Biology subjects

Zuo, X.-N.

Publications and source records attributed to Zuo, X.-N..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Activating Cognitive Processes in Human Cortex at Multiple Low-Frequency Bands

Neural oscillations are fundamental for brain function, governing various cognitive processes. While electrophysiological studies have characterized the frequency properties of these oscillations, their spatial resolution is limited. In contrast, functional magnetic resonance imaging (fMRI) provides high spatial resolution but was initially restricted to a narrow frequency range. This study aims to bridge the gap by investigating the role of blood-oxygen-level-dependent (BOLD) oscillations across multiple frequency bands in cognitive processes using high temporal resolution task-fMRI data. Our findings reveal that different frequency bands are associated with distinct functional processes. Specifically, the slow-1 to slow-3 bands primarily contribute to local sensory information processing, while the slow-4 band is crucial for various fundamental cognitive functions, including somatic motor function and social cognitive function. The slow-5 band is involved in cognitive processes requiring higher memory load, integrated cognitive processing, and attention maintenance. Through multiband activation analysis, this study underscores the importance of analyzing a broad frequency range to capture the full spectrum of brain function. These findings highlight the diverse roles of different frequency bands in brain activity, shedding light on the underlying mechanisms of cognitive processes. This research enhances our understanding of the neural mechanisms underlying cognitive processes and has significant implications for cognitive neuroscience and clinical applications.

neuroscience↗

bioRxiv2024

Social anxiety links of amygdala switch cross puberty

Socioemotional functions have been linked to the amygdala through molecular mechanisms observed in animals. However, these links remain largely unestablished in the development of the human amygdala during puberty. By precisely tracing the amygdala with longitudinal data spanning childhood and adolescence, our aim is to capture the dynamic relationship between social anxiety and amygdala geometry. Around the onset of puberty (10-11.5 years), we detected shifting associations between amygdala volume and social anxiety. Higher social anxiety is associated with a larger amygdala in mid-childhood but a smaller amygdala in early adolescence. Further geometry analysis revealed regional deformations that underpinned the shift. Our findings reconcile inconsistent results from previous studies and respect the intrinsic dimension of development in resolving amygdala-anxiety links during puberty.

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bioRxiv2024

Human Brain Mapping of Homotopic Functional Affinity

Homotopic positions are defined as the two areas with opposite but equal horizonal coordinates in the standard symmetric brain space. Characterizing similarity between two homotopic areas, brain homotopy represents a typical feature of the two hemispheres for both structure and function. Functional homotopy provides important perspectives for understanding neural correlates of cognition and behavior. Despite the decisive role of spatial geometric constraints and homophilic attachment on the human connectome, traditional practices in mapping functional homotopy only considered the temporal correlations of functional timeseries between homotopic areas, but ignored the homophily factors in generative connectivity models. Here, we proposed a novel method for functional homotopy analysis, namely Homotopic Functional Affinity (HFA). This method quantifies the homotopic affinity as the cosine distance of the full-brain functional connectivity profiles or fingerprints between the homotopic areas. HFA captures both geometric constraints (homotopic location) and homophily (affinity) simultaneously. By leveraging the resting-state fMRI data from the Human Connectome Project (HCP) and the Chinese HCP (CHCP), we mapped the 700ms-2mm high spatiotemporal resolution HFA and evaluated its test-retest reliability with linear mixed models, exhibiting generally fair-to-substantial reliable measurements of individual differences in HFA. The lowest HFA observed in the temporo-parietal junction (TPJ) inspired to perform an edge-detection algorithm on its surface render and derived three clearly differentiable and adjacent TPJ subregions: the anterior TPJ (TPJa), the central TPJ (TPJc), and the posterior TPJ (TPJp). We further validated the HFA for the three TPJ regions through a set of comprehensive analyses, including the delineation of their functional connectivity fingerprints, the meta-analysis of their cognitive functions, and their task-activation correlation. Finally, we linked the cortical HFA map to those multimodal brain maps of gene expression, evolution, myelination, functional hierarchy, and cognitive association. The systematic subregion analysis revealed the complex hemispheric specialization of TPJ in attention, social cognition, and language functions. In general, functional specialization of the TPJ areas was stronger in the left hemisphere. The findings from the task activation correlation were highly consistent with those of the meta-analysis. Notably, there were significant differences in social cognition relevant to the three TPJ areas between HCP and CHCP datasets. Furthermore, the correlation analysis of multimodal brain maps illustrated a close relationship between the HFA map and multimodal brain maps. The consistency of maps derived in distinct analyses demonstrated the feasibility of HFA in further understanding psychological and behavioral mechanisms on neural lateralization from the perspective of hemispheric functional integration and specialization. In summary, the proposed HFA framework provides a reliable and valid functional brain mapping tool, with broad applicability in population neuroscience.

