Author name cluster

Guray Erus

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15 papers

1 author row

YNICL Journal 2025 Journal Article

Spatial and signal features of white matter integrity and associations with clinical factors: A CARDIA brain MRI study

Faezeh Vedaei
Dhivya Srinivasan
Drew Parker
Guray Erus
Sudipto Dolui
Farzaneh A. Sorond
David R. Jacobs
Lenore J. Launer

White matter hyperintensities (WMH) may be indicative of age-related cerebrovascular diseases and contribute to cognitive and functional decline. Normal appearing WM (NAWM) adjacent to WMH, termed "penumbra," is known to be vulnerable to future WMH pathology. WM integrity can be evaluated using multiple magnetic resonance imaging (MRI) modalities. We aimed to identify MRI features predictive of WMH growth and to compare the implications of these features based on spatial proximity to existing WMH versus signal features in baseline NAWM. We used baseline and 5-year follow-up MRI scans in 485 middle-aged participants form the Coronary Artery Risk Development in Young Adults (CARDIA). Multimodal MRI at baseline, including fluid attenuated inversion recovery (FLAIR), diffusion tensor imaging (DTI), and cerebral blood flow (CBF), was measured within WM ROIs including baseline WMH and regions that later developed into new WMH, within and external to the baseline penumbra. Overall, we found that 80% of new WMH appeared within the baseline penumbra. We also found lower fractional anisotropy (FA) and CBF and higher FLAIR and median diffusivity (MD) in NAWM at baseline in regions with subsequent WMH growth compared to those without WMH growth. For NAWM regions defined by signal features, subthreshold FA and suprathreshold MD and FLAIR abnormality at baseline were the most robust predictors of WMH growth. Baseline systolic blood pressure had significant associations with baseline abnormalities in NAWM and subsequently with cognitive decline, particularly for FA and MD measures. The findings support the use of DTI as the predictor of WMH growth, which is correlated with subtle, adverse WM alterations and cognitive function years before developing to WMH. The results may contribute to future clinical trials aimed at preserving WM integrity.

YNIMG Journal 2023 Journal Article

Associations of baseline and longitudinal change in cerebellum volume with age-related changes in verbal learning and memory

C'iana P. Cooper
Andrea T. Shafer
Nicole M. Armstrong
Yang An
Guray Erus
Christos Davatzikos
Luigi Ferrucci
Peter R. Rapp

The cerebellum is involved in higher-order cognitive functions, e.g., learning and memory, and is susceptible to age-related atrophy. Yet, the cerebellum's role in age-related cognitive decline remains largely unknown. We investigated cross-sectional and longitudinal associations between cerebellar volume and verbal learning and memory. Linear mixed effects models and partial correlations were used to examine the relationship between changes in cerebellum volumes (total cerebellum, cerebellum white matter [WM], cerebellum hemisphere gray matter [GM], and cerebellum vermis subregions) and changes in verbal learning and memory performance among 549 Baltimore Longitudinal Study of Aging participants (2,292 visits). All models were adjusted by baseline demographic characteristics (age, sex, race, education), and APOE e4 carrier status. In examining associations between change with change, we tested an additional model that included either hippocampal (HC), cuneus, or postcentral gyrus (PoCG) volumes to assess whether cerebellar volumes were uniquely associated with verbal learning and memory. Cross-sectionally, the association of baseline cerebellum GM and WM with baseline verbal learning and memory was age-dependent, with the oldest individuals showing the strongest association between volume and performance. Baseline volume was not significantly associated with change in learning and memory. However, analysis of associations between change in volumes and changes in verbal learning and memory showed that greater declines in verbal memory were associated with greater volume loss in cerebellum white matter, and preserved GM volume in cerebellum vermis lobules VI-VII. The association between decline in verbal memory and decline in cerebellar WM volume remained after adjustment for HC, cuneus, and PoCG volume. Our findings highlight that associations between cerebellum volume and verbal learning and memory are age-dependent and regionally specific.

YNIMG Journal 2023 Journal Article

Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease

Jingxuan Bao
Junhao Wen
Zixuan Wen
Shu Yang
Yuhan Cui
Zhijian Yang
Guray Erus
Andrew J. Saykin

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.

