Brain (Germany)

The increasing size of electrophysiological datasets has heightened the need for quality metrics that automatically reject neurons whose activity was recorded with low sensitivity or specificity. One key approach estimates artifactual contamination by assuming that each neuron has a refractory period (RP), a brief time interval following each action potential when further activity cannot occur. However, existing methods cannot be applied without prior knowledge of the neurons’ RP durations, limiting their usefulness in datasets that include neurons from brain regions or species in which RP durations have not been systematically characterized. Here, we find that neurons in some brain regions (thalamus) and species (macaque) have shorter RP durations than commonly assumed, and we introduce a new metric, the Sliding Refractory Period metric, which is robust to variation in a neuron’s RP duration without tuning. We validate the method using simulations, demonstrating that it improves acceptance of uncontaminated spike trains with short or long RP durations while still rejecting contaminated ones. Moreover, by incorporating Poisson statistics into the calculation, the method also improves on prior work by allowing the user to approximately control the false acceptance rate. Our new metric improves quantification of contamination in electrophysiological recordings and enables application of a single tuning-free quality metric to data recorded from diverse brain regions and species.

The brain is thought to optimise behaviour by generating predictions based on learned statistical regularities. Predictive processing seemingly explains expectation suppression (ES), the attenuation of neural activity in response to expected stimuli. However, the mechanisms behind ES are unclear, with conflicting evidence for alternative models. Sharpening models propose that expectations suppress neurons away from the expected stimulus, increasing the signal-to-noise ratio and boosting decoding for expected stimuli. In contrast, dampening models posit that expectations suppress neurons that are tuned to the expected stimuli, reducing overall response magnitude and decoding accuracy. The opposing process theory (OPT) suggests that both processes occur at different time points, namely that initial sharpening is followed by later dampening of the neural representations of the expected stimulus. Here we test this theory and shed light on the dynamics of expectation effects, both at single-trial level and over time. Thirty-one participants completed a statistical learning task in which a 'leading' image from one category predicted a 'trailing' image from a different category. Multivariate EEG analyses decoded stimulus information related to the trailing category. Within-trial, expectation increased decoding accuracy at early latencies and decreased it at later latencies, in line with OPT. However, across trials, stimulus expectation decreased decoding accuracy in initial trials and increased it in later trials. We theorise that these dissociable dynamics of expectation effects within and across trials support hierarchical learning mechanisms. While within-trial results support the OPT, across-trial results suggest that sharpening and dampening effects emerge at distinct stages of associative learning.

Abstract Because many neurobehavioural functions rely on distributed brain networks, anatomically diverse brain lesions can cause the same neurobehavioural deficit. Lesion network mapping (LNM) builds on this principle to better understand the functional anatomy of the brain, by mapping focal lesions onto a normative functional connectome. Yet, recent work raised concerns about anatomical specificity of LNM, showing that commonly used LNM procedures converge on nonspecific connectome properties. Here, we show that anatomically specific LNM is possible with the right statistical approach – using symptom-label permutation as a null model. We demonstrate this in a multicenter dataset of 2,950 stroke patients across 12 cohorts, comparing patients with and without impairment in 6 cognitive domains. First, we showed that permutation-based LNM yielded distinct and biologically plausible network maps with modest cross-cognitive domain similarity. Second, we replicated the previously raised concern of nonspecific connectome-driven convergence when using parametric statistics. Third, we assessed specificity of our approach through simulation analyses across 10,000 null studies which confirmed that the permutation framework maintained valid type I error control. These findings demonstrate that permutation-based null models preserve anatomical specificity in LNM, enabling the identification of brain networks that are genuinely linked to distinct neurobehavioural functions. This approach may thus allow researchers to more reliably map the network basis of neurobehavioural deficits from focal brain lesions.

[This corrects the article DOI: 10.3389/fnhum.2025.1739802.].

Phase contrast electron microscopy is fundamental for visualizing unstained biological specimens. Advances in electron detection have not yet overcome the low contrast caused by weak scattering. Here, we demonstrate that an orthogonal pulsed laser-electron beam interaction produces a pronounced peak phase shift of 430 radians through ponderomotive defocusing, leading to a maximum angular deflection of 45 µrad. Experiments encompassing a variety of probe pulse energies and pump positions verified the properties of the electron pulses in a range of pulse durations from 5.8 ± 1.9 ps to 13.4 ± 0.9 ps and a width of 15.0 ± 2.6 µm at the interaction region. The stability of the beam was also tested across 10 hours of cumulative acquisition time, with only small variations in laboratory conditions resulting in a gradually shifting baseline measurement. Pulsed laser lensing of the electron beam offers the potential for refinement in phase shift and electron beam shaping with careful consideration to the overlap between laser and electron pulses. Calculations of phase shifts across a wide experimental envelope show that poorly chosen laser parameters can generate large incoherent distributions at both 30 keV and 300 keV. Thus, a delicate balance between laser and electron widths and pulse durations must be struck to adequately achieve uniform phase shifts, particularly when singling out specific beamlets in the back-focal-plane.

