assessed using the Damerau-Levenshtein distance. Semantic and causal centrality were quantified using sentence embeddings and rater-identified causal links, respectively. Beta regression with cluster-robust standard errors assessed group and centrality effects on recall probability. Beta regression evaluated the influence of age, MOCA, and testing platform on sequence recall error. RESULTS:We recruited 51 subjects (27 TLEs; 24 HCs, 70.1% F, mean 29.9 ±8.3 years). TLE patients and HCs showed similar recall of films (HC 89% ±11% vs TLE 88% ±18%, p = 0.54), coarse-grained events (HC 50% ±16% vs TLE 44% ±18%, p = 0.19) and fine-grained events (HC 25%±10% vs. TLE 22%±12%, p=0.17). Both groups recalled high causal centrality events better. However, TLE patients showed significantly greater fine-grained event sequence deviations at recall than HCs (HC 15% ±13% vs TLE 23% ±18%, p = 0.02, Hedges' g = 0.85, Cliff's δ = 0.51), with RTLE demonstrating more sequence deviations than HCs (15%±13 vs. 29%±21% p = 0.021) Age, education, MOCA, and performance on standard verbal and visual memory tasks were unrelated to film, event, and sequence recall performance. DISCUSSION:We demonstrate that a short film task with spontaneous spoken recall can identify group level differences in episodic memory. TLE patients demonstrate impaired sequence memory despite intact film- and event-level recall compared to healthy controls. Sequence memory may represent a subtle manifestation of memory impairment that is not detected by standard cognitive testing.

Neuronal electrical activity underlies human cognition, yet its direct, noninvasive measurement in the living human brain remains a fundamental challenge. Existing neuroimaging techniques, including EEG, MEG, and fMRI, are limited by trade-offs in sensitivity and spatial or temporal resolution. Here we propose quantum sensing MRI (qsMRI), a noninvasive approach that enables direct detection of neuronal firing-induced magnetic fields using a clinical MRI system. qsMRI exploits endogenous proton (1H) nuclear spins in water molecules as intrinsic quantum sensors and decodes time-resolved phase information from free induction decay (FID) signals to infer neuronal magnetic fields. We validate qsMRI through simulations, phantom experiments, and human studies at rest and during motor tasks, and provide open experimental procedures to facilitate independent rigorous validation. We further present a case study demonstrating potential applications to neurological disorders. qsMRI represents, to our knowledge, the first-in-human application of quantum sensing on a clinical MRI platform and may lay the foundation for a non-BOLD functional imaging modality capable of probing neuronal firing dynamics in both cortical and deep brain regions.

Alzheimer's disease is associated with neurotoxic amyloid-beta (Aβ) plaques. Studies in mice demonstrated that cerebrospinal fluid (CSF) clearance, if impaired, reduces Aβ clearance by 70% and that sleep enhances CSF clearance via expanding extracellular space by 60%. However, the impact of sleep on extracellular volume in human remains unclear due to lack of non-invasive technology. In this study, we use unique sodium ( 23 Na) magnetic resonance imaging (MRI) to measure extracellular volume fraction (ECVF) in healthy subjects while monitoring their sleep stage with MRI-compatible electroencephalography (EEG). With IRB approval, we studied 16 cognitively-healthy subjects (age 52.9 ± 17.6 years, ranging 27–77 years, 7 males and 9 females). The study lasted 1.5 hours, including four repeated 16-min sodium MRIs on a clinical scanner at 3T (Prisma, Siemens), and continuous EEG recording (Brain Vision, 32-channel, Garner, NC). The subjects were instructed to relax and fall asleep. Sleep was scored to five stages (wake, N1, N2, N3, and REM), based on American Academy of Sleep Medicine (AASM) Manual (v2.6, 2020). Sodium MRI was performed with a dual-tuned ( 1 H- 23 Na) birdcage coil (QED, Cleveland, OH) and a custom-developed pulse sequence, twisted projection imaging (TPI), with parameters: FOV=220mm, matrix size=64, 3D isotropic, TE/TR = 0.5/100ms, flip angle=90°, averages=6, and TA=16min. ECVF, a e , was calculated on the sodium images voxel-by-voxel, i.e., s = ΔV (a e C e + a i C i ) = ΔV (145 a e + 15 a i ), with a e + a i = 1. In panel P1 is the set-up of the study, including the MRI, EEG, sodium images, and hardware set-up. In P2 are typical EEG waveforms and spectra of sleep stages. In P3 is a representative of ECVF maps from a subject (43 years old, female). In P4 are outcomes of the study. Surprisingly, we observed a decrease in ECVF with sleep stage in the gray matter ( P =0.036) and in the white matter ( P =0.085, nearly significant). This study surprisingly observed a decrease in ECVF with sleep stage in the gray matter of healthy subjects, and in white matter as possible. This finding is contrary to the outcome from the animal studies and needs to confirm with more human subjects.