neuroscience↗

bioRxiv2023

A resource for probing influences of perinatal factors on neurodevelopment in 3-10-years-old Chinese children

Adverse perinatal factors can disrupt the normal development of the brain, with potential long-term impacts on childrens overall development. Currently, the neuropathological mechanisms by which these factors lead to various neurodevelopmental disorders (NDDs) remain largely unknown. An open resource that integrate perinatal factors with brain and mental health development is essential for investigating NDD-related aetiology. In this Data Descriptor, we introduce a multicentre database containing information on perinatal factors that influence childrens brain-mind development, namely, periCBD, that combines neuroimaging and behavioural phenotypes with perinatal factors associated with a high incidence of NDDs at county/region/central district hospitals. PeriCBD was designed to establish a platform for the investigation of individual differences in brain-mind development among children aged 3-10 years are associated with perinatal factors. Ultimately, our goal was to develop an early prediction and screening model for NDDs that leverages normative data to facilitate NDD aetiology research. Herein, we provide a systematic overview of the data acquisition/cleaning/quality control/sharing, processes of periCBD and present preliminary brain-mind associations.

neuroscience↗

bioRxiv2023

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), a ten-year pilot stage of the lifespan CCNP (2013-2032), is an ongoing project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community (https://www.scidb.cn/en/c/ccnp) at the Science Data Bank. Design TypesO_LIAccelerated longitudinal design C_LIO_LIBrain-mind development C_LIO_LIPopulation imaging C_LIO_LIBrain chart C_LIO_LIRepeated measure C_LI MeasurementsO_LIPsychological behaviours C_LIO_LIBiophysical and physical measures C_LIO_LIIntelligence quotient measure C_LIO_LINeuroimaging C_LI Sample Characteristic - OrganismO_LIHomo sapiens C_LI Sample Characteristic - EnvironmentO_LISchool- and community-based sample C_LI Sample Characteristic - LocationO_LIChongqing and Beijing, China C_LI DurationO_LI10 years (2013-2022) C_LI

neuroscience↗

bioRxiv2022

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local- global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (GITHUB_LINK).

neuroscience↗

bioRxiv2022

Reduced ventral attention network connectivity is linked to the accelerated maturation of adult-like cortical organization in childhood

The human brain experiences functional changes through childhood and adolescence, shifting from an organizational framework anchored within sensorimotor and visual regions into one that is balanced through interactions with later-maturing aspects of association cortex. Here, we link this profile of functional reorganization to the development of ventral attention network connectivity across independent datasets. We demonstrate that maturational changes in cortical organization preferentially link to within-network connectivity and heightened degree centrality in the ventral attention network, while connectivity within network-linked vertices predicts cognitive ability. This connectivity is closely associated with maturational refinement of cortical organization. Children with low ventral attention network connectivity exhibit adolescent-like topographical profiles, suggesting that attentional systems may be relevant in understanding how brain functions are refined across development. These data suggest a role for attention networks in supporting age-dependent shifts in cortical organization and cognition across childhood and adolescence.

neuroscience↗

bioRxiv2021

A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis

Connectomics--the study of brain networks--provides a unique and valuable opportunity to study the brain. Research in human connectomics, leveraging functional and diffusion Magnetic Resonance Imaging (MRI), is a resource-intensive practice. Typical analysis routines require significant computational capabilities and subject matter expertise. Establishing a pipeline that is low-resource, easy to use, and off-the-shelf (can be applied across multifarious datasets without parameter tuning to reliably estimate plausible connectomes), would significantly lower the barrier to entry into connectomics, thereby democratizing the field by empowering a more diverse and inclusive community of connectomists. We therefore introduce MRI to Graphs (m2g). To illustrate its properties, we used m2g to process MRI data from 35 different studies ({approx} 6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that adhered to established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes estimated by m2g are more similar within individuals than between them, suggesting that m2g preserves biological variability. m2g is portable, and can run on a single CPU with 16 GB of RAM in less than a couple hours, or be deployed on the cloud using its docker container. All code is available on https://github.com/neurodata/m2g and documentation is available on docs.neurodata.io/m2g.

