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Resting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity (FC) of traumatic brain injury (TBI) patients. We applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 TBI patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial. Pipelines were evaluated by three quality control (QC) metrics across three exclusion regimes based on the participant's head movement profile. While no pipeline eliminated noise effects on FC, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data-driven methods performed comparatively worse. In this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related FC in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as TBI datasets.
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Lesiones Traumáticas del Encéfalo , Procesamiento de Imagen Asistido por Computador , Artefactos , Lesiones Traumáticas del Encéfalo/diagnóstico por imagen , Ensayos Clínicos como Asunto , Movimientos de la Cabeza , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Espectroscopía de Resonancia MagnéticaRESUMEN
Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing-on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.
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Post-stroke behavioural symptoms often correlate and systematically co-occur with each other, either because they share cognitive processes, or because their neural correlates are often damaged together. Thus, neuropsychological symptoms often share variance. Many previous lesion-behaviour mapping studies aimed to methodologically consider this shared variance between neuropsychological variables. A first group of studies controlled the behavioural target variable for the variance explained by one or multiple other variables to obtain a more precise mapping of the target variable. A second group of studies focused on the shared variance of multiple variables itself with the aim to map neural correlates of cognitive processes that are shared between the original variables. In the present study, we tested the validity of these methods by using real lesion data and both real and simulated data sets. We show that the variance that is shared between post-stroke behavioural variables is ambiguous, and that mapping procedures that consider this variance are prone to biases and artefacts. We discuss under which conditions such procedures could still be used and what alternative approaches exist.
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Síntomas Conductuales/diagnóstico por imagen , Mapeo Encefálico/métodos , Disfunción Cognitiva/diagnóstico por imagen , Accidente Cerebrovascular/diagnóstico por imagen , Anciano , Artefactos , Síntomas Conductuales/etiología , Síntomas Conductuales/psicología , Disfunción Cognitiva/etiología , Disfunción Cognitiva/psicología , Simulación por Computador , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Pruebas Neuropsicológicas , Análisis de Componente Principal , Reproducibilidad de los Resultados , Estudios Retrospectivos , Accidente Cerebrovascular/complicaciones , Accidente Cerebrovascular/psicologíaRESUMEN
For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. -55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.
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Neuroimagen Funcional/normas , Modelos Estadísticos , Espectroscopía Infrarroja Corta/normas , Adulto , Artefactos , Femenino , Neuroimagen Funcional/métodos , Humanos , Modelos Lineales , Masculino , Espectroscopía Infrarroja Corta/métodos , Adulto JovenRESUMEN
In resting-state functional MRI, the correlation between blood-oxygenation-level-dependent (BOLD) signals across brain regions is used to estimate the functional connectivity (FC) of the brain. FC estimates are prone to the influence of nuisance factors including scanner-related artifacts and physiological modulations of the BOLD signal. Nuisance regression is widely performed to reduce the effect of nuisance factors on FC estimates on a per-scan basis. However, a dedicated analysis of nuisance effects on the variability of FC metrics across a collection of scans has been lacking. This work investigates the effects of nuisance factors on the variability of FC estimates across a collection of scans both before and after nuisance regression. Inter-scan variations in FC estimates are shown to be significantly correlated with the geometric norms of various nuisance terms, including head motion measurements, signals derived from white-matter and cerebrospinal regions, and the whole-brain global signal (GS) both before and after nuisance regression. In addition, it is shown that GS regression (GSR) can introduce GS norm-related fluctuations that are negatively correlated with inter-scan FC estimates. The empirical results are shown to be largely consistent with the predictions of a theoretical framework previously developed for the characterization of dynamic FC measures. This work shows that caution must be exercised when interpreting inter-scan FC measures across scans both before and after nuisance regression.