Humans primarily use vision to engage with and learn about the world. The hippocampus plays a crucial role in binding visual experiences of people, objects and contexts over time to create event memories. Thus, eye tracking could read out hippocampal dynamics in a precise and sensitive manner. Furthermore, eye tracking could potentially detect subjective memory decline reported by temporal lobe epilepsy patients that is missed by standardized cognitive testing. We asked whether eye movements could precisely and sensitively detect memory variability within trials and between subject cohorts. We predicted that (i) eye-tracking behaviour during visual retrieval could be validated against accuracy-based tests and that (ii) memory failures would be characterized by distinct spatiotemporal patterns of visual scanning. Fourteen healthy controls and 30 temporal lobe epilepsy patients participated in a visual object association task while eye movements and pupil size were recorded. We found a difference in accuracy during retrieval between healthy controls and temporal lobe epilepsy patients. Correct retrieval trials correlated with fewer saccades, early target preference, and a more organized search pattern. Eye-movement patterns could predict retrieval accuracy at the single trial level with outstanding performance, with percentage of gaze time on the target versus the lure as the most important features. Even during correct retrieval trials, temporal lobe epilepsy patients exhibited a more chaotic scanning pattern compared to healthy controls, suggesting a weaker memory trace. Healthy versus epilepsy diagnosis could be predicted with good performance, with trial entropy and pupillary changes as key predictive factors. Saccade patterns correlated with individual subjects' accuracy scores and performance on standardized cognitive tests but provided a greater range of performance. In summary, scanning behaviour provides a continuous measure of associative memory function that capture meaningful variability during trials, between trials, and between subjects. Thus, eye tracking could be a precise and sensitive method to detect subtle memory decline in temporal lobe epilepsy or other neuropsychiatric populations with memory impairment and may generate precise behavioural phenotyping in research settings.

Hippocampal sharp-wave ripples (SPW-Rs) are high-frequency oscillations critical for memory consolidation. Despite extensive characterization in rodents, their detection in humans is limited by coarse spatial sampling, interictal epileptiform discharges (IEDs), and a lack of consensus on human ripple localization and morphology. Here, we demonstrate that mouse and human hippocampal ripples share spatial, spectral and temporal features, which are clearly distinct from IEDs. In recordings from male APP/PS1 mice, SPW-Rs were distinguishable from IEDs by multiple criteria. Hippocampal ripples recorded during NREM sleep in female and male surgical epilepsy patients exhibited similar narrowband frequency peaks and multiple ripple cycles in the CA1 and subiculum regions. Conversely, IEDs showed a broad spatial extent and wide-band frequency power. We developed a semi-automated, ripple curation toolbox (ripmap) to separate event waveforms by low-dimensional embedding to reduce false-positive rate in selected ripple channels. Our approach improves ripple detection and provides a firm foundation for future human memory research.

Sleep was associated with an increased extracellular volume fraction (ECVF) in mice and enhanced cerebrospinal fluid (CSF) clearance of neurotoxic amyloid-beta (Aβ) proteins – a hallmark of Alzheimer's disease (AD). Such a beneficiary impact of sleep is however difficult to study on humans due to the lack of non-invasive imaging techniques. Last year (2024), we were, with unique sodium MRI and MRI-compatible EEG, able to study a cohort of healthy subjects and found a decrease, instead of increase, in ECVF during sleep. To confirm such an unexpected finding, here we report a study on a different cohort of healthy subjects using the same technologies as in the last-year study, i.e., simultaneous measurements of ECVF by sodium ( 23 Na) MRI and sleep by MRI-compatible EEG. This study (Figure 1) was performed on 30 cognitively normal human subjects (age 25–87 years, 22 females, 8 males), with approved IRB and signed consent. Each subject underwent a 90-min sodium MRI (Siemens Prisma, 3T) with a dual-tuned ( 1 H- 23 Na) birdcage head coil (QED, Cleveland, OH) and a continuous recording of MR-compatible EEG (Brain Vision, 32 channels). MRI scans consist three segments, each of 16min long with a 2-min gap in between for artifact-free EEG recording. Sleep was scored to five stages (wake, N1, N2, N3, and REM) according to AASM standards (V2.6, 2020). The pulse sequence was custom-developed twisted projection imaging (TPI). ECVF was quantified voxel-by-voxel using a two-compartment model of intra- and extra-cellular spaces. Figure 1 (P2) shows representatives of our EEG waveforms from our study subjects at different sleep stages. Figure 2 presents typic maps of ECVF from an individual subject, while Figure 3 summarizes ECVF from all of the subjects studied. Overall, ECVF changed during sleep in both white and gray matter regions of the brain; decreasing statistically significant in gray matter regions during N3 ( p  = 0.005) but not in N2 ( p  = 0.464) and not in white matter regions during N3 ( p  = 0.067). We found ECVF to decrease in slow wave sleep (N3), confirming our previous results in a different cohort of subjects but contradicting previous animal studies. This prompts further investigation of physiological importance of sleep.

