Gracie Grimsrud

SUMMARY: Precision functional mapping (PFM) reveals stable individual differences in brain functional network organization and holds promise for advancing neuropsychiatric clinical care. PFM requires sufficient high-quality and densely sampled functional MRI data at the individual level that is typically acquired during the so-called resting state while passively fixating on a crosshair. However, crosshair fixation acquisitions can be challenging especially for pediatric or patient populations, where alternatives like movie watching might be used to improve data quality and reduce participant burden. Here, we compare the use of resting state crosshair fixation and movie watching fMRI data to derive individual-specific functional brain network topography using PFM data (28–38 minutes of low-motion fMRI data per-participant per-condition) in nineteen adolescents (12-18 years) with a range of transdiagnostic psychiatric diagnoses. Network assignments were largely conserved between crosshair fixation and movie watching, with a smaller subset of reproducible network reassignments which were overwhelmingly border shifts between neighboring networks. This suggests largely conserved global structure of functional network topography between crosshair fixation and movie watching, though border shifts can compound into variable estimates of cortical network size particularly for movie positive (language, salience, and parietal memory) and movie negative networks (default mode, action mode). Functional network assignments derived from movies and crosshair were also similarly individual-specific and consistent within common neuromodulation targets including dorsolateral prefrontal cortex and somatomotor cortex. Key results were replicated in independent pediatric consortia data (Healthy Brain Network, n = 466, 6-22 years) and reproduced across independent PFM data sessions. Acknowledging individual differences and level of analysis, we conclude that movies are a viable alternative to resting state crosshair fixation for individual network definitions.

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.

The neighborhoods children grow up in are associated with cognitive and mental health outcomes across the lifespan. Yet how neighborhood-level factors are quantified, and how they relate to large-scale functional brain connectivity, remains an emerging area of investigation. This scoping review synthesizes empirical evidence on associations between area-level factors and resting-state functional brain connectivity and network organization. We included studies assessing resting-state fMRI functional connectivity or rs-fMRI-derived intrinsic activity in relation to validated area-level deprivation indices guided by Trinidad et al.’s (2022) inventory, the Opportunity Atlas, or geocoded crime exposure from the Uniform Crime Reporting program. Of the 28 studies meeting inclusion criteria, the evidence base was unevenly distributed across the lifespan; 25 of 28 (89.3%) examined perinatal, childhood, or adolescent samples, while only one examined adults exclusively. The literature was also methodologically non-independent, over half drew on the Adolescent Brain and Cognitive Development (ABCD) Study, and the Area Deprivation Index was the most commonly used measure (k = 16, 57.1%). Therefore, apparent convergence partly reflects shared data and instruments rather than independent replication. Where directions could be compared, higher area-level disadvantage was associated predominantly with lower within-network connectivity in association and control systems and higher connectivity in somatomotor systems, with several findings attenuating once area-level exposure was isolated from household socioeconomic status. Geographic resolution varied but was rarely justified. Progress will require greater theoretical intentionality in selecting area-level measures, transparent justification of geographic resolution, treatment of dataset and measure non-independence, and investment in understudied developmental periods and non-US contexts.

Early adversity is among the most potent environmental influences on the developing brain; yet whether it remodels the areal layout of cortical networks, and along which dimensions, is unknown. In a longitudinal sample of 4,525 youth aged 9-18, we derived individualized cortical parcellations and related the topography of 15 networks to 10 data-driven dimensions of adversity. Greater exposure to threat, but not deprivation or unpredictability, predicted dose-dependent expansion of the somato-cognitive action network (SCAN), an effect nearly seven times larger than for any other network. The SCAN expanded toward the sensorimotor pole, encroached on neighbors, and shifted functional connectivity toward sensorimotor and away from control systems. Expansion mediated the association between threat and poorer cognition, replicating across waves, within children, and out-of-sample. Rather than diffusing across many systems, the imprint of adversity on the developing cortex converges on the territory of a single integrative network that couples brain to body.

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 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.