Biology subjects

Dosenbach, N. U.

Publications and source records attributed to Dosenbach, N. U..

10 recordsLinked to original sources

bioRxiv2026

Functional organization underlying superior performance in a memory champion

Memory athletes can achieve superior performance (e.g., memorizing 339 digits in 5 minutes) with extensive daily training, by converting abstract information into vivid scenes, and placing them along a mental path, that is later retraced (Method of Loci). Understanding the brain mechanisms underlying such training-derived mastery would increase our understanding of the brains memory systems and could suggest novel approaches to improving cognition in other domains. As memory athletes use personalized training techniques, it has been challenging to study them with standard group paradigms. Fortunately, precision functional mapping (PFM) enables detailed investigation of individual brains through repeated sampling of resting-state functional connectivity and task fMRI. Here, we precisely mapped the brain organization of a 6-time U.S. Memory Champion (>13 hours fMRI). Relative to controls, the Memory Champions network functional connectivity (FC) was strengthened with the retrosplenial, extrastriate visual, and dorsal frontal cortex (area 55b), as well as with the caudate nucleus. The Memory Champion had modules related to scene and semantic processing not seen in controls, alongside stronger connectivity between the caudate and classical memory networks. During rote memorization, the Champions task fMRI patterns were typical, with the hippocampus active during encoding. This pattern was reversed when he used his Method of Loci technique, with greater hippocampal activity during recall than encoding. Hence, intense practice at converting abstract information into more memorable formats can develop a procedural memory skill that utilizes brain regions typically reserved for navigation, language, social cognition, and associative learning.

neuroscience↗

bioRxiv2025

Patterns of brain-wide associations reflect socioeconomics

Previous brain-wide association studies (BWAS) cross-sectionally linked a specific behavioral trait, most commonly IQ or psychopathology, to variation in brain function or structure. Here, we expanded the focus of BWAS from effect sizes to interpretability and generalizability by mapping 649 variables to brain function and structure. We compared the resultant BWAS maps to other types of brain data to annotate the BWAS patterns. Socioeconomic status (SES) -- not IQ or psychopathology -- showed the strongest associations with both resting-state functional connectivity (RSFC) and cortical thickness in the Adolescent Brain Cognitive Development (ABCD) Study. A principal exposome brain pattern, anchored to sensory and motor cortex, captured 34% of the variance across all BWAS maps. This exposome pattern was strongly correlated with the SES and IQ BWAS maps and non-BWAS maps of sleep (EEG), norepinephrine (PET), and stimulants (drug trial), but not cognitive activation maps (task fMRI). Adjusting for SES, reduced brain-IQ associations by 40%. Brain with IQ associations did not generalize, as they could no longer be detected in subsamples drawn from only higher SES backgrounds, while brain with SES associations remained strong in higher-IQ-only subsamples. These findings reveal SES as the principal axis of population-level brain variation, possibly stemming from the sleep deprivation and heightened stress associated with lower SES, since socioeconomics can only indirectly affect the brain.

neuroscience↗

bioRxiv2025

Precision functional mapping reveals less inter-individual variability in the child vs. adult human brain

Human brain organization shares a common underlying structure, though recent studies have shown that features of this organization also differ significantly across individual adults. Understanding the developmental pathways that lead to individually unique brains is important for advancing models of cognitive development and neurodevelopmental disorders. Here we use highly personalized precision neuroimaging methods to map brain networks within 12 individual children, ages 8-12 years. We demonstrate fMRI functional connectivity maps that substantially exceed the reliability of traditional techniques, allowing us to measure individual differences after overcoming biases from measurement noise. Children share core functional network topography, with greatest inter-individual variability in association regions, consistent with adult findings. However, children show less between-subject variability than adults, suggesting increasing individual differentiation in brain networks with development. This pediatric precision neuroimaging dataset is publicly available to support future brain development research and provides a high-fidelity foundation for studying individual variation in atypical development.

neuroscience↗

bioRxiv2022

Motion Impact Score for Detecting Spurious Brain-Behavior Associations

Between-participant differences in head motion introduce systematic bias to resting state fMRI brain-wide association studies (BWAS) that is not completely removed by denoising algorithms. Researchers who study traits, or phenotypes associated with in-scanner head motion (e.g. psychiatric disorders) need to know if trait-functional connectivity (FC) effects are biased by residual motion artifact in order to avoid reporting false positive results. We devised an adaptable method, Split Half Analysis of Motion Associated Networks (SHAMAN), to assign a motion impact score to specific trait-FC effects. The SHAMAN approach distinguishes between motion artifact causing overestimation or underestimation of trait-FC effects. SHAMAN was > 95% specific at sample sizes of n = 100 and above. SHAMAN was powered to detect motion overestimation scores 80% of the time at sample sizes of n = 5,000 but could detect motion underestimation scores only 50% of the time at n = 5000, making it most useful for researchers seeking to avoid overestimating trait-FC effects in large BWAS. We computed motion impact scores for trait-FC effect with 45 demographic, biophysical, cognitive, and personality traits from n = 7,270 participants in the Adolescent Brain Cognitive Development (ABCD) Study. After standard denoising with ABCD-BIDS and without motion censoring, 42% (19/45) of traits had significant (p < 0.05) motion overestimation scores and 38% (17/45) of traits had significant motion underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced the proportion of traits with significant motion overestimation scores from 42% to 2% (1/45) but did not decrease the number of traits with significant motion underestimation scores.

