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Monday mornings at 9:30 am Amsterdam time,
we discuss exciting new science from last week with colleagues,
students and everyone interested in the brain.
Previous events are available OnDemand on YouTube:  https://www.youtube.com/@CNSeminars

Hippocampal mismatch signals are based on episodic memories and not schematic knowledge Significance Our brains use memories of the past to make sense of the present and predict the future. These memories might be of specific events or more general knowledge about the world. The hippocampus is widely implicated in signaling mismatches with memory-based predictions, but whether it uses specific episodic memories or generalized knowledge remains unclear. We show that the hippocampus selectively signals mismatches with episodic memories, while other brain networks respond to unexpected situations more broadly, regardless of memory type. These findings clarify the hippocampus’ role as a comparator, showing it is specialized for evaluating reality against episodic memories, and offer insight into how the brain uses past experiences to interpret the present and anticipate the future, shaping learning and memory. Abstract Prediction errors drive learning by signaling mismatches between expectations and reality, but the neural systems supporting these computations remain debated. The hippocampus is implicated in mismatch detection, yet it is not known whether it signals mismatches with episodic memories or generalized knowledge. Across three functional Magnetic Resonance Imaging (fMRI) experiments, we show that the hippocampus selectively responds to mismatches with episodic memories of specific events. In contrast, schematic mismatches engage Semantic Control and Multiple Demand Networks, as well as subcortical regions linked to prediction error signaling. Episodic mismatches also recruit the Default Mode Network. These findings challenge accounts that propose the hippocampus is a domain-general mismatch detector. Instead, the findings support a more specialized role for the hippocampus in learning that is underpinned by its well-established importance in processing episodic memories. Paper link: https://www.pnas.org/doi/10.1073/pnas.2503535122

Structured Abstract INTRODUCTION Synaptic vesicle (SV) exocytosis is triggered by an action potential and leads to neurotransmitter release. As such, SV exocytosis is fundamental to neuronal communication. However, the structural and biophysical mechanisms underlying SV exocytosis remain incompletely understood. In particular, questions remain regarding the dynamic interactions between the SV membrane, the presynaptic membrane, and the protein complexes involved. This gap has fueled a long-standing debate over the existence of transient “kiss-and-run” fusion versus irreversible “full-collapse” fusion in central synapses. RATIONALE Resolving this debate requires techniques that can achieve nanometer spatial resolution and millisecond temporal resolution. To address this need, we developed a time-resolved, cellular cryo–electron tomography (cryo-ET) method to image intact synapses in cultured rat hippocampal neurons. This approach integrated optogenetic stimulation for synaptic activation and plunge-freezing at millisecond intervals. It also provided high three-dimensional spatial resolution, enabling accurate vesicle size measurements and detailed structural analysis of vesicle–plasma membrane interactions. Using this technique, we acquired more than 1000 tomograms of entire excitatory synapses, frozen at various time points from 0 to 300 ms post–action potential. This large dataset facilitated rigorous statistical analysis of vesicle states and subtomogram averaging to visualize fusion-pore structures. RESULTS Near the presynaptic active zones, we observed two distinct SV populations with diameters centered at ~29 and ~41 nm. These SVs were mainly categorized into seven distinct structural states: tethered (large and small), semifused (large and small), pore-opened (large and small), and Ω-shaped (small). Notably, the population of small SVs decreased markedly after neural network inactivity with 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) and was completely absent when vesicle exocytosis was blocked by tetanus toxin. Time-resolved cryo-ET revealed a sequence of transitions among these vesicle states. In the resting condition, large SVs were primarily tethered to the plasma membrane (docking). Within 4 ms post–action potential, docked SVs transitioned to large semifused SVs (priming) that transiently “kiss” the plasma membrane. These primed SVs then formed pore-opened SVs with a ~4-nm lipidic fusion pore flanked by putative soluble NSF attachment protein receptor (SNARE) complexes. These fused SVs rapidly shrank to small pore-opened SVs with approximately half the surface area of the original large SVs. Most shrunken SVs subsequently closed their fusion pores and converted into small semifused SVs. By 70 ms, small semifused SVs detached from the presynaptic membrane (“run-away”), whereas the remaining shrunken, pore-opened SVs fully collapsed into the plasma membrane. After 100 ms, the run-away SVs began to migrate to the periphery of the SV cluster, and the resulting expanded presynaptic membrane began to be retrieved through ultrafast endocytosis. CONCLUSION Our study identifies a kiss-shrink-run pathway as the dominant biophysical mechanism for SV exocytosis and rapid recycling in hippocampal synapses. This kiss-shrink-run mechanism reconciles the kiss-and-run and full-collapse models of neurotransmission and provides a unified explanation for the high efficiency and fidelity of synaptic transmission. Our integrative methodology also establishes a general framework for probing membrane dynamics and molecular interactions in situ with high spatiotemporal precision. Paper link: https://www.science.org/doi/10.1126/science.ads7954