bioinformatics↗

bioRxiv2021

FMRI multi-scale cortical spontaneous activity: 7T vs. 3T

This paper describes the use of the Human Connectome Project (HCP) data for mapping the distribution of spontaneous activity in the human brain across different spatial scales, magnets and individuals. Specifically, the resting-state functional MRI signals acquired under the HCP 3 tesla (T) and 7T magnet protocols were measured by computational methods at multiple spatial scales across the cerebral cortex using: 1) an amplitude metric on a single measuring unit (ALFF), 2) a functional homogeneity metric on a set of neighboring measuring units (ReHo) and 3) a homotopic functional connectivity metric on pairs of symmetric measuring units between the two hemispheres (VMHC). Statistical assessments on these measurements revealed that all the raw metrics were enhanced by the higher magnetic field, highlighting their dependence on magnet field strength. Measurement reliability of these global measurements were moderate to high and comparable between between 3T and 7T magnets. The differences in these measurements introduced by the higher magnetic field were spatially dependent and varied according to specific cortical regions. Specifically, the spatial contrasts of ALFF were enhanced by the 7T magnet within the anterior cortex while weakened in the posterior cortex. This is opposite for ReHo and VMHC. This scale-dependent phenomena also held true for measurement reliabilities, which were enhanced by the 7T magnet for ReHo and VMHC and weakened for ALFF. These reliability differences were primarily located in high-order associate cortex, reflecting the corresponding changes of individual differences: higher between-subject variability and lower within-subject variability for ReHo and VMHC, lower between-subject variability and higher within-subject variability for ReHo and VMHC with respect to higher magnetic field strength. Our work, for the first time, demonstrates the spatial-scale dependence of spontaneous cortical activity measurements in the human brain and their test-retest reliability across different magnet strengths, and discussed about the statistical implications for experimental design using resting-state fMRI.

neuroscience↗

bioRxiv2021

Toward Reliable Network Neuroscience for Mapping Individual Differences

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies - with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: 1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions, 2) construct functional networks using spontaneous brain activity in multiple slow bands, 3) optimize topological economy of networks at individual level, 4) characterise information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (ibraindata.com/research/ifNN). AUTHOR SUMMARYIt is an essential mission for neuroscience to understand the individual differences in brain function. Graph or network theory offer novel methods of network neuroscience to address such a challenge. This article documents optimal strategies on the test-retest reliability of measuring individual differences in intrinsic brain networks of spontaneous activity. The analytical pipelines are identified to optimize for highly reliable, individualized network measurements. These pipelines optimize network metrics for high inter-individual variances and low inner-individual variances by defining network nodes with whole-brain parcellations, deriving the connectivity with spontaneous high-frequency slow-band oscillations, constructing brain graphs with topology-based methods for edge filtering, and favoring multi-level or multi-modal metrics. These psychometric findings are critical for translating the functional network neuroscience into clinical or other personalized practices requiring neuroimaging markers.

neuroscience↗

bioRxiv2021

Tracing Human Amygdala across School Age

The developmental pattern of the amygdala throughout childhood and adolescence has been inconsistently reported in previous neuroimaging studies. Given the relatively small size of the amygdala on full brain MRI scans, discrepancies may be partly due to methodological differences in amygdalar segmentation. To investigate the impact of volume extraction methods on amygdala volume, we compared FreeSurfer, FSL and volBrain segmentation measurements with those obtained by manual tracing. The manual tracing method, which we used as the gold standard, exhibited almost perfect intra- and inter-rater reliability. We observed systematic differences in amygdala volumes between automatic (FreeSurfer and volBrain) and manual methods. Specifically, compared with the manual tracing, FreeSurfer estimated larger amygdalae, and volBrain produced smaller amygdalae while FSL demonstrated a mixed pattern. The tracing bias was not uniform, but higher for smaller amygdalae. We further modeled amygdalar growth curves using accelerated longitudinal cohort data from the Chinese Color Nest Project. Trajectory modeling and statistical assessments of the manually traced amygdalae revealed linearly increasing and parallel developmental patterns for both girls and boys, although the amygdalae of boys were larger than those of girls. Compared to these trajectories, the shapes of developmental curves were similar when using the volBrain derived volumes. FreeSurfer derived trajectories had more nonlinearities and appeared flatter. FSL derived trajectories demonstrated an inverted U shape and were significantly different from those derived from manual tracing method. The use of amygdala volumes adjusted for total gray-matter volumes, but not intracranial volumes, resolved the shape discrepancies and led to reproducible growth curves curves between manual tracing and the automatic methods (except FSL). Our findings revealed steady growth of the human amygdala, mirroring its functional development across the school age. Methodological improvements are warranted for current automatic tools to achieve more accurate tracing of the amygdala at school age, calling for next generation tools.

neuroscience↗

bioRxiv2021

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Resting-state functional MRI (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10min of data generalized better than other approaches using 150min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity (RSFC) derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM (cMS-HBM) exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM (gMS-HBM) was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).