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, but have not been directly compared3. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)4,5 and global neuronal workspace theory (GNWT)6-10 via a theory-neutral consortium11-13. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof12. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here14-17. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.

We introduce an intracranial EEG (iEEG) dataset collected as part of an adversarial collaboration between proponents of two theories of consciousness: Global Neuronal Workspace Theory and Integrated Information Theory. The data were recorded from 38 patients undergoing intracranial monitoring of epileptic seizures across three research centers using the same experimental protocol. Participants were presented with suprathreshold visual stimuli belonging to four different categories (faces, objects, letters, false fonts) in three orientations (front, left, right view), and for three durations (0.5, 1.0, 1.5 s). Participants engaged in a non-speeded Go/No-Go target detection task to identify infrequent targets with some stimuli becoming task-relevant and others task-irrelevant. Participants also engaged in a motor localizer task. The data were checked for its quality and converted to Brain Imaging Data Structure (BIDS). The de-identified dataset contains demographics, clinical information, electrode reconstruction, behavioral performance, and eye-tracking data. We also provide code to preprocess and analyze the data. This dataset holds promise for reuse in consciousness science and vision neuroscience to answer questions related to stimulus processing, target detection, and task-relevance, among many others.

Objective and Background:Epilepsy patients rank memory problems as their most significant cognitive comorbidity. Current clinical assessments are laborious to administer and score and may not always detect subtle memory decline. The Famous Faces Task (FF) has robustly demonstrated that left temporal lobe epilepsy (LTLE) patients remember fewer names and biographical details compared to right TLE (RTLE) patients and healthy controls (HCs). We adapted the FF task to capture subjects' entire spontaneous spoken recall, then scored responses using manual and natural language processing (NLP) methods. We expected to replicate previous group level differences using spontaneous speech and semi-automated analysis. Methods:Seventy-three (N=73) adults (28 LTLE, 18 RTLE, and 27 HCs) were included in a case-control prospective study design. Twenty FF in politics, sports, and entertainment (active 2008-2017) were shown to subjects, who were asked if they could recognize and spontaneously recall as much biographical detail as possible. We created human-generated and automatically-generated keyword dictionaries for each celebrity, based on a randomly selected training set of half of the HC transcripts. To control for speech output, we measured the speech duration, total word count and content word count for the FF task and a Cookie Theft Control Task (CTT), in which subjects were merely asked to describe a visual scene. Subjects' responses to FF and CTT tasks were recorded, transcribed, and analyzed in a blinded manner with a combination of manual and automated NLP approaches. Results:Famous face recognition accuracy was similar between groups. LTLE patients recalled fewer biographical details compared to HCs and RTLEs using both the gold-standard human-generated dictionary (24%±12% vs. 31%±12% and 30%±12%, p=0.007) and the automated dictionary (24%±12% vs. 31%±12% and 32%±13%, p=0.007). There were no group level differences in speech duration, total word count, or content word count for either the FF and CTT to explain difference in recall performance. There was a positive, statistically significant relationship between MOCA score and FF recall performance as scored by the human-generated (ρ= .327, p= .029) and automatically-generated dictionaries (ρ= .422, p= .004) for TLE subjects, but not HCs, an effect that was driven by LTLE subjects. Discussion:LTLE patients remember fewer details of famous people than HCs or RTLE patients, as discovered by NLP analysis of spontaneous recall. Decreased biographical memory was not due to decreased speech output and correlated with lower MOCA scores. NLP analysis of spontaneous recall can detect memory dysfunction in clinical populations in a semi-automated, objective, and sensitive manner.

