Julia Moser

Individualized resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to guide neuromodulation target selection. However, clinical scans are often short and noisy, and standard pipelines for functional network identification do not provide information about confidence of network assignment. With limited data, unstable network assignments can misdirect stimulation toward off-target regions, making it critical to know which assignments can be trusted. We developed Precision Confidence Mapping (PCM), a bootstrap-based framework that makes this uncertainty explicit and actionable. The resulting confidence maps can be thresholded to exclude less stable regions. We evaluated PCM across scan durations from 5 to 70 minutes and determined the resulting spatial precision and extent of individualized network assignments. PCM confidence thresholding markedly improved positive predictive value (PPV) of individualized network assignments relative to an independent within-subject 70-minute reference map across multiple networks, with the largest gains for short scan durations. Critically, within-subject agreement also remained greater than between-subject agreement, indicating that thresholding preserved individual-specific network topography. These precision gains came with modest reductions in full reference-network coverage, particularly at shorter scan durations. This tradeoff may be acceptable for neuromodulation applications that prioritize minimizing off-network assignments. By adding a reliability layer to individualized mapping, PCM supports more cautious and precise neuromodulation targeting under real-world clinical scan constraints.

Individualized resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to guide neuromodulation target selection. However, clinical scans are often short and noisy, and standard pipelines for functional network identification do not provide information about confidence of network assignment. With limited data, unstable network assignments can misdirect stimulation toward off-target regions, making it critical to know which assignments can be trusted. We developed Precision Confidence Mapping (PCM), a bootstrap-based framework that makes this uncertainty explicit and actionable. PCM repeatedly resamples the time series and reruns network detection to estimate how consistently each vertex is assigned to a given network. The resulting confidence maps can be thresholded to exclude less stable regions. We evaluated PCM across scan durations from 5 to 70 minutes using positive predictive value (PPV) as the primary measure of network-assignment precision. PPV quantified the proportion of vertices assigned to a network that received the same label in an independent within-subject 70 minute reference map. Confidence thresholding markedly improved PPV across functional networks, with the largest gains for short scan durations. Compared with standard network assignment, PCM significantly increased agreement with this independent reference. Within-subject agreement remained greater than between-subject agreement, indicating that thresholding preserved individual-specific network topography. These precision gains came with modest reductions in reference-network coverage, particularly at shorter scan durations. This tradeoff may be acceptable for neuromodulation applications that prioritize minimizing off-network assignments. By adding a reliability layer to individualized mapping, PCM supports more cautious and precise neuromodulation targeting under real-world clinical scan constraints.

Personalized functional brain developmental trajectories can be studied with Precision Functional Mapping (PFM). Our previous work has demonstrated that PFM can be achieved in infants despite rapid brain growth. However, even with extensive data collection (up to 1 hour of fMRI), the reliability and precision of these maps remain lower than those observed in youth and adults - particularly within subcortical structures. In this work we demonstrate the utility of high-field 7T MRI compared to 3T MRI for facilitating PFM in infants. We showcase data from multi-echo fMRI acquisitions in the same infants at both 7T and 3T and demonstrate that 7T imaging in infants is safe and feasible with our subject-specific safety workflow. Moreover, we demonstrate that the use of a higher magnetic field strength affords a spatial resolution more appropriately matched to infants’ smaller head and brain sizes, yielding notable improvements in data quality, especially for PFM. The increase in both spatial precision and reliability also suggests that 7T MRI can reduce the amount of data required for PFM. Last, we show how ultra-high field imaging can help us study the development of subcortical-to-cortical connectivity patterns, crucial for understanding brain development during this developmental window. 7T MRI is a promising new avenue for developmental cognitive neuroscience.