YNIMG Journal 2023 Journal Article

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

Zhen Zhou
Hongming Li
Dhivya Srinivasan
Ahmed Abdulkadir
Ilya M. Nasrallah
Junhao Wen
Jimit Doshi
Guray Erus

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

YNIMG Journal 2020 Journal Article

A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies

Dhivya Srinivasan
Guray Erus
Jimit Doshi
David A. Wolk
Haochang Shou
Mohamad Habes
Christos Davatzikos

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.

YNIMG Journal 2020 Journal Article

Associations between cognitive and brain volume changes in cognitively normal older adults

Nicole M. Armstrong
Yang An
John J. Shin
Owen A. Williams
Jimit Doshi
Guray Erus
Christos Davatzikos
Luigi Ferrucci

Investigation of relationships between age-related changes in regional brain volumes and changes in domain-specific cognition could provide insights into the neural underpinnings of individual differences in cognitive aging. Domain-specific cognition (memory, verbal fluency, visuospatial ability) and tests of executive function and attention (Trail-Making Test Part A and B) and 47 brain volumes of interest (VOIs) were assessed in 836 Baltimore Longitudinal Study of Aging participants with mean follow-up of 4.1 years (maximum 23.1 years). To examine the correlation between changes in domain-specific cognition and changes in brain volumes, we used bivariate linear mixed effects models with unstructured variance-covariance structure to estimate longitudinal trajectories for each variable of interest and correlations among the random effects of these measures. Higher annual rates of memory decline were associated with greater volume loss in 14 VOIs primarily within the temporal and occipital lobes. Verbal fluency decline was associated with greater ventricular enlargement and volume loss in 24 VOIs within the frontal, temporal, and parietal lobes. Decline in visuospatial ability was associated with volume loss in 3 temporal and parietal VOIs. Declines on the attentional test were associated with volume loss in 4 VOIs located within temporal and parietal lobes. Greater declines on the executive function test were associated with greater ventricular enlargement and volume loss in 10 frontal, parietal, and temporal VOIs. Our findings highlight domain-specific patterns of regional brain atrophy that may contribute to individual differences in cognitive aging.

YNIMG Journal 2020 Journal Article

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Siddhesh Thakur
Jimit Doshi
Sarthak Pati
Saima Rathore
Chiharu Sako
Michel Bilello
Sung Min Ha
Gaurav Shukla

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n = 3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach 1 1 Publicly available source code: https: //github. com/CBICA/BrainMaGe obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

YNIMG Journal 2020 Journal Article

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

Raymond Pomponio
Guray Erus
Mohamad Habes
Jimit Doshi
Dhivya Srinivasan
Elizabeth Mamourian
Vishnu Bashyam
Ilya M. Nasrallah

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10, 477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3–96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

YNICL Journal 2019 Journal Article

Sex differences in the association between amyloid and longitudinal brain volume change in cognitively normal older adults

Nicole M. Armstrong
Chiung-Wei Huang
Owen A. Williams
Murat Bilgel
Yang An
Jimit Doshi
Guray Erus
Christos Davatzikos

OBJECTIVE: Amyloid positivity is a biomarker of AD pathology, yet the associations between amyloid positivity and brain volumetric changes, especially in the hippocampus, are inconsistent. We hypothesize that sex differences in associations may contribute to inconsistent findings among cognitively normal older adults. METHODS: C-Pittsburgh Compound B (PiB) distribution volume ratio (DVR) cut-off of 1.062. All analyses included age, race, sex, education, APOE e4 carrier status, and two-way interactions of these covariates with time. Two-way interaction between sex and PiB+/- status and three-way interaction of sex and PiB+/- status with time were added to assess whether sex modified associations. RESULTS: PiB+ status was associated with greater volumetric declines in the phg (β = -0.036, SE = 0.011, p = 0.001) and erc (β = -0.019, SE = 0.009, p = 0.045). Sex modified the association of PiB+ status and rates of volumetric declines in fusiform (β = -0.117, SE = 0.049, p = 0.019). PiB+ males had steeper rates of volumetric declines in phg (β = -0.051, SE = 0.013, p < 0.001) and erc (β = -0.029, SE = 0.012, p = 0.014) than PiB- males, while there was no difference in rates of volumetric change between PiB+ and PiB- females. CONCLUSIONS: Amyloidosis is a marker of entorhinal and parahippocampal volume loss. Amyloid positivity is a predictor of volume loss in brain regions affected by early AD pathology in men, but not women.