Paper link: https://link.springer.com/article/10.1007/s00429-025-03025-0 As part of the Special Collection in Brain Structure and Function: https://link.springer.com/collections/jfccijhacc The structural brain connectome spans multiple length scales with varying complexity, from major commissural, association, and projection pathways down to much smaller pathways within brain regions. Diffusion MRI is often acquired for whole-brain coverage with limited spatial resolution for tractography purposes. How does this ’one size fits all’ approach impact tractography and diffusion quantification, and do we need to care? Here, we discuss the concept of image resolution and define the structural image resolution limit, representing the resolution threshold at which a given dMRI setup can reliably resolve anatomical structures.

Paper link: https://www.pnas.org/doi/10.1073/pnas.2426815122 Abstract A central question in the study of language change is whether or not such change is generational. If a language changes over time generation-by-generation, the process looks as follows: New generations of speakers introduce innovations, while older speakers conserve their usage patterns, and the language changes as new generations replace older ones. At the opposite extreme, language change could be a zeitgeist phenomenon, in which changes are universally adopted by speakers simultaneously, regardless of age or generational cohort. This paper asks this question in the context of word meaning change. We analyze meaning change in over 100 words across more than 7.9 million U.S. congressional speeches, to observe whether, when a word sense rises or falls in prominence, adult speakers from different generations uniformly adopt it, or those from older generations conserve their prior usage. Using language model-based word sense induction methods, we identify different senses of each word, and then model the prevalence of each of these word senses as a function of time and speaker age. We find that most words show a small but statistically significant effect of speaker age; across almost 140 y of Congress, older speakers typically take longer than younger speakers to follow changes in word usage, but nevertheless do so within a few years. Our findings indicate that despite minor age-based differences, word meaning change among mature speakers is likely not a generational process, but rather a zeitgeist process, in which older adult speakers can readily adopt new word usage patterns.

Paper link: https://pmc.ncbi.nlm.nih.gov/articles/PMC12345208/ Abstract Human evolution involved major anatomical transformations, including a rapid increase in brain volume over the last 2 million years. Examination of fossil records provides insight into these physical changes but offers limited information on the evolution of brain function and cognition. A complementary approach integrates genome dating from the Human Genome Dating Project with genome-wide association studies to trace the emergence of genetic variants linked to human traits over 5 million years. We find that genetic variants underlying cortical morphology (~300,000 years, P = 4 × 10−28), fluid intelligence (~500,000 years, P = 1.4 × 10−4), and psychiatric disorders (~475,000 years, P = 5.9 × 10−33) emerged relatively recently in hominin evolution. Among psychiatric phenotypes, variants associated with depression (~24,000 years, P = 1.6 × 10−4) and alcoholism-related traits (~40,000 years, P = 5.2 × 10−12) are the youngest. Genes with recent evolutionary modifications are involved in intelligence (P = 1.7 × 10−6) and cortical area (P = 3.5 × 10−4) and exhibit elevated expression in language-related areas (P = 7.1 × 10−4), a hallmark of human cognition. Our findings suggest that recently evolved genetic variants shaped the human brain, cognition, and psychiatric traits. Useful links: https://www.gtexportal.org/home/ https://neurosynth.org/genes/ genome dating

Abstract Task-based functional magnetic resonance imaging (tb-fMRI) has advanced our understanding of brain-behavior relationships. Standard tb-fMRI analyses suffer from limited reliability and low effect sizes, and machine learning (ML) approaches often require thousands of subjects, restricting their ability to inform how brain function may arise from and contribute to individual differences. Using data from 9,024 early adolescents, we derived a classifier (‘neural signature’) distinguishing between high and low working memory loads in an emotional n-back fMRI task, which captures individual differences in the separability of activation to the two task conditions. Signature predictions were more reliable and had stronger associations with task performance, cognition, and psychopathology than standard estimates of regional brain activation. Further, the signature was more sensitive to psychopathology associations and required a smaller training sample (N=320) than standard ML approaches. Neural signatures hold tremendous promise for enhancing the informativeness of tb-fMRI individual differences research and revitalizing its use.