Adaptively responding to salient stimuli in the environment is a fundamental feature of cognitive development in early life, which is enabled by the developing brain. Understanding individual variability in how the brain supports this fundamental process is essential for uncovering neurodevelopmental trajectories and potential neurodevelopmental risks. In the present study, we used a precision functional imaging approach to probe activation in response to salient auditory stimuli and its relation to brain functional networks in individual infants. A minimum of 60 minutes of fMRI BOLD data with an auditory oddball paradigm were collected in ten infants with a mean postmenstrual age of 48 weeks. Results demonstrate the feasibility of performing a precision functional imaging study to investigate individual specific responses to salient stimuli in infants. While responses to the auditory oddball were consistent between individuals in auditory processing areas, responses across the rest of the brain differed across individuals in their magnitude and shape. Individual specific response patterns appeared to be relatively stable and differed from other participant’s response patterns, despite fluctuations across runs. Commonalities and differences between individuals demonstrated in this sample contribute to our understanding of how the developing brain instantiates processing of salient stimuli. Our findings suggest that during early development, early unimodal processing is well conserved across individuals, however subsequent perceptual processing is still being personally defined. In this context, individual specific response patterns could be a promising target for biomarkers of normative brain and cognitive development.

In early human development, perceptual processes grow faster with maturation, as inferred using the duration of the attentional blink and multisensory integration window. The consequences of this developmental trend for sensory-cognition in fetuses are unclear: does the fetus perceive rapid stimuli as discrete events or, rather, one fused stimulus? We addressed this question using frequency tagging in two experiments with rapid auditory stimuli while neural responses were recorded in the third trimester with fetal magnetoencephalography (MEG). Our results are the first successful demonstration of frequency tagging in the fetal MEG amplitude spectrum and show that the fetal cortex generates separate neural responses to discrete auditory stimuli in both experiments, and similar results were also obtained when one experiment was repeated in newborns. While we cannot rule out perceptual fusion of rapid stimuli in higher-order association cortices, our results weaken the hypothesis that fetuses fuse rapid auditory stimuli into a single prolonged percept. Finally, our work points to frequency tagging analysis as a solution which avoids the uncertainties surrounding immature neural response latencies in time-domain analysis of fetal MEG.

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development. Precise characterization of systems organization during periods of high plasticity is likely to be essential for discoveries promoting lifelong health. Obtaining precision functional magnetic resonance imaging (fMRI) data during development has unique challenges that highlight the importance of establishing new methods to improve data acquisition, processing, and analysis. Here, we investigate two methods that can facilitate attaining this goal: multi-echo (ME) data acquisition and thermal noise removal with Noise Reduction with Distribution Corrected (NORDIC) principal component analysis. We applied these methods to precision fMRI data from adults, children, and newborn infants. In adults, both ME acquisitions and NORDIC increased temporal signal to noise ratio (tSNR) as well as the split-half reliability of functional connectivity matrices, with the combination helping more than either technique alone. The benefits of NORDIC denoising replicated in both our developmental samples. ME acquisitions revealed longer and more variable T2* relaxation times across the brain in infants relative to older children and adults, leading to major differences in the echo weighting for optimally combining ME data. This result suggests ME acquisitions may be a promising tool for optimizing developmental fMRI, albeit application in infants needs further investigation. The present work showcases methodological advances that improve Precision Functional Mapping in adults and developmental populations and, at the same time, highlights the need for further improvements in infant-specific fMRI.

Predictive coding theories propose that the brain constantly updates internal models to minimize prediction errors and optimize sensory processing. However, the neural mechanisms that link prediction error encoding and optimization of sensory representations remain unclear. Here, we provide evidence how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in humans listening to acoustic sequences with different levels of regularity. We found that the brain aligns its representational geometry to match the statistical structure of the sensory inputs, by clustering temporally contiguous and predictable stimuli. Crucially, the magnitude of this representational shift correlates with the synergistic encoding of prediction errors in a network of high-level and sensory areas. Our findings suggest that, in response to the statistical regularities of the environment, large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas to enhance sensory processing.

A single dose of psilocybin, a psychedelic that acutely causes distortions of space–time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials^1–4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus^5–8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6–12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.