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Encéfalo/diagnóstico por imagen , Conectoma/normas , Procesamiento de Imagen Asistido por Computador/normas , Imagen por Resonancia Magnética/normas , Adulto , Artefactos , Conectoma/métodos , Movimientos de la Cabeza , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Adulto JovenRESUMEN
Resting-state functional magnetic resonance imaging (rs-fMRI) based on the blood-oxygen-level-dependent (BOLD) signal has been widely used in healthy individuals and patients to investigate brain functions when the subjects are in a resting or task-negative state. Head motion considerably confounds the interpretation of rs-fMRI data. Nuisance regression is commonly used to reduce motion-related artifacts with six motion parameters estimated from rigid-body realignment as regressors. To further compensate for the effect of head movement, the first-order temporal derivatives of motion parameters and squared motion parameters were proposed previously as possible motion regressors. However, these additional regressors may not be sufficient to model the impact of head motion because of the complexity of motion artifacts. In addition, while using more motion-related regressors could explain more variance in the data, the neural signal may also be removed with increasing number of motion regressors. To better model how in-scanner motion affects rs-fMRI data, a robust and automated convolutional neural network (CNN) model is developed in this study to obtain optimal motion regressors. The CNN network consists of two temporal convolutional layers and the output from the network are the derived motion regressors used in the following nuisance regression. The temporal convolutional layer in the network can non-parametrically model the prolonged effect of head motion. The set of regressors derived from the neural network is compared with the same number of regressors used in a traditional nuisance regression approach. It is demonstrated that the CNN-derived regressors can more effectively reduce motion-related artifacts.
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This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data-driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal-to-noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal-noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting-state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR-corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.
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Artefactos , Encéfalo/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Mapeo Encefálico/métodos , Líquido Cefalorraquídeo , Humanos , Sustancia BlancaRESUMEN
Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility to assess brain function independent of explicit tasks and individual performance. This absence of explicit stimuli in rs-fMRI makes analyses more susceptible to nonneural signal fluctuations than task-based fMRI. Data preprocessing is a critical procedure to minimise contamination by artefacts related to motion and physiology. We herein investigate the effects of different preprocessing strategies on the amplitude of low-frequency fluctuations (ALFFs) and its fractional counterpart, fractional ALFF (fALFF). Sixteen artefact reduction schemes based on nuisance regression are applied to data from 82 subjects acquired at 1.5 T, 30 subjects at 3 T, and 23 subjects at 7 T, respectively. In addition, we examine test-retest variance and effects of bias correction. In total, 569 data sets are included in this study. Our results show that full artefact reduction reduced test-retest variance by up to 50%. Polynomial detrending of rs-fMRI data has a positive effect on group-level t-values for ALFF but, importantly, a negative effect for fALFF. We show that the normalisation process intrinsic to fALFF calculation causes the observed reduction and introduce a novel measure for low-frequency fluctuations denoted as high-frequency ALFF (hfALFF). We demonstrate that hfALFF values are not affected by the negative detrending effects seen in fALFF data. Still, highest grey matter (GM) group-level t-values were obtained for fALFF data without detrending, even when compared to an exploratory detrending approach based on autocorrelation measures. From our results, we recommend the use of full nuisance regression including polynomial detrending in ALFF data, but to refrain from using polynomial detrending in fALFF data. Such optimised preprocessing increases GM group-level t-values by up to 60%.
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Mapeo Encefálico/métodos , Encéfalo/diagnóstico por imagen , Encéfalo/fisiología , Imagen por Resonancia Magnética/métodos , Adulto , Anciano , Artefactos , Procesamiento Automatizado de Datos , Femenino , Análisis de Fourier , Sustancia Gris/diagnóstico por imagen , Sustancia Gris/fisiología , Humanos , Procesamiento de Imagen Asistido por Computador , Masculino , Persona de Mediana Edad , Análisis de Regresión , Descanso , Adulto JovenRESUMEN
In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between seed-specific sliding window correlation-based DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors. Strong correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not necessarily eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed between the DFC estimates and nuisance norms even after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.