Classical models of memory in psychology and neuroscience rely on similarity-based retrieval of stored patterns, where similarity is a function of retrieval cues and the stored patterns. Although parsimonious, these models do not allow distinct representations for storage and retrieval, despite their distinct computational demands. Key-value memory systems, in contrast, distinguish representations used for storage (values) and those used for retrieval (keys). This allows key-value memory systems to optimize simultaneously for fidelity in storage and discriminability in retrieval. We review the computational foundations of key-value memory, its role in modern machine-learning systems, related ideas from psychology and neuroscience, applications to a number of empirical puzzles, and possible biological implementations. Paper link: https://doi.org/10.1016/j.neuron.2025.02.029

Paper link: https://doi.org/10.1371/journal.pbio.3002906 Short-range human cortico-cortical white matter fibers have thinner axons and are less myelinated compared to long-range fibers despite a similar g-ratio Abstract The size and complexity of the human brain require optimally sized and myelinated fibers. White matter fibers facilitate fast communication between distant areas, but also connect adjacent cortical regions via short association fibers. The difference in length and packing density of long and short association fibers pose different requirements on their optimal size and degree of myelination. The fundamental questions of (i) how thick the short association fibers are and (ii) how strongly they are myelinated as compared to long fibers, however, remain unanswered. We present a comprehensive two-dimensional transmission electron microscopic analysis of ~400,000 fibers of human white matter regions with long (corpus callosum) and short fibers (superficial white matter). Using a deep learning approach, we demonstrate a substantially higher fiber diameter and higher myelination thickness (both approximately 25% higher) in corpus callosum than in superficial white matter. Surprisingly, we do not find a difference in the ratio between axon diameter and myelin thickness (g-ratio), which is close to the theoretically optimal value of ~0.6 in both areas (0.54). This work reveals a fundamental principle of brain organization that provides a key foundation for understanding the human brain. Discussion papers we referred to: Diameter, Length, Speed, and Conduction Delay of Callosal Axons in Macaque Monkeys and Humans: Comparing Data from Histology and Magnetic Resonance Imaging Diffusion Tractography https://www.jneurosci.org/content/33/36/14501 Fiber composition of the human corpus callosum https://www.scopus.com/pages/publications/0027097055?inward

Paper link: https://www.nature.com/articles/s41593-025-02037-7 Abstract The adult brain’s capacity for cortical reorganization remains debated. Using longitudinal neuroimaging in three adults, followed before and up to 5 years after arm amputation, we compared cortical activity elicited by movement of the hand (before amputation) versus phantom hand (after amputation) and lips (before and after amputation). We observed stable cortical representations of both hand and lips in primary sensorimotor regions. By directly quantifying activity changes across amputation, we demonstrate that amputation does not trigger large-scale cortical reorganization. Presented by Lilit Dulyan, Radboud University

How are memories formed and stored in the brain? Uytiepo et al. used three-dimensional electron microscopy coupled with chemogenetic tagging and behavioral analysis to determine the structural changes supporting the acquisition of long-term fear memories, focusing on the Schaffer collateral synapses. Long-term memory acquisition was found to be associated with a selective increase in multisynaptic boutons without the involvement of simultaneous activation of synaptically connected neurons. These results challenge the general applicability of the Hebbian model and expand our understanding of the mechanisms responsible for memory formation. —Mattia Maroso Paper link: https://www.science.org/doi/10.1126/science.ado8316

Recent generations of language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established math and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from contamination and does not provide insights into the reasoning traces. In this work, we systematically investigate these gaps with the help of controllable puzzle environments that allow precise manipulation of complexity while maintaining consistent logical structures. This setup enables the analysis of not only final answers but also the internal reasoning traces, offering insights into how LRMs think. Through extensive experiments, we show that LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counterintuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having remaining token budget. By comparing LRMs with their standard LLM counterparts under same inference compute, we identify three performance regimes: (1) low-complexity tasks where standard models outperform LRMs, (2) medium-complexity tasks where LRMs demonstrates advantage, and (3) high-complexity tasks where both models face complete collapse. We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across scales. We also investigate the reasoning traces in more depth, studying the patterns of explored solutions and analyzing the models' computational behavior, shedding light on their strengths, limitations, and raising questions about their reasoning capabilities. Paper link: https://arxiv.org/abs/2506.06941

