ABSTRACTThis paper introduces an advanced framework for accelerated processing of diffusion‐weighted imaging (DWI) data that utilizes an entire‐image modeling approach to optimize the estimation of diffusion parameters from DWIs by mapping input diffusion data to predicted signals and estimating parameter values via a stochastic gradient descent optimizer (Adam). To validate this approach, we applied this framework to diffusion basis spectrum imaging (DBSI) and analyzed in vivo human brain and ex vivo mouse brain DWIs. Results demonstrate significant improvements to computational speed and signal‐to‐noise ratio (SNR) in estimated parameter maps compared to standard DBSI. Our approach is applicable to any diffusion signal representation and enables rapid and reliable signal partitioning in complex microstructural environments, demonstrating the potential of this framework for future neuroimaging research.

Using fMRI and pRF modeling, we show that visual spatial information is represented in the auditory cortex of congenital deaf individuals through deactivation signals. These negative BOLD responses suggest a novel mechanism of cross-modal plasticity.

Puberty is a critical developmental process that is associated with changes in pubertal (or steroid) hormone levels, which are believed to influence adolescent behaviour via their effects on the developing brain. So far, there are limited and inconsistent findings regarding the relationship between steroid hormones and brain structure and function in adolescent females, with many existing studies employing small sample sizes. Thus, in this study, we explored the association between oestradiol (E2), testosterone (Tes), and dehydroepiandrosterone (DHEA) and brain structure (gray matter volume, sulcal depth, cortical thickness, and white matter microstructure) and function (resting-state connectivity, emotional n-back task-related function) in 3024 adolescent females (age 8.92-13.33 years, mean age (SD) = 10.37 (0.94) years) from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study. We used elastic-net regression with cross-validation to investigate associations between hormones and brain phenotypes derived from multiple imaging modalities. We found that structural brain features, including cortical thickness, sulcal depth, and white matter microstructure, and resting state connectivity between cortical networks and subcortical regions, were important features associated with hormones. E2 was most strongly associated with prefrontal and premotor regions involved in working memory and emotion processing, while Tes and DHEA were most strongly associated with parietal and occipital regions involved in visuospatial functioning. All three hormones were also associated with prefrontal, temporoparietal junction, and insula cortices. Thus, using an advanced methodological approach, this study suggests both unique and overlapping neural correlates of pubertal hormones in adolescent females and sheds light on the mechanisms by which puberty influences adolescent development and behaviour.

ABSTRACTThe sense of self is a multidimensional feature of human experience. Different dimensions of self‐experience can change drastically during altered states of consciousness induced through meditation or psychedelic drugs, as well as in a variety of mental disorders. Some experienced meditation practitioners are able to modulate their sense of self deliberately, which allows for a direct comparison between an active and suspended sense of self. Meditation therefore has the potential to serve as a model‐system for alterations in the sense of self. The current study aims to identify a neural marker of such meditation‐induced alterations in the sense of self based on magnetoencephalography (MEG) recordings of meditation practitioners (N = 41). Participants alternated between a state of reduced sense of self, termed self‐boundary dissolution, a resting state and a control meditation state of maintaining their sense of self. Machine learning methods were used to find multivariate patterns of brain activity which distinguish these states on a single‐trial basis. Source band power and Lempel‐Ziv complexity features allowed to predict the mental state from MEG recordings with significantly above‐chance accuracy (> 0.5). The highest performance was obtained for the self‐boundary dissolution versus rest classification based on Lempel‐Ziv complexity, which showed an average accuracy of ~0.64 when training and testing were performed on data from the same individual (within‐participant prediction) and ~0.57 when models trained on one group of individuals were tested on different participants (across‐participant prediction). Potential applications include decoded neurofeedback, for example, for clinical treatments of disorders of the sense of self, or for assistance in meditation training.