neuroscience↗

bioRxiv2020

Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence

The transition from childhood to adolescence is marked by pronounced shifts in brain structure and function that coincide with the development of physical, cognitive, and social abilities. Prior work in adult populations has characterized the topographical organization of cortex, revealing macroscale functional gradients that extend from unimodal (somato/motor and visual) regions through the cortical association areas that underpin complex cognition in humans. However, the presence of these core functional gradients across development as well as their maturational course have yet to be established. Here, leveraging 378 resting-state fMRI scans from 190 healthy individuals aged 6-17 years, we demonstrate that the transition from childhood to adolescence is reflected in the gradual maturation of gradient patterns across the cortical sheet. In children, the overarching organizational gradient is anchored within unimodal cortex, between somato/motor and visual territories. Conversely, in adolescence the principal gradient of connectivity transitions into an adult-like spatial framework, with the default network at the opposite end of a spectrum from primary sensory and motor regions. The observed gradient transitions are gradually refined with age, reaching a sharp inflection point in 13- and 14-year-olds. Functional maturation was nonuniformly distributed across cortical networks. Unimodal networks reached their mature positions early in development, while association regions, in particular medial prefrontal cortex, reached a later peak during adolescence. These data reveal age-dependent changes in the macroscale organization of cortex and suggest the scheduled maturation of functional gradient patterns may be critically important for understanding how cognitive and behavioral capabilities are refined across development. SignificanceHuman abilities and behavior change dramatically across development, emerging from a cascade of hierarchical changes in brain circuitry. Here, we describe age-dependent shifts in the macroscale functional organization of cortex in childhood and adolescence. The characterization of functional connectivity patterns in children revealed an overarching organizational framework anchored within unimodal cortex, between somato/motor and visual regions. Conversely, in adolescents we observed a transition into an adult-like gradient that situates the default network at the opposite end of a spectrum from primary sensory and motor regions. This spatial framework emerged gradually with age, reaching a sharp inflection point at the transition from childhood to adolescence. These data reveal the presence of a developmental change from a functional motif first dominated by the distinction between sensory and motor systems, and then balanced through interactions with later-maturing aspects of association cortex that support more abstract cognitive functions.

neuroscience↗

bioRxiv2020

DREAM: A Toolbox to Decode Rhythms of the Brain System

Rhythms of the brain are generated by neural oscillations across multiple frequencies, which can be observed with multiple modalities. Following the natural log linear law of frequency distribution, these oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. This perspective on neural oscillations has been increasingly applied to study human brain function and related behaviors. In practice, relevant signals are commonly measured as a discrete time series, and thus the sampling period and number of samples determine the number and ranges of decodable frequency intervals. However, these limits have been often ignored by researchers who instead decode measured oscillations into multiple frequency intervals using a fixed sample period and numbers of samples. One reason for such misuse is the lack of an easy-to-use toolbox to implement automatic decomposition of frequency intervals. We report on a toolbox with a graphical user interface for achieving local and remote decoding rhythms of the brain system (DREAM) which is accessible to the public via GitHub. We provide worked examples of DREAM used to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM analyzed the head motion oscillations and found that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age interval from 7 to 9 years. It is interesting that the higher frequency bands contains more head movements, and showed stronger age-motion associations but the weaker sex-motion interactions. Using the fast functional magnetic resonance imaging data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. A novel result indicated that the higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, these findings demonstrated the applicability of DREAM for frequency-specific human brain mapping as well as the assessments on their measurement reliability and validity.

neuroscience↗

bioRxiv2019

Functional connectome analyses reveal a highly optimized human olfactory network

The olfactory system is uniquely heterogeneous, performing multifaceted functions (beyond basic sensory processing) across diverse, widely distributed neural substrates. While knowledge of human olfaction continues to grow, it remains unclear how the olfactory network is organized to serve this unique set of functions. Leveraging a large and high-quality resting-state functional magnetic resonance imaging (rs-fMRI) dataset of nearly 900 participants from the Human Connectome Project (HCP), we identified a human olfactory network encompassing cortical and subcortical regions across the temporal and frontal lobes. Highlighting its reliability and generalizability, the connectivity matrix of this olfactory network mapped closely onto that extracted from an independent rs-fMRI dataset. Graph theoretical analysis further explicated the organizational principles of the network. The olfactory network exhibits a functionally advantageous modular composition of three (i.e., the sensory, limbic, and frontal) subnetworks and demonstrates strong small-world properties, high in both global integration and local segregation (i.e., circuit specialization). This network organization thus ensures the segregation of local circuits, which are nonetheless integrated via connecting hubs (i.e., amygdala and anterior insula), thereby enabling the specialized, yet integrative, functions of olfaction. In particular, the degree of local segregation positively predicted olfactory discrimination performance in the independent sample. In sum, an olfactory functional network has been identified through the large HCP dataset, affording a representative template of the human olfactory functional neuroanatomy. Importantly, the topological analysis of the olfactory network provides network-level insights into the remarkable functional specialization and spatial segregation of the olfactory system. Significance StatementOlfaction is an intriguing multifunctional system, playing key roles in regulating emotions, autonomic tone, and feeding, beyond basic sensory perception. However, it is unclear how the neuroanatomy of olfaction is organized in humans to subserve these functions. Functional connectivity analysis of the HCP dataset combined with graph theoretical analysis revealed an optimized large-scale network consisting of three subnetworks--the sensory, limbic, and frontal subnetworks. Distributed across frontal and temporal lobes in well segregated fashion, these olfactory structures are also highly integrated, linked through hub nodes of the amygdala and anterior insula. Our independent dataset replicated the HCP-derived olfactory network and, importantly, highlighted a direct association between the degree of network segregation and olfactory perception.