YNIMG Journal 2018 Journal Article

Longitudinally and inter-site consistent multi-atlas based parcellation of brain anatomy using harmonized atlases

Guray Erus
Jimit Doshi
Yang An
Dimitris Verganelakis
Susan M. Resnick
Christos Davatzikos

As longitudinal and multi-site studies become increasingly frequent in neuroimaging, maintaining longitudinal and inter-scanner consistency of brain parcellation has become a major challenge due to variation in scanner models and/or image acquisition protocols across scanners and sites. We present a new automated segmentation method specifically designed to achieve a consistent parcellation of anatomical brain structures in such heterogeneous datasets. Our method combines a site-specific atlas creation strategy with a state-of-the-art multi-atlas anatomical label fusion framework. Site-specific atlases are computed such that they preserve image intensity characteristics of each site's scanner and acquisition protocol, while atlas pairs share anatomical labels in a way consistent with inter-scanner acquisition variations. This harmonization of atlases improves inter-study and longitudinal consistency of segmentations in the subsequent consensus labeling step. We tested this approach on a large sample of older adults from the Baltimore Longitudinal Study of Aging (BLSA) who had longitudinal scans acquired using two scanners that vary with respect to vendor and image acquisition protocol. We compared the proposed method to standard multi-atlas segmentation for both cross-sectional and longitudinal analyses. The harmonization significantly reduced scanner-related differences in the age trends of ROI volumes, improved longitudinal consistency of segmentations, and resulted in higher across-scanner intra-class correlations, particularly in the white matter.

YNICL Journal 2018 Journal Article

White matter microstructure, white matter lesions, and hypertension: An examination of early surrogate markers of vascular-related brain change in midlife

Thaddeus Haight
R. Nick Bryan
Guray Erus
Meng-Kang Hsieh
Christos Davatzikos
Ilya Nasrallah
Mark D'Esposito
David R. Jacobs

Objective: We examined imaging surrogates of white matter microstructural abnormalities which may precede white matter lesions (WML) and represent a relevant marker of cerebrovascular injury in adults in midlife. Methods: In 698 community-dwelling adults (mean age 50 years ±3.5 SD) from the Coronary Artery Risk Development in Young Adults (CARDIA) Brain MRI sub-study, WML were identified on structural MR and fractional anisotropy (FA), representing WM microstructural integrity, was derived using Diffusion Tensor Imaging. FA and WML maps were overlaid on a parcellated T1-template, based on an expert-delineated brain atlas, which included 42 WM tract ROIs. Analyses occurred in stages: 1) WML were quantified for the different tracts (i.e., frequency, volume, volume relative to tract size); 2) the interdependence of FA in normal appearing WM (NAWM) and WML was examined across tracts; 3) associations of NAWM FA and hypertension status were assessed controlling for WML volume. In the latter analysis, both overall hypertension (i.e. hypertension vs. normotension and prehypertension vs. normotension) and hypertension categorized by antihypertensive treatment status (yes/no) and blood pressure control (e.g., diastolic <90 mmHg, systolic <140 mmHg), were assessed. Results: WML were widely distributed across different WM tracts, however, WML volume was small. Mean NAWM FA was lower in participants with vs. participants without WML in given tracts. Hypertension was significantly associated with lower mean NAWM FA globally across tracts, both before and after adjustment for WML volume. Moreover, the magnitude of this association differed by treatment status and the level of control of the hypertension. Conclusions: In middle-aged adults, NAWM FA could represent a relevant marker of cerebrovascular injury when WML are minimally present.

YNIMG Journal 2016 Journal Article

Capturing heterogeneous group differences using mixture-of-experts: Application to a study of aging

Harini Eavani
Meng Kang Hsieh
Yang An
Guray Erus
Lori Beason-Held
Susan Resnick
Christos Davatzikos

