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Nathan W. Churchill

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12 papers

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YNICL Journal 2025 Journal Article

Multi-voxel pattern analysis for characterizing functional connectivity and neurocognitive function in major depression: A CAN-BIND-1 report

Alice Rueda
Ilya Demchenko
Vanessa K. Tassone
Fatemeh Gholamali Nezhad
Vanessa Peters
Nathan W. Churchill
Benicio N. Frey
Stefanie Hassel

BACKGROUND: Major depressive disorder (MDD) affects not only mood but also neurocognitive function. In this study, we used whole-brain functional connectivity multi-voxel pattern analysis (fc-MVPA) to examine the relationship between resting-state functional connectivity (rsFC) and neurocognitive function in individuals with MDD compared to healthy controls (HC). METHODS: Baseline functional magnetic resonance imaging (fMRI) scans from the CAN-BIND-1 dataset were analyzed using a data-driven whole-brain fc-MVPA approach in 147 individuals with MDD and 98 HC. All participants completed the Computerized Neurocognitive Assessment Vital Signs (CNS-VS) battery outside the scanner, and correlations between rsFC differences and CNS-VS domain scores were explored. RESULTS: The fc-MVPA reduced the dimensionality of fMRI data at both individual and group levels, identifying six clusters with altered rsFC in MDD relative to HC: left cerebellar crus I, right precuneus, left superior lateral occipital cortex, right ventral caudate, left superior parietal lobule, and left dorsal anterior cingulate cortex. Using these clusters as seeds, post-hoc analyses identified 24 patterns of altered rsFC in MDD involving the default mode, central executive, visual recognition, salience, and sensorimotor networks. Five of these patterns showed significant correlations with CNS-VS domain scores for composite memory, neurocognition index, processing speed, executive function, and simple attention in HC, but these associations were absent in individuals with MDD. CONCLUSIONS: Our findings highlight that MDD is associated with disrupted rsFC across networks relevant to neurocognitive function. The data-driven nature of the fc-MVPA identified the left cerebellar crus I as the most significant region of aberrant rsFC.

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YNIMG Journal 2020 Journal Article

Distinguishing cognitive effort and working memory load using scale-invariance and alpha suppression in EEG

Omid Kardan
Kirsten C.S. Adam
Irida Mance
Nathan W. Churchill
Edward K. Vogel
Marc G. Berman

Despite being intuitive, cognitive effort has proven difficult to define quantitatively. Here, we proposed to study cognitive effort by investigating the degree to which the brain deviates from its default state, where brain activity is scale-invariant. Specifically, we measured such deviations by examining changes in scale-invariance of brain activity as a function of task difficulty and posited suppression of scale-invariance as a proxy for exertion of cognitive effort. While there is some fMRI evidence supporting this proposition, EEG investigations on the matter are scant, despite the EEG signal being more suitable for analysis of scale invariance (i. e. , having a much broader frequency range). In the current study we validated the correspondence between scale-invariance (H) of cortical activity recorded by EEG and task load during two working memory (WM) experiments with varying set sizes. Then, we used this neural signature to disentangle cognitive effort from the number of items stored in WM within participants. Our results showed monotonic decreases in H with increased set size, even after set size exceeded WM capacity. This behavior of H contrasted with behavioral performance and an oscillatory indicator of WM load (i. e. , alpha-band desynchronization), both of which showed a plateau at difficulty levels surpassing WM capacity. This is the first reported evidence for the suppression of scale-invariance in EEG due to task difficulty, and our work suggests that H suppression may be used to quantify changes in cognitive effort even when working memory load is at maximum capacity.