Bipolar disorder (BD) is a severe mental disorder, characterized by episodes of mania and depression. The longitudinal neurobiological impact of BD episodes on brain structure remains largely unknown. In 124 age-sex-matched participants (62 BD patients; 62 healthy controls; HCs), aged 20-62 years, we investigated the longitudinal relationship between BD episodes and whole-brain gray matter volume (GMV) changes (3 Tesla MRI) during a two-year interval, using voxel-based morphometry in SPM12/CAT12. We compared GMV trajectories between BD patients with at least one depressive or manic episode during the two-year interval, BD patients without an episode, and HCs. We explored associations between GMV changes and clinical variables, like the number and duration of depressive or manic episodes both during the two-year interval and before baseline assessment. BD patients showed GMV increases in the right exterior cerebellum with an increasing number of depressive episodes during the two-year interval. BD patients without recurrence showed GMV reductions in this area, relative to BD patients with recurrence and HCs. Notably, BD patients without recurrence exhibited greater GMV reductions during the two-year interval, the longer they had spent in a manic episode before baseline. Our findings underscore the dynamic nature of brain changes in BD. GMV increases in BD patients with recurrence may be due to acute neuroinflammatory mechanisms including glial cell proliferation, whereas GMV reductions in BD patients without recurrence may result from abnormal synaptic refinement or pruning, as a consequence of past neuroinflammation during BD episodes.

ObjectiveFocal cortical dysplasia (FCD) is a leading cause of drug‐resistant epilepsy and is associated with sleep‐related seizures, yet the underlying electrophysiological mechanisms during different brain states remain poorly understood. We investigated whether fast oscillations (FOs) within the seizure onset zone (SOZ) and irritative zone (IZ) change significantly during sleep compared with wake in FCD patients. We analyzed multiple frequency bands—beta (14–20 Hz), gamma (40–80 Hz), ripple (80–250 Hz), and broadband BGR (14–250 Hz) to provide comprehensive information about sleep‐related changes. We hypothesized that sleep‐related FO changes would be associated with sleep‐related epilepsy, frontal location, or FCD type II.MethodsWe examined intracranial EEG (iEEG) recordings from 22 FCD patients undergoing presurgical evaluation between 2010 and 2023, with a mean age of 25.3 ± 12.8 years and a disease duration of 17.7 ± 12.3 years. Using semiautomated detection, we compared FO rates between wake and sleep epochs, focusing on contacts within IZ and SOZ. Distance‐based multivariate analysis of variance (MANOVA) was employed for patient‐level analysis, accounting for spatial organization and enabling multiband evaluation.ResultsAnalysis of 67 ± 28 bipolar iEEG contact pairs per patient revealed distinct sleep‐related patterns. In SOZ, gamma oscillations showed significant increases in 10/22 patients (45%), followed by BGR in 7/22 patients (32%). IZ exhibited stronger changes, with BGR and gamma showing significance in 13/22 patients each (59%) with high concordance. Gamma oscillation rates in SOZ increased in patients with confirmed sleep‐related epilepsy (p < 0.05), while no associations were found with frontal location or FCD type II.SignificanceGamma oscillations showed robust sleep‐related increases in SOZ, while gamma and BGR frequencies demonstrated strong changes in IZ, often occurring simultaneously. These findings suggest gamma oscillations, complemented by BGR analysis, may qualify as markers for characterizing sleep‐related changes in FCD patients, potentially advancing understanding of mechanisms underlying sleep‐related seizures.Plain Language SummaryFocal cortical dysplasia (FCD) is a brain malformation that causes difficult‐to‐treat epilepsy, with patients experiencing seizures mainly during sleep. We studied electrical brain waves in 22 FCD patients using electrodes placed directly within or on the brain during pre‐surgery evaluation. We compared brain wave activity between wake and sleep, focusing on fast brain waves. We found that fast brain waves, especially gamma waves, increased significantly during sleep in brain areas where seizures start, particularly in patients whose seizures occur mainly during sleep. These findings potentially help us better understand why seizures happen more often during sleep in FCD patients.

Current psychiatric neuroimaging supports the view that major depressive disorder (MDD) is a dysconnection syndrome, characterized by structural brain dysconnectivity. Recent studies investigating this question, however, did not evaluate the involvement of comorbid disorders, of which anxiety disorders (ANX) are particularly prevalent. Here, we investigated the structural connectivity alterations observed in MDD with and without comorbid ANX. To this end, we reconstructed structural brain networks of n = 781 individuals with a diagnosis of MDD who had at least one diagnosis of an ANX (n = 249) and those without any diagnosis of ANX (n = 532), as well as n = 906 healthy controls (HC) from structural and diffusion-weighted MRI. The network-based statistic (NBS) toolbox was employed to evaluate network-level differences in structural connectivity among the three groups. Transdiagnostic analyses were conducted to explore the dimensional relationship between anxiety and structural connectivity. NBS revealed decreased structural connectivity in MDD patients without comorbid ANX and increased structural connectivity in MDD patients with comorbid ANX relative to HC, with both effects found in spatially overlapping white matter connections. Transdiagnostic analyses suggested that increases in anxiety were associated with increased structural connectivity across all groups. Our finding that hyperconnectivity rather than hypoconnectivity characterizes the structural connectome of MDD patients with comorbid ANX challenges the applicability of the dysconnection syndrome hypothesis to MDD with comorbid ANX, warranting symptom-based investigations of brain changes in mental disorders.