The arcuate fascicle (AF) is the main fibre tract in the brain for human language. It connects frontal and temporal language areas in the superior and middle temporal gyrus (MTG). The AF’s connection to the MTG was considered unique to humans and has influenced theories of the evolution of language. Here, using high-resolution diffusion MRI of post-mortem brains, we demonstrate that both wild and captive chimpanzees have a direct AF connection into the MTG, albeit weaker than in humans. This finding challenges the notion of a strictly human-specific AF morphology and suggests that language-related neural specialisation in humans likely evolved through gradual evolutionary strengthening of a pre-existing connection, rather than arising de novo. It is likely that this neural architecture supporting complex communication was already present in the last common ancestor of hominins and chimpanzees 7 million years ago, enabling the evolution of language processes in the human lineage. Paper link: https://www.nature.com/articles/s41467-025-59254-8#Sec27

Abstract Currently, we understand much about how children’s brains attend to and learn from information presented while they are alone, viewing a screen – but less about how interpersonal social influences are substantiated in the brain. Here, we consider research that examines how social behaviors affect not one, but both partners in a dyad. We review studies that measured interpersonal neural entrainment during early social interaction, considering two ways of measuring entrainment: concurrent entrainment (e.g., ‘when A is high, B is high’ – also known as synchrony) and sequential entrainment (‘changes in A forward-predict changes in B’). We discuss possible causes of interpersonal neural entrainment, and consider whether it is merely an epiphenomenon, or whether it plays an independent, mechanistic role in early attention and learning. Paper link: DOI: 10.1016/j.tics.2020.01.006

Synthetic Diffusion Tensor Imaging Maps Generated by 2D and 3D Probabilistic Diffusion Models: Evaluation and Applications Diffusion tensor imaging (DTI) is a key neuroimaging modality for assessing brain tissue microstructure, yet high-quality acquisitions are costly, time-intensive, and prone to artifacts. To address data scarcity and privacy concerns – and to augment the available data for training deep learning methods – synthetic DTI generation has gained interest. Specifically, denoising diffusion probabilistic models (DDPMs) have emerged as a promising approach due to their superior fidelity, diversity, controllability, and stability compared to generative adversarial networks (GANs) and variational autoencoders (VAEs). In this work, we evaluate the quality, fidelity and added value for downstream applications of synthetic DTI mean diffusivity (MD) maps generated by 2D slice-wise and 3D volume-wise DDPMs. We evaluate their computational efficiency and utility for data augmentation in two downstream tasks: sex classification and dementia classification using 2D and 3D convolutional neural networks (CNNs). Our findings show that 3D synthesis outperforms 2D slice-wise generation in downstream tasks. We present a benchmark analysis of synthetic diffusion-weighted imaging approaches, highlighting key trade-offs in image quality, diversity, efficiency, and downstream performance. Paper link: https://www.biorxiv.org/content/10.1101/2025.02.21.639511v1

Auditory and speech signals are undisputedly processed in both left and right hemispheres, but this bilateral allocation is likely unequal. The Asymmetric Sampling in Time (AST) hypothesis proposed a division of labor that has its neuroanatomical basis in the distribution of neuronal ensembles with differing temporal integration constants: left auditory areas house a larger proportion of ensembles with shorter temporal integration windows (tens of milliseconds), suited to process rapidly changing signals; right auditory areas host a larger proportion with longer time constants (∼150–300 ms), ideal for slowly changing signals. Here we evaluate the large body of findings that clarifies this relationship between auditory temporal structure and functional lateralization. In this reappraisal, we unpack whether this relationship is influenced by stimulus type (speech/nonspeech), stimulus temporal extent (long/short), task engagement (high/low), or (imaging) modality (hemodynamic/electrophysiology/behavior). We find that the right hemisphere displays a clear preference for slowly changing signals whereas the left-hemispheric preference for rapidly changing signals is highly dependent on the experimental design. We consider neuroanatomical properties potentially linked to functional lateralization, contextualize the results in an evolutionary perspective, and highlight future directions. Paper link: https://doi.org/10.1016/j.neubiorev.2025.106082