ABSTRACTGlutamate‐weighted Chemical Exchange Saturation Transfer (GluCEST) captures in vivo glutamate (Glu) levels with high spatial resolution and has been used to assess glutamatergic function in healthy and clinical populations. While GluCEST is well‐validated against proton magnetic resonance spectroscopy (1H‐MRS), its correspondence with local expression of glutamatergic neurotransmitter receptors remains unclear. Recent initiatives, such as Neuromaps, have collated positron emission tomography (PET) data into curated, publicly available databases, providing a novel opportunity to establish convergence in the regional distribution of GluCEST and normative receptor density maps. Here, we examine the spatial correspondence between GluCEST signal and PET‐based cortical receptor density levels of N‐methyl‐D‐aspartate (NMDA), metabotropic glutamate receptor 5 (mGluR5), and gamma‐aminobutyric acid A (GABAA). A cohort of 86 participants (age: 22.7 years [3.7 years], 45% female) included 34 individuals with no psychiatric history, 31 participants with significant sub‐threshold psychosis symptoms, and 21 participants with first‐episode psychosis. All participants underwent 7T GluCEST imaging. Data were processed using in‐house and field‐standard pipelines. Mean receptor density levels were computed using the Neuromaps PET receptor density data. GluCEST and Neuromaps data were parcellated using the Cammoun 500 atlas. Pearson correlations assessed the correspondence between GluCEST signal and PET‐based receptor density, and spin tests were used for empirical significance testing of the spatial correlations across all parcels. Sensitivity analyses examined the effect of age, sex, and diagnosis and other covariates. Exploratory analyses assessed regional variability across cytoarchitecturally defined von Economo regions and overall trends with gene expression. Analyses were performed in Python and R. GluCEST signal converged with the regional distribution of both NMDA (r = 0.23, pspin = 0.039) and GABAA (r = 0.35, pspin = 0.004). There was no significant effect for mGluR5 (r = 0.09, pspin > 0.05). Exploratory analyses indicated that cytoarchitecturally defined von Economo regions showed variable GluCEST‐receptor association patterns across the cortex and that gene expression patterns generally correspond with receptor density findings. Our findings reveal a positive spatial association between GluCEST signal in a transdiagnostic cohort and atlas‐based PET‐derived cortical receptor density of NMDA and GABAA, and a nominal positive association with mGluR5. The association between GluCEST and NMDA suggests that regions with dense ionotropic Glu receptors exhibit higher Glu levels, while the coupling between GluCEST and GABAA may reflect tight regulation of excitation‐inhibition balance. Regional differences in these associations point to the potential influence of local cytoarchitectural specialization on Glu‐receptor dynamics. These results advance our understanding of the neurobiological basis of GluCEST and highlight its potential utility as a non‐invasive tool for probing receptor‐mediated glutamatergic neurotransmission.

ABSTRACTPupillary responses are windows into human cognition, but their neural substrates are poorly understood. We studied brain–pupil coupling through intracranial recordings and pupillometry in 13 children and youth with epilepsy (ages 9–18) during an attentional set‐shifting task. Time‐resolved mixed‐effects modelling identified associations between pupil diameter, neural activity and cognitive performance. We first showed that pupillary dynamics are closely linked to cognitive performance, with task‐stage dependencies. Larger pupil sizes prior to stimulus onset were associated with faster reaction times, whereas smaller pupil sizes during and after stimulus presentation were linked to better performance. Next, linear models identified associations between band‐limited power in task‐relevant neural networks and pupil size changes during the task. Finally, deep learning models based on intracranial neural activity captured patterns predictive of changes in pupil size in five of seven participants that generalised to recordings from a separate day. Using salience‐based gradient mapping, we identified a network of task‐relevant cortical and subcortical regions whose engagement was consistently associated with higher model performance in predicting pupil dynamics during attentional set‐shifting. Our findings suggest pupillary responses are coordinated with goal‐oriented cognitive processing, providing a basis for modelling cognitive functions through pupillary dynamics.

ABSTRACTListening to music is a ubiquitous human activity, but little is known about its functional cerebral correlates. We investigated the dynamics of fMRI‐based brain activation patterns associated with two musical compositions and examined whether these patterns are modulated by the degree of musical expertise. Specifically, 24 aspiring professionals and 17 amateur musicians listened to a baroque composition by J. S. Bach and an early modern piece by A. Webern. Using measures of dynamic and static functional connectivity and graph theory, we identified two distinct brain states: one characterized by higher modularity (greater segregation), and the other by higher global efficiency (greater integration). Participants spent more time in the segregated state while listening to Bach, and more frequently shifted to the integrated state during Webern's piece. An anticorrelation was observed between segregation and music complexity as measured by permutation entropy, indicating that music with higher complexity elicited more integrated brain states. Individuals with greater musical expertise demonstrated higher global efficiency during the Webern piece and engaged more frontal, temporal, and parietal regions as functional hubs. These findings suggest that musical form and expertise jointly shape the brain's functional organization during naturalistic music listening.