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Mapeo Encefálico/métodos , Encéfalo/fisiología , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética , Artefactos , Femenino , Humanos , Masculino , Análisis de Regresión , Reproducibilidad de los ResultadosRESUMEN
BACKGROUND: Physiological noise reduction plays a critical role in spinal cord (SC) resting-state fMRI (rsfMRI). PURPOSE: To reduce physiological noise and increase the robustness of SC rsfMRI by using an independent component analysis (ICA)-based nuisance regression (ICANR) method. STUDY TYPE: Retrospective. SUBJECTS: Ten healthy subjects (female/male = 4/6, age = 27 ± 3 years, range 24-34 years). FIELD STRENGTH/SEQUENCE: 3T/gradient-echo echo planar imaging (EPI). ASSESSMENT: We used three alternative methods (no regression [Nil], conventional region of interest [ROI]-based noise reduction method without ICA [ROI-based], and correction of structured noise using spatial independent component analysis [CORSICA]) to compare with the performance of ICANR. Reduction of the influence of physiological noise on the SC and the reproducibility of rsfMRI analysis after noise reduction were examined. The correlation coefficient (CC) was calculated to assess the influence of physiological noise. Reproducibility was calculated by intraclass correlation (ICC). STATISTICAL TESTS: Results from different methods were compared by one-way analysis of variance (ANOVA) with post-hoc analysis. RESULTS: No significant difference in cerebrospinal fluid (CSF) pulsation influence or tissue motion influence were found (P = 0.223 in CSF, P = 0.2461 in tissue motion) in the ROI-based (CSF: 0.122 ± 0.020; tissue motion: 0.112 ± 0.015), and Nil (CSF: 0.134 ± 0.026; tissue motion: 0.124 ± 0.019). CORSICA showed a significantly stronger influence of CSF pulsation and tissue motion (CSF: 0.166 ± 0.045, P = 0.048; tissue motion: 0.160 ± 0.032, P = 0.048) than Nil. ICANR showed a significantly weaker influence of CSF pulsation and tissue motion (CSF: 0.076 ± 0.007, P = 0.0003; tissue motion: 0.081 ± 0.014, P = 0.0182) than Nil. The ICC values in the Nil, ROI-based, CORSICA, and ICANR were 0.669, 0.645, 0.561, and 0.766, respectively. DATA CONCLUSION: ICANR more effectively reduced physiological noise from both tissue motion and CSF pulsation than three alternative methods. ICANR increases the robustness of SC rsfMRI in comparison with the other three methods. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1421-1431.
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Imagen Eco-Planar , Imagen por Resonancia Magnética , Médula Espinal/diagnóstico por imagen , Adulto , Artefactos , Encéfalo/diagnóstico por imagen , Mapeo Encefálico , Femenino , Sustancia Gris/diagnóstico por imagen , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Masculino , Análisis de Componente Principal , Análisis de Regresión , Reproducibilidad de los Resultados , Estudios RetrospectivosRESUMEN
Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre-processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (www.cam-can.com). This dataset contained two sessions of resting-state fMRI from 214 adults aged 18-88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between-participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high-pass filtering, instead of band-pass filtering, produced stronger and more reliable age-effects. Head motion was correlated with gray-matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125-4156, 2017. © 2017 Wiley Periodicals, Inc.
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Encéfalo/diagnóstico por imagen , Encéfalo/fisiología , Envejecimiento Saludable/fisiología , Imagen por Resonancia Magnética , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Variación Biológica Poblacional , Encéfalo/irrigación sanguínea , Mapeo Encefálico/métodos , Femenino , Sustancia Gris/irrigación sanguínea , Sustancia Gris/diagnóstico por imagen , Sustancia Gris/fisiología , Movimientos de la Cabeza , Humanos , Imagen por Resonancia Magnética/métodos , Masculino , Persona de Mediana Edad , Análisis Multivariante , Vías Nerviosas/irrigación sanguínea , Vías Nerviosas/diagnóstico por imagen , Vías Nerviosas/fisiología , Tamaño de los Órganos , Análisis de Regresión , Reproducibilidad de los Resultados , Descanso , Adulto JovenRESUMEN
Recently, emerging studies have demonstrated the existence of brain resting-state spontaneous activity at frequencies higher than the conventional 0.1 Hz. A few groups utilizing accelerated acquisitions have reported persisting signals beyond 1 Hz, which seems too high to be accommodated by the sluggish hemodynamic process underpinning blood oxygen level-dependent contrasts (the upper limit of the canonical model is â¼0.3 Hz). It is thus questionable whether the observed high-frequency (HF) functional connectivity originates from alternative mechanisms (e.g., inflow effects, proton density changes in or near activated neural tissue) or rather is artificially introduced by improper preprocessing operations. In this study, we examined the influence of a common preprocessing step-whole-band linear nuisance regression (WB-LNR)-on resting-state functional connectivity (RSFC) and demonstrated through both simulation and analysis of real dataset that WB-LNR can introduce spurious network structures into the HF bands of functional magnetic resonance imaging (fMRI) signals. Findings of present study call into question whether published observations on HF-RSFC are partly attributable to improper data preprocessing instead of actual neural activities.