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YNICL Journal 2020 Journal Article

Neurometabolites and sport-related concussion: From acute injury to one year after medical clearance

Nathan W. Churchill
Michael G. Hutchison
Simon J. Graham
Tom A. Schweizer

Sport-related concussion is associated with acute disturbances in neurometabolic function, with effects that may last weeks to months after injury. However, is presently unknown whether these disturbances resolve at medical clearance to return to play (RTP) or continue to evolve over longer time intervals. Moreover, little is known about how these neurometabolic changes correlate with other measures of brain physiology. In this study, these gaps were addressed by evaluating ninety-nine (99) university-level athletes, including 33 with sport-related concussion and 66 without recent injury, using multi-parameter magnetic resonance imaging (MRI), which included single-voxel spectroscopy (SVS), diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). The concussed athletes were scanned at the acute phase of injury (27/33 imaged), medical clearance to RTP (25/33 imaged), one month post-RTP (25/33 imaged) and one year post-RTP (13/33 imaged). We measured longitudinal changes in N-acetyl aspartate (NAA) and myo-inositol (Ins), over the course of concussion recovery. Concussed athletes showed no significant abnormalities or longitudinal change in NAA values, whereas Ins was significantly elevated at RTP and one month later. Interestingly, Ins response was attenuated by a prior history of concussion. Subsequent analyses identified significant associations between Ins values, DTI measures of white matter microstructure and fMRI measures of functional connectivity. These associations varied over the course of concussion recovery, suggesting that elevated Ins values at RTP and beyond reflect distinct changes in brain physiology, compared to acute injury. These findings provide novel information about neurometabolic recovery after a sport-related concussion, with evidence of disturbances that persist beyond medical clearance to RTP.

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YNICL Journal 2019 Journal Article

Brain connectivity tracks effects of chemotherapy separately from behavioral measures

Omid Kardan
Patricia A. Reuter-Lorenz
Scott Peltier
Nathan W. Churchill
Bratislav Misic
Mary K. Askren
Mi Sook Jung
Bernadine Cimprich

Several studies in cancer research have suggested that cognitive dysfunction following chemotherapy, referred to in lay terms as "chemobrain", is a serious problem. At present, the changes in integrative brain function that underlie such dysfunction remain poorly understood. Recent developments in neuroimaging suggest that patterns of functional connectivity can provide a broadly applicable neuromarker of cognitive performance and other psychometric measures. The current study used multivariate analysis methods to identify patterns of disruption in resting state functional connectivity of the brain due to chemotherapy and the degree to which the disruptions can be linked to behavioral measures of distress and cognitive performance. Sixty two women (22 healthy control, 18 patients treated with adjuvant chemotherapy, and 22 treated without chemotherapy) were evaluated with neurocognitive measures followed by self-report questionnaires and open eyes resting-state fMRI scanning at three time points: diagnosis (M0, pre-adjuvant treatment), 1 month (M1), and 7 months (M7) after treatment. The results indicated deficits in cognitive health of breast cancer patients immediately after chemotherapy that improved over time. This psychological trajectory was paralleled by a disruption and later recovery of resting-state functional connectivity, mostly in the parietal and frontal brain regions. Mediation analysis showed that the functional connectivity alteration pattern is a separable treatment symptom from the decreased cognitive health. Current study indicates that more targeted support for patients should be developed to ameliorate these multi-faceted side effects of chemotherapy treatment on neural functioning and cognitive health.

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YNICL Journal 2018 Journal Article

Connectomic markers of symptom severity in sport-related concussion: Whole-brain analysis of resting-state fMRI

Nathan W. Churchill
Michael G. Hutchison
Simon J. Graham
Tom A. Schweizer

Concussion is associated with significant adverse effects within the first week post-injury, including physical complaints and altered cognition, sleep and mood. It is currently unknown whether these subjective disturbances have reliable functional brain correlates. Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to measure functional connectivity of individuals after traumatic brain injury, but less is known about the relationship between functional connectivity and symptom assessments after a sport concussion. In this study, rs-fMRI was used to evaluate whole-brain functional connectivity for seventy (70) university-level athletes, including 35 with acute concussion and 35 healthy matched controls. Univariate analyses showed that greater symptom severity was mainly associated with lower pairwise connectivity in frontal, temporal and insular regions, along with higher connectivity in a sparser set of cerebellar regions. A novel multivariate approach also extracted two components that showed reliable covariation with symptom severity: (1) a network of frontal, temporal and insular regions where connectivity was negatively correlated with symptom severity (replicating the univariate findings); and (2) a network with anti-correlated elements of the default-mode network and sensorimotor system, where connectivity was positively correlated with symptom severity. These findings support the presence of connectomic signatures of symptom complaints following a sport-related concussion, including both increased and decreased functional connectivity within distinct functional brain networks.