neuroscience↗

bioRxiv2021

Adolescent Brain Cognitive Development (ABCD) Community MRI Collection and Utilities

The Adolescent Brain Cognitive Development Study (ABCD), a 10 year longitudinal neuroimaging study of the largest population based and demographically distributed cohort of 9-10 year olds (N=11,877), was designed to overcome reproducibility limitations of prior child mental health studies. Besides the fantastic wealth of research opportunities, the extremely large size of the ABCD data set also creates enormous data storage, processing, and analysis challenges for researchers. To ensure data privacy and safety, researchers are not currently able to share neuroimaging data derivatives through the central repository at the National Data Archive (NDA). However, sharing derived data amongst researchers laterally can powerfully accelerate scientific progress, to ensure the maximum public benefit is derived from the ABCD study. To simultaneously promote collaboration and data safety, we developed the ABCD-BIDS Community Collection (ABCC), which includes both curated processed data and software utilities for further analyses. The ABCC also enables researchers to upload their own custom-processed versions of ABCD data and derivatives for sharing with the research community. This NeuroResource is meant to serve as the companion guide for the ABCC. In section we describe the ABCC. Section II highlights ABCC utilities that help researchers access, share, and analyze ABCD data, while section III provides two exemplar reproducibility analyses using ABCC utilities. We hope that adoption of the ABCCs data-safe, open-science framework will boost access and reproducibility, thus facilitating progress in child and adolescent mental health research.

neuroscience↗

bioRxiv2021

Precision Diffusion Imaging

Diffusion tensor imaging (DTI) aims to non-invasively characterize the anatomy and integrity of the brains white matter fibers. To establish individual-specific precision approaches for DTI, we defined its reliability and accuracy as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). DTI methods that allow for crossing fibers (BedpostX [BPX], Q-Ball Imaging [QBI]) estimated excess fibers when insufficient data was present and when the data did not match the model priors. To reduce such overfitting, we developed a novel crossing-fiber diffusion imaging method, Bayesian Multi-tensor Model-selection (BaMM), that is designed for high-quality repeated sampling data sets. BaMM was robust to overfitting, showing high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, the choice of diffusion imaging analysis method is important for the success of individual-specific diffusion imaging. Importantly, for potential clinical applications of individual-specific precision DTI, such as deep brain stimulation (DBS), other forms of neuromodulation or neurosurgical planning, the data quantities required to achieve DTI reliability are lower than for functional MRI measures.

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bioRxiv2020

Parallel Hippocampal-Parietal Circuits for Self- and Goal-oriented Processing

The hippocampus is critically important for a diverse range of cognitive processes, such as episodic memory, prospective memory, affective processing, and spatial navigation. The human hippocampus has been thought of as being solely functionally connected to a set of neocortical regions known as the default mode network (DMN), which supports self-referential cognition. Using individual-specific precision functional mapping of resting state fMRI data, we found the anterior hippocampus (head and body) to be preferentially connected to the DMN as expected. The hippocampal tail, however, was strongly preferentially connected to the parietal memory network (PMN), which supports goal-oriented cognition and stimulus recognition. This resting state functional connectivity (RSFC) anterior-posterior dichotomy was well-matched by differences in task deactivations and anatomical segmentations of the hippocampus. Task deactivations were localized to the head and body of the hippocampus (DMN), relatively sparing the tail (PMN). Anterior and posterior hippocampal connectivity was network-specific even though the DMN and PMN are interdigitated in medial parietal cortex. The functional dichotomization of the hippocampus into anterior DMN-connected and posterior PMN-connected parcels suggests parallel, but distinct circuits between the hippocampus and medial parietal cortex for self vs. goal-oriented processing.

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bioRxiv2018

Atypical functional connectivity in Tourette syndrome differs between children and adults

BackgroundTourette syndrome (TS) is a neuropsychiatric disorder characterized by motor and vocal tics that typically change over development. Whether and how brain function in TS also differs across development has been largely understudied. Here, we used functional connectivity MRI to examine whole brain functional networks in children and adults with TS.\n\nMethodsMultivariate classification methods were used to find patterns among functional connections that distinguish TS from controls separately for children and adults (total N = 202). We tested whether the patterns of connections that classify diagnosis in one age group (e.g., children) could classify diagnosis in another age group (e.g., adults). We also tested whether the developmental trajectory of these connections were altered in TS.\n\nResultsPatterns of functional connections that distinguished TS from controls were generalizable to an age-matched independent test set, but not to other age groups. While diagnostic classification was successful in children and adults separately, the connections that best distinguished TS from controls were age-specific. When contextualized with typical development, some functional connections exhibited accelerated maturation in childhood TS, while others exhibited delayed maturation in adulthood TS.\n\nConclusionsOur results demonstrate that brain networks are differentially altered in children and adults with TS, and that the developmental trajectory of affected connections is disrupted. These findings further our understanding of neurodevelopmental trajectories in TS and carry implications for future applications aimed at predicting the clinical course of TS in individuals over development.

neuroscience↗

bioRxiv2018

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011 and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

neuroscience↗

bioRxiv2018

The community structure of functional brain networks exhibits scale-specific patterns of variability across individuals and time

The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brains modular network organization, which can be assessed quantitatively using powerful computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Though the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability - which would be undetectable using past approaches - is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advancement over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.

neuroscience↗