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Artefactos , Mapeo Encefálico , Encéfalo/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador , Imagen por Resonancia Magnética , Adulto , Mapeo Encefálico/métodos , Simulación por Computador , Conjuntos de Datos como Asunto , Femenino , Humanos , Masculino , Persona de Mediana Edad , Redes Neurales de la Computación , Vías Nerviosas/diagnóstico por imagen , Oxígeno/sangre , Relación Señal-Ruido , Análisis Espectral , Adulto JovenRESUMEN
In resting state fMRI, it is necessary to remove signal variance associated with noise sources, leaving cleaned fMRI time-series that more accurately reflect the underlying intrinsic brain fluctuations of interest. This is commonly achieved through nuisance regression, in which the fit is calculated of a noise model of head motion and physiological processes to the fMRI data in a General Linear Model, and the "cleaned" residuals of this fit are used in further analysis. We examine the statistical assumptions and requirements of the General Linear Model, and whether these are met during nuisance regression of resting state fMRI data. Using toy examples and real data we show how pre-whitening, temporal filtering and temporal shifting of regressors impact model fit. Based on our own observations, existing literature, and statistical theory, we make the following recommendations when employing nuisance regression: pre-whitening should be applied to achieve valid statistical inference of the noise model fit parameters; temporal filtering should be incorporated into the noise model to best account for changes in degrees of freedom; temporal shifting of regressors, although merited, should be achieved via optimisation and validation of a single temporal shift. We encourage all readers to make simple, practical changes to their fMRI denoising pipeline, and to regularly assess the appropriateness of the noise model used. By negotiating the potential pitfalls described in this paper, and by clearly reporting the details of nuisance regression in future manuscripts, we hope that the field will achieve more accurate and precise noise models for cleaning the resting state fMRI time-series.
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Neuroimagen Funcional/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Adulto , Femenino , Humanos , Masculino , Adulto JovenRESUMEN
Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of "scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor.
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Artefactos , Mapeo Encefálico/métodos , Encéfalo/fisiología , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Imagen por Resonancia Magnética/métodos , Movimiento , Algoritmos , Niño , Interpretación Estadística de Datos , Humanos , Masculino , Movimiento (Física) , Análisis de Componente Principal , Reproducibilidad de los Resultados , Descanso/fisiología , Sensibilidad y Especificidad , Programas InformáticosRESUMEN
Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent reintroduction of nuisance-related variation into frequencies previously suppressed by the bandpass filter, as well as suboptimal correction for noise signals in the frequencies of interest. This is important because many RS-fcMRI studies, including some focusing on motion-related artifacts, have applied this approach. In two cohorts of individuals (n=117 and 22) who completed resting-state fMRI scans, we found that the bandpass-regress approach consistently overestimated functional connectivity across the brain, typically on the order of r=.10-.35, relative to a simultaneous bandpass filtering and nuisance regression approach. Inflated correlations under the bandpass-regress approach were associated with head motion and cardiac artifacts. Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach. We recommend that future RS-fcMRI studies ensure that the frequencies of nuisance regressors and fMRI data match prior to nuisance regression, and we advocate a simultaneous bandpass filtering and nuisance regression strategy that better controls nuisance-related variability.