Electrophysiological signatures underlying variability in human memory consolidation We experience countless pieces of new information each day, but remembering them later depends on firmly instilling memory storage in the brain. Numerous studies have implicated non-rapid eye movement (NREM) sleep in consolidating memories via interactions between hippocampus and cortex. However, the temporal dynamics of this hippocampal-cortical communication and the concomitant neural oscillations during memory reactivations remains unclear. To address this issue, the present study used the procedure of targeted memory reactivation (TMR) following learning of object-location associations to selectively reactivate memories during human NREM sleep. Cortical pattern reactivation and hippocampal-cortical coupling were measured with intracranial EEG recordings in patients with epilepsy. We found that TMR produced variable amounts of memory enhancement across a set of object-location associations. Successful TMR increased hippocampal ripples and cortical spindles, apparent during two discrete sweeps of reactivation. The first reactivation sweep was accompanied by increased hippocampal-cortical communication and hippocampal ripple events coupled to local cortical activity (cortical ripples and high-frequency broadband activity). In contrast, hippocampal-cortical coupling decreased during the second sweep, while increased cortical spindle activity indicated continued cortical processing to achieve long-term storage. Taken together, our findings show how dynamic patterns of item-level reactivation and hippocampal-cortical communication support memory enhancement during NREM sleep. Paper link: https://www.nature.com/articles/s41467-025-57766-x#Abs1 Presenter: Dr Marcela Ovando-Tellez

At the heart of medicine lies physician–patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust1,2. Artificial intelligence (AI) systems capable of diagnostic dialogue could increase accessibility and quality of care. However, approximating clinicians’ expertise is an outstanding challenge. Here we introduce AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based AI system optimized for diagnostic dialogue. AMIE uses a self-play-based3 simulated environment with automated feedback for scaling learning across disease conditions, specialties and contexts. We designed a framework for evaluating clinically meaningful axes of performance, including history-taking, diagnostic accuracy, management, communication skills and empathy. We compared AMIE’s performance to that of primary care physicians in a randomized, double-blind crossover study of text-based consultations with validated patient-actors similar to objective structured clinical examination4,5. The study included 159 case scenarios from providers in Canada, the United Kingdom and India, 20 primary care physicians compared to AMIE, and evaluations by specialist physicians and patient-actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 30 out of 32 axes according to the specialist physicians and 25 out of 26 axes according to the patient-actors. Our research has several limitations and should be interpreted with caution. Clinicians used synchronous text chat, which permits large-scale LLM–patient interactions, but this is unfamiliar in clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI. Paper link: https://www.nature.com/articles/s41586-025-08866-7

Promoting Open Discussions of Scientific Failure within the Annual Society for Neuroscience Conference The annual Society for Neuroscience (SfN) meeting is a bonanza of scientific achievement: famous keynote speakers, beautiful scientific results, and award ceremonies. This focus is exciting and invigorating but glosses over the many failures, mistakes, and rejections that typically lead to scientific success. Our goal has been to create a space within the annual SfN meeting for open conversation about scientific failure and, by doing so, increase transparency, resilience, and mental well-being within our community. In this article, we share the materials that we have used at SfN during the past 4 years (2021–2024) to promote discussions of scientific failure, including formal storytelling, individual and interactive games, and confessionals. For each activity, we provide the rationale and practical guidance regarding logistics and usage. We hope this will aid scientists interested in adapting the activities for their own communities or local events. We end with a call for scientific institutions to commit to providing space for open discussions of failure within their educational programs and conferences. Paper link: https://www.eneuro.org/content/12/3/ENEURO.0524-24.2024