In MRI studies, linear multi-variate methods are often employed to identify regions or connections that are affected due to disease or normal aging. Such linear models inherently assume that there is a single, homogeneous abnormality pattern that is present in all affected individuals. While kernel-based methods can implicitly model a non-linear effect, and therefore the heterogeneity in the affected group, extracting and interpreting information about affected regions is difficult. In this paper, we present a method that explicitly models and captures heterogeneous patterns of change in the affected group relative to a reference group of controls. For this purpose, we use the Mixture-of-Experts (MOE) framework, which combines unsupervised modeling of mixtures of distributions with supervised learning of classifiers. MOE approximates the non-linear boundary between the two groups with a piece-wise linear boundary, thus allowing discovery of multiple patterns of group differences. In the case of patient/control comparisons, each such pattern aims to capture a different dimension of a disease, and hence to identify patient subgroups. We validated our model using multiple simulation scenarios and performance measures. We applied this method to resting state functional MRI data from the Baltimore Longitudinal Study of Aging, to investigate heterogeneous effects of aging on brain function in cognitively normal older adults (>85years) relative to a reference group of normal young to middle-aged adults (<60years). We found strong evidence for the presence of two subgroups of older adults, with similar age distributions in each subgroup, but different connectivity patterns associated with aging. While both older subgroups showed reduced functional connectivity in the Default Mode Network (DMN), increases in functional connectivity within the pre-frontal cortex as well as the bilateral insula were observed only for one of the two subgroups. Interestingly, the subgroup showing this increased connectivity (unlike the other subgroup) was, cognitively similar at baseline to the young and middle-aged subjects in two of seven cognitive domains, and had a faster rate of cognitive decline in one of seven domains. These results suggest that older individuals whose baseline cognitive performance is comparable to that of younger individuals recruit their “cognitive reserve” later in life, to compensate for reduced connectivity in other brain regions.

YNIMG Journal 2016 Journal Article

MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection

Jimit Doshi
Guray Erus
Yangming Ou
Susan M. Resnick
Ruben C. Gur
Raquel E. Gur
Theodore D. Satterthwaite
Susan Furth

Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https: //ipp. cbica. upenn. edu), a web based platform for remote processing of medical images.

YNIMG Journal 2015 Journal Article

Vascular risk factors, cerebrovascular reactivity, and the default-mode brain network

Thaddeus J. Haight
R. Nick Bryan
Guray Erus
Christos Davatzikos
David R. Jacobs
Mark D'Esposito
Cora E. Lewis
Lenore J. Launer

Cumulating evidence from epidemiologic studies implicates cardiovascular health and cerebrovascular function in several brain diseases in late life. We examined vascular risk factors with respect to a cerebrovascular measure of brain functioning in subjects in mid-life, which could represent a marker of brain changes in later life. Breath-hold functional MRI (fMRI) was performed in 541 women and men (mean age 50. 4years) from the Coronary Artery Risk Development in Young Adults (CARDIA) Brain MRI sub-study. Cerebrovascular reactivity (CVR) was quantified as percentage change in blood-oxygen level dependent (BOLD) signal in activated voxels, which was mapped to a common brain template and log-transformed. Mean CVR was calculated for anatomic regions underlying the default-mode network (DMN) – a network implicated in AD and other brain disorders – in addition to areas considered to be relatively spared in the disease (e. g. occipital lobe), which were utilized as reference regions. Mean CVR was significantly reduced in the posterior cingulate/precuneus (β=−0. 063, 95% CI: −0. 106, −0. 020), anterior cingulate (β=−0. 055, 95% CI: −0. 101, −0. 010), and medial frontal lobe (β=−0. 050, 95% CI: −0. 092, −0. 008) relative to mean CVR in the occipital lobe, after adjustment for age, sex, race, education, and smoking status, in subjects with pre-hypertension/hypertension compared to normotensive subjects. By contrast, mean CVR was lower, but not significantly, in the inferior parietal lobe (β=−0. 024, 95% CI: −0. 062, 0. 014) and the hippocampus (β=−0. 006, 95% CI: −0. 062, 0. 050) relative to mean CVR in the occipital lobe. Similar results were observed in subjects with diabetes and dyslipidemia compared to those without these conditions, though the differences were non-significant. Reduced CVR may represent diminished vascular functionality for the DMN for individuals with prehypertension/hypertension in mid-life, and may serve as a preclinical marker for brain dysfunction in later life.

YNIMG Journal 2013 Journal Article

Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth

Theodore D. Satterthwaite
Daniel H. Wolf
Kosha Ruparel
Guray Erus
Mark A. Elliott
Simon B. Eickhoff
Efstathios D. Gennatas
Chad Jackson

Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8–22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group levels. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased within-module connectivity occurring with development. Finally, we show that subject’s age can still be accurately estimated from the multivariate pattern of functional connectivity even while controlling for motion. Taken together, these results indicate that while motion artifact has a marked and heterogeneous impact on estimates of connectivity change during adolescence, functional connectivity remains a valuable phenotype for the study of neurodevelopment.