ABSTRACTOver time, we develop event schemas or scripts that shape our expectations about what typically happens in certain contexts. However, even after forming a memory about a certain event, we are often exposed to related information about that same event at later points in time. This additional information sometimes causes one to have to re‐evaluate the interpretation of the original event. Over a two‐day fNIRS experiment, participants were exposed to events that were subsequently updated with schema‐congruent or schema‐incongruent additional details. These schema‐incongruent additional details make those events more fitting to another schema than originally was the case, meaning that participants would need to dissociate that event from the original schema and re‐integrate it with another schema. The fNIRS results showed higher oxygenated hemoglobin (HbO) concentrations in the prefrontal cortex (PFC)—which indicates enhanced PFC activation—for events updated with schema‐congruent compared to schema‐incongruent details. When specifically looking at those events that were updated with schema‐incongruent details, our results suggest that dissociating an event from the original schema and re‐integrating it with another schema was accompanied by an initial PFC decrease early in the trial followed by a PFC increase later in the trial. This was a distinctly different pattern compared to trials in which participants failed to re‐integrate the event with another schema, which showed delayed PFC increase with lower amplitude and no initial PFC decrease. Our findings provide insight into how people process and integrate conflicting information.

ABSTRACTUnderstanding how the brain develops, matures, ages, and declines is one of the fundamental questions facing neuroscience. This study uses 3‐tissue constrained spherical deconvolution (3T‐CSD) and more recently developed fixel‐based derivatives to examine the relationship between brain diffusion microstructure and chronological age. These metrics are able to quantify signal fraction measurements at the voxel‐wise level from six different tissue microenvironments found in the brain: extracellular free water, intracellular isotropic, intracellular anisotropic, fiber density and cross‐section, the g‐ratio, and aggregate conduction velocity. This study analyzes the Nathanial Kline Institute's Rockland cohort, a large‐scale community sample of brain MRI data across the lifespan (409 subjects, ages 5–85 years). Microstructural measurements were taken in a number of structures throughout the white matter, subcortical gray matter, and lobar cortical regions while additionally evaluating lateral differences in microstructural measurements. The general trajectory of signal fraction measurements was a positive relationship with age and extracellular signal fraction and g‐ratio, a negative relationship between age and intracellular isotropic signal fraction, fiber density and cross‐section, and conduction velocity, and an inverted U‐shaped trajectory for the intracellular anisotropic signal fraction. In individual sub‐areas, these trends tended to still be present, with some notable exceptions. However, there were large differences in microstructure measurements between individual structures, including significant lateral differences between hemispheres for each of the subcortical gray matter structures and for each of the cortical regions. These results demonstrate that 3T‐CSD and fixel‐based metrics are able to describe age‐related change across the brain and lifespan. By using a healthy population cohort this study can be used as a point of comparison for analysis of microstructure changes in the presence of pathology or with behavior. Finally, the detailed analysis of lateralized ROI results can inform diffusion microstructure studies examining cortical and subcortical regions.

ABSTRACTWhen operating on gliomas near critical language regions, surgeons risk either leaving residual tumor or inducing permanent postoperative language deficits (PLDs). Despite the advent of intraoperative mapping techniques, subjective judgments frequently determine important surgical decisions. We aim to inform data‐driven surgery by constructing a non‐invasive mapping approach that quantitatively predicts the impact of individual surgical decisions on long‐term language function. This study included 79 consecutive patients undergoing resection of language‐eloquent gliomas. Patients underwent preoperative navigated transcranial magnetic stimulation (TMS) language mapping to identify language‐positive sites (“TMS points”) and their associated white matter tracts (“TMS tracts”) as well as formal language evaluations pre‐and postoperatively. The resection of regions identified by preoperative mapping was correlated with permanent postoperative language deficits (PLDs). Resected tract segments (RTS) were normalized to MNI space for comparison with normative data. The resection of TMS points did not predict PLDs. However, a TMS point subgroup defined by white matter connectivity significantly predicted PLDs (OR = 8.74, p < 0.01) and demonstrated a canonical distribution of cortical language sites at a group level. TMS tracts recapitulated normative patterns of white matter connectivity defined by the Human Connectome Project. Subcortical resection of TMS tracts predicted PLDs independently of cortical resection (OR = 60, p < 0.001). In patients with PLDs, RTS showed significantly stronger co‐localization with normative language‐associated tracts compared to RTS in patients without PLDs (p < 0.05). Resecting patient‐specific co‐localizations between TMS tracts and normative tracts in native space predicted PLDs with an accuracy of 94% (OR = 134, p < 0.001). Prospective application of this data in a patient with glioblastoma precisely predicted the results of intraoperative language mapping with direct subcortical stimulation. Long‐term postoperative language deficits result from resecting patient‐specific white matter segments. We integrate these findings into a personalized tool that uses TMS language mappings, diffusion tractography, and population‐level connectivity to preoperatively predict the long‐term linguistic impact of individual surgical decisions.