neuroscience↗

bioRxiv2019

Optimal Experimental Design for Big Data: Applications in Brain Imaging

Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations--such as measurement error--as compared to systematic deviations--such as individual differences--are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individuals samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error. Author SummaryIn recent decades, the size and complexity of data has grown exponentially. Unfortunately, the increased scale of modern datasets brings many new challenges. At present, we are in the midst of a replicability crisis, in which scientific discoveries fail to replicate to new datasets. Difficulties in the measurement procedure and measurement processing pipelines coupled with the influx of complex high-resolution measurements, we believe, are at the core of the replicability crisis. If measurements themselves are not replicable, what hope can we have that we will be able to use the measurements for replicable scientific findings? We introduce the "discriminability" statistic, which quantifies how discriminable measurements are from one another, without limitations on the structure of the underlying measurements. We prove that discriminable strategies tend to be strategies which provide better accuracy on downstream scientific questions. We demonstrate the utility of discriminability over competing approaches in this context on two disparate datasets from both neuroimaging and genomics. Together, we believe these results suggest the value of designing experimental protocols and analysis procedures which optimize the discriminability.

neuroscience↗

bioRxiv2019

Generating Templates and Growth Charts for School-Aged Brain Development

Standard brain templates and growth charts provide an invaluable resource for basic science research, with the eventual goal of contributing to the clinical care of neuropsychiatric conditions. Here, we report on a protocol to generate MRI brain templates in children and adolescents at one-year intervals from 6-to-18 years of age, with their corresponding growth charts, using a large-scale neuroimaging data resource (948 brain images from China and United States). To assure that the brain templates and growth charts are reliable and accurate, we developed a refined pipeline consisting of template construction, image registration, brain area labeling and growth chart modeling. The pipeline comprises multiple modular workflows that can be used for multiple applications. In our approach, population- and age-specific templates were first constructed to avoid systemic bias in registration. Brain areas were then labeled based on the appropriate templates, and their morphological metrics were extracted for modeling associated growth curves. We implemented warp cost as a function of age differences between individual brains and template brains. A strong U-shaped cost function was revealed, indicating larger age differences are associated with greater registration errors. This validates the necessity of age-specific reference templates in pediatric brain imaging studies. Growth chart analyses revealed preferential shape differences between Chinese and US samples in lateral frontal and parietal areas, aspects of cortex which are most variable across individuals with regard to structure and function as well as associated behavioral performance. This growth distinction is largely driven by neurodevelopmental differences between Chinese and US age-specific brain templates. The pipeline together with the brain templates and charts are publicly available and integrated into the Connectome Computation System.

neuroscience↗

bioRxiv2018

Network-based asymmetry of the human auditory system

Converging evidence from activation, connectivity and stimulation studies suggests that auditory brain networks are lateralized. Here we show that these findings can be at least partly explained by the asymmetric network embedding of the primary auditory cortices. Using diffusion-weighted imaging in three independent datasets, we investigate the propensity for left and right auditory cortex to communicate with other brain areas by quantifying the centrality of the auditory network across a spectrum of communication mechanisms, from shortest path communication to diffusive spreading. Across all datasets, we find that the right auditory cortex is better integrated in the connectome, facilitating more efficient communication with other areas, with much of the asymmetry driven by differences in communication pathways to the opposite hemisphere. Critically, the primacy of the right auditory cortex emerges only when communication is conceptualized as a diffusive process, taking advantage of more than just the topologically shortest paths in the network. Altogether, these results highlight how the network configuration and embedding of a particular region may contribute to its functional lateralization.

neuroscience↗