Summary Different theories explain how subjective experience arises from brain activity 1,2 . These theories have independently accrued evidence, yet, confirmation bias and dependence on design choices hamper progress in the field 3 . Here, we present an open science adversarial collaboration which directly juxtaposes Integrated Information Theory (IIT) 4,5 and Global Neuronal Workspace Theory (GNWT) 6–10 , employing a theory-neutral consortium approach 11,12 . We investigate neural correlates of the content and duration of visual experience. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes, and their interpretation 12 . 256 human subjects viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography, and electrocorticography. We find information about conscious content in visual, ventro-temporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results confirm some predictions of IIT and GNWT, while substantially challenging both theories: for IIT, a lack of sustained synchronization within posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in prefrontal cortex. Beyond challenging the theories themselves, we present an alternative approach to advance cognitive neuroscience through a principled, theory-driven, collaborative effort. We highlight the challenges to change people’s mind 13 and the need for a quantitative framework integrating evidence for systematic theory testing and building.

In this chapter we will introduce the concept of neural frequency tagging (NFT), a versatile tool that can be used to explore how the brain processes, segments, and tracks specific cognitive processes via rhythmic neural responses. First, we explain how NFT can be used to investigate perceptual and cognitive processes. We explore critical experimental design considerations and how they can be exploited by NFT to answer specific questions in cognition using intracranial electroencephalography (iEEG). Next, we describe how NFT is calculated and, crucially, we explain how results can be interpreted in the context of human cognition and possible limitations of its use to explore certain brain processes. We end the chapter by addressing specific signal processing issues and potential pitfalls in its implementation and explore promising new avenues of cognitive research using NFT, such as development across the lifespan, and discuss possible future directions for which NFT would be an ideal tool for tackling difficult neuroscientific topics.

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery. While the contribution of sharp wave ripples in memory consolidation and decision-making is established in rodent models, our understanding of their role in human memory is incomplete. Here, the authors discuss common methodological challenges in detecting, analyzing, and reporting sharp wave ripples, then they suggest practical solutions to distinguish them from other high-frequency events

We describe the spatiotemporal course of cortical high-gamma activity, hippocampal ripple activity and interictal epileptiform discharges during an associative memory task in 15 epilepsy patients undergoing invasive EEG. Successful encoding trials manifested significantly greater high-gamma activity in hippocampus and frontal regions. Successful cued recall trials manifested sustained high-gamma activity in hippocampus compared to failed responses. Hippocampal ripple rates were greater during successful encoding and retrieval trials. Interictal epileptiform discharges during encoding were associated with 15% decreased odds of remembering in hippocampus (95% confidence interval 6-23%). Hippocampal interictal epileptiform discharges during retrieval predicted 25% decreased odds of remembering (15-33%). Odds of remembering were reduced by 25-52% if interictal epileptiform discharges occurred during the 500-2000 ms window of encoding or by 41% during retrieval. During encoding and retrieval, hippocampal interictal epileptiform discharges were followed by a transient decrease in ripple rate. We hypothesize that interictal epileptiform discharges impair associative memory in a regionally and temporally specific manner by decreasing physiological hippocampal ripples necessary for effective encoding and recall. Because dynamic memory impairment arises from pathological interictal epileptiform discharge events competing with physiological ripples, interictal epileptiform discharges represent a promising therapeutic target for memory remediation in patients with epilepsy.

Research has shown that sleep is beneficial for the long-term retention of memories. According to theories of memory consolidation, memories are gradually reorganized, becoming supported by widespread, distributed cortical networks, particularly during postencoding periods of sleep. However, the effects of sleep on the organization of memories in the hippocampus itself remains less clear. In a 3-d study, participants encoded separate lists of word–image pairs differing in their opportunity for sleep-dependent consolidation. Pairs were initially studied either before or after an overnight sleep period, and were then restudied in a functional magnetic resonance imaging (fMRI) scan session. We used multivariate pattern similarity analyses to examine fine-grained effects of consolidation on memory representations in the hippocampus. We provide evidence for a dissociation along the long axis of the hippocampus that emerges with consolidation, such that representational patterns for object–word memories initially formed prior to sleep become differentiated in anterior hippocampus and more similar, or overlapping, in posterior hippocampus. Differentiation in anterior hippocampal representations correlated with subsequent behavioral performance. Furthermore, representational overlap in posterior hippocampus correlated with the duration of intervening slow wave sleep. Together, these results demonstrate that sleep-dependent consolidation promotes the reorganization of memory traces along the long axis of the hippocampus.

Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain's ability to discover regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words), while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.