Paper link: https://direct.mit.edu/nol/article/5/3/676/114495/The-Contributions-of-the-Cerebellar-Peduncles-and Abstract Fluent speech production is a complex task that spans multiple processes, from conceptual framing and lexical access, through phonological encoding, to articulatory control. For the most part, imaging studies portraying the neural correlates of speech fluency tend to examine clinical populations sustaining speech impairments and focus on either lexical access or articulatory control, but not both. Here, we evaluated the contribution of the cerebellar peduncles to speech fluency by measuring the different components of the process in a sample of 45 neurotypical adults. Participants underwent an unstructured interview to assess their natural speaking rate and articulation rate, and completed timed semantic and phonemic fluency tasks to assess their verbal fluency. Diffusion magnetic resonance imaging with probabilistic tractography was used to segment the bilateral cerebellar peduncles (CPs) and frontal aslant tract (FAT), previously associated with speech production in clinical populations. Our results demonstrate distinct patterns of white matter associations with different fluency components. Specifically, verbal fluency is associated with the right superior CP, whereas speaking rate is associated with the right middle CP and bilateral FAT. No association is found with articulation rate in these pathways, in contrast to previous findings in persons who stutter. Our findings support the contribution of the cerebellum to aspects of speech production that go beyond articulatory control, such as lexical access, pragmatic or syntactic generation. Further, we demonstrate that distinct cerebellar pathways dissociate different components of speech fluency in neurotypical speakers. cerebellum, DTI, probabilistic tractography, speaking rate, speech production, white matter

Cell-based therapies hold great promise for brain repair after stroke. While accumulating evidence confirms the preclinical and clinical benefits of cell therapies, the underlying mechanisms by which they promote brain repair remain unclear. Here, we briefly review endogenous mechanisms of brain repair after ischaemic stroke and then focus on how different stem and progenitor cell sources can promote brain repair. Specifically, we examine how transplanted cell grafts contribute to improved functional recovery either through direct cell replacement or by stimulating endogenous repair pathways. Additionally, we discuss recently implemented preclinical refinement methods, such as preconditioning, microcarriers, genetic safety switches and universal (immune evasive) cell transplants, as well as the therapeutic potential of these pharmacologic and genetic manipulations to further enhance the efficacy and safety of cell therapies. By gaining a deeper understanding of post-ischaemic repair mechanisms, prospective clinical trials may be further refined to advance post-stroke cell therapy to the clinic. paper link: https://academic.oup.com/brain/article/147/10/3286/7698450

Paper link: https://pmc.ncbi.nlm.nih.gov/articles/PMC10570710 Neural evidence for procedural automatization during cognitive development: Intraparietal response to changes in very-small addition problem-size increases with age Cognitive development is often thought to depend on qualitative changes in problem-solving strategies, with early developing algorithmic procedures (e.g., counting when adding numbers) considered being replaced by retrieval of associations (e.g., between operands and answers of addition problems) in adults. However, algorithmic procedures might also become automatized with practice. In a large cross-sectional fMRI study from age 8 to adulthood (n = 128), we evaluate this hypothesis by measuring neural changes associated with age-related reductions in a behavioral hallmark of mental addition, the problem-size effect (an increase in solving time as problem sum increases). We found that age-related decreases in problem-size effect were paralleled by age-related increases of activity in a region of the intraparietal sulcus that already supported the problem-size effect in 8- to 9-year-olds, at an age the effect is at least partly due to explicit counting. This developmental effect, which was also observed in the basal ganglia and prefrontal cortex, was restricted to problems with operands ≤ 4. These findings are consistent with a model positing that very-small arithmetic problems–and not larger problems–might rely on an automatization of counting procedures rather than a shift towards retrieval, and suggest a neural automatization of procedural knowledge during cognitive development. Keywords: Development, Arithmetic, Procedure, Problem-size effect, FMRI

Adaptive deep brain stimulation (aDBS) is an emerging advancement in DBS technology; however, local field potential (LFP) signal rate detection sufficient for aDBS algorithms and the methods to set-up aDBS have yet to be defined. Here we summarize sensing data and aDBS programming steps associated with the ongoing Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) pivotal trial (NCT04547712). Sixty-eight patients were enrolled with either subthalamic nucleus or globus pallidus internus DBS leads connected to a Medtronic PerceptTM PC neurostimulator. During the enrollment and screening procedures, a LFP (8–30 Hz, ≥1.2 µVp) control signal was identified by clinicians in 84.8% of patients on medication (65% bilateral signal), and in 92% of patients off medication (78% bilateral signal). The ADAPT-PD trial sensing data indicate a high LFP signal presence in both on and off medication states of these patients, with bilateral signal in the majority, regardless of PD phenotype. Paper link: https://www.nature.com/articles/s41531-024-00772-5