Memory consolidation is hypothesized to involve the distribution and restructuring of memory representations across hippocampal and cortical regions. Theories suggest that, through extended hippocampal-cortical interactions, cortical ensembles come to represent more integrated, or overlapping, memory traces that prioritize commonalities across related memories. Sleep processes, particularly fast sleep spindles, are thought to support consolidation, but evidence for this relationship has been mostly limited to memory retention benefits. Whether fast spindles provide a mechanism for neural changes hypothesized to support consolidation, including the strengthening of hippocampal-cortical networks and integration across memory representations, remains unclear, as does the specificity of regions involved. Using functional connectivity analyses of human fMRI data (both sexes), we show that fast spindle density during overnight sleep is related to enhanced hippocampal-cortical functional connectivity the next day, when restudying information learned before sleep. Spindle density modulated connectivity in distinct hippocampal-cortical networks depending on the category of the consolidated stimuli. Specifically, spindle density correlated with functional connectivity between anterior hippocampus and ventromedial prefrontal cortex (vmPFC) for object-word pairs, and posterior hippocampus and posteromedial cortex for scene-word pairs. Using multivariate pattern analyses, we also show that fast spindle density during postlearning sleep is associated with greater pattern similarity, or representational overlap, across individual object-word memories in vmPFC the next day. Further, the relationship between fast spindle density and representational overlap in vmPFC was mediated by the degree of anterior hippocampal-vmPFC functional connectivity. Together, these results suggest that fast spindles support the network distribution of memory traces, potentially restructuring memory representations in vmPFC.SIGNIFICANCE STATEMENT How new experiences are transformed into long-term memories remains a fundamental question for neuroscience research. Theories suggest that memories are stabilized as they are reorganized in the brain, a process thought to be supported by sleep oscillations, particularly sleep spindles. Although sleep spindles have been associated with benefits in memory retention, it is not well understood how spindles modify neural memory traces. This study found that spindles during overnight sleep correlate with changes in neural memory traces, including enhanced functional connectivity in distinct hippocampal-cortical networks and increased pattern similarity among memories in the cortex. The results provide critical evidence that spindles during overnight sleep may act as a physiological mechanism for the restructuring of neural memory traces.

Slow oscillations and spindle activity during non-rapid eye movement sleep have been implicated in memory consolidation. Closed-loop acoustic stimulation has previously been shown to enhance slow oscillations and spindle activity during sleep and improve verbal associative memory. We assessed the effect of closed-loop acoustic stimulation during a daytime nap on a virtual reality spatial navigation task in 12 healthy human subjects in a randomized within-subject crossover design. We show robust enhancement of slow oscillation and spindle activity during sleep. However, no effects on behavioral performance were observed when comparing real versus sham stimulation. To explore whether memory enhancement effects were task specific and dependent on nocturnal sleep, in a second experiment with 19 healthy subjects, we aimed to replicate a previous study that used closed-loop acoustic stimulation to enhance memory for word pairs. The methods used were as close as possible to those used in the original study, except that we used a double-blind protocol, in which both subject and experimenter were unaware of the test condition. Again, we successfully enhanced slow oscillation and spindle power, but again did not strengthen associative memory performance with stimulation. We conclude that enhancement of sleep oscillations may be insufficient to enhance memory performance in spatial navigation or verbal association tasks, and provide possible explanations for lack of behavioral replication.

ABSTRACT Although sensory input arrives continuously, we experience the world in discrete units, consisting of words, objects, scenes and events, which in turn form the basis of language, thought and memory. How does sensory input get parsed into meaningful units? A process known as statistical learning (SL) may underlie this ability. SL is ubiquitous, for example allowing babies to discover word boundaries in continuous speech by tracking transitional probabilities between syllables. Here we examine which cortical circuits extract such regularities, how these regularities are represented, and how this learning compares across sensory modalities. We exposed subjects to auditory and visual sequences containing temporal regularities while collecting direct, intracranial recordings (23 patients, 3689 electrodes). We used neural frequency tagging to first map the cortical circuits for SL and then representational similarity analysis to determine which aspect(s) of the regularities are learned. SL manifested into two distinct ways across electrodes, differing in terms of both anatomical location and hierarchical organization: one cluster of electrodes located in earlier processing stages (e.g., superior temporal gyrus, STG) represented both the constituent elements (e.g., syllables) and learned higher-order units (e.g., words); the other cluster was localized to later processing stages (e.g., inferior frontal gyrus, IFG) and represented only the higher-order units. Within these regions, SL shaped the similarity of neural representations at multiple levels, with a division of labor between earlier vs. later brain areas in terms of encoding of simple generic aspects of the sequences (i.e., transitional probability) vs. complex and specific information (i.e., ordinal position, identity). The anatomical and representational segregation of these circuits was observed for SL in both the auditory and visual modality, yet the anatomical areas (with the exception of IFG and anterior temporal pole), showed specificity to modality. These findings indicate the existence of multiple computational systems for sequence processing supporting learning across hierarchically segregated cortical circuits.