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YNIMG Journal 2017 Journal Article

Optimizing fMRI preprocessing pipelines for block-design tasks as a function of age

Nathan W. Churchill
Pradeep Raamana
Robyn Spring
Stephen C. Strother

Functional Magnetic Resonance Imaging (fMRI) is a powerful neuroimaging tool, which is often hampered by significant noise confounds. There is evidence that our ability to detect activations in task fMRI is highly dependent on the preprocessing steps used to control noise and artifact. However, the vast majority of studies examining preprocessing pipelines in fMRI have focused on young adults. Given the widespread use of fMRI for characterizing the neurobiology of aging, it is critical to examine how the impact of preprocessing choices varies as a function of age. In this study, we employ the NPAIRS cross-validation framework, which optimizes pipelines based on metrics of prediction accuracy (P) and spatial reproducibility (R), to compare the effects of pipeline optimization between young (21–33 years) and older (61–82 years) cohorts, for three different block-design contrasts. Motion is shown to be a greater issue in the older cohort, and we introduce new statistical approaches to control for potential biases due to head motion during pipeline optimization. In comparison, data-driven methods of physiological noise correction show comparable benefits for both young and old cohorts. Using our optimization framework, we demonstrate that the optimal pipelines tend to be highly similar across age cohorts. In addition, there is a comparable, significant benefit of pipeline optimization across age cohorts, for (P, R) metrics and independent validation measures of activation overlap (both between-subject, within-session and within-subject, between-session). The choice of task contrast consistently shows a greater impact than the age cohort, for (P, R) metrics and activation overlap. Finally, adaptive pipeline optimization per task run shows improved sensitivity to age-related changes in brain activity, particularly for weaker, more complex cognitive contrasts. The current study provides the first detailed examination of preprocessing pipelines across age cohorts, demonstrating a significant benefit of adaptive pipeline optimization across age groups.

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YNICL Journal 2017 Journal Article

Symptom correlates of cerebral blood flow following acute concussion

Nathan W. Churchill
Michael G. Hutchison
Simon J. Graham
Tom A. Schweizer

Concussion is associated with significant symptoms within hours to days post-injury, including disturbances in physical function, cognition, sleep and emotion. However, little is known about how subjective impairments correlate with objective measures of cerebrovascular function following brain injury. This study examined the relationship between symptoms and cerebral blood flow (CBF) in individuals following sport-related concussion. Seventy university level athletes had CBF measured using Arterial Spin Labelling (ASL), including 35 with acute concussion and 35 matched controls and their symptoms were assessed using the Sport Concussion Assessment Tool 3 (SCAT3). For concussed athletes, greater total symptom severity was associated with elevated posterior cortical CBF, although mean CBF was not significantly different from matched controls (p =0. 46). Examining symptom clusters, athletes reporting greater cognitive symptoms also had lower frontal and subcortical CBF, relative to athletes with greater somatic symptoms. The “cognitive” and “somatic” subgroups also exhibited significant differences in CBF relative to controls (p ≤0. 026). This study demonstrates objective CBF correlates of symptoms in recently concussed athletes and shows that specific symptom clusters may have distinct patterns of altered CBF, significantly extending our understanding of the neurobiology of concussion and traumatic brain injury.

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YNICL Journal 2017 Journal Article

The first week after concussion: Blood flow, brain function and white matter microstructure

Nathan W. Churchill
Michael G. Hutchison
Doug Richards
General Leung
Simon J. Graham
Tom A. Schweizer

Concussion is a major health concern, associated with short-term deficits in physical function, emotion and cognition, along with negative long-term health outcomes. However, we remain in the early stages of characterizing MRI markers of concussion, particularly during the first week post-injury when symptoms are most severe. In this study, 52 varsity athletes were scanned using Magnetic Resonance Imaging (MRI), including 26 athletes with acute concussion (scanned 1-7 days post-injury) and 26 matched control athletes. A comprehensive set of functional and structural MRI measures were analyzed, including cerebral blood flow (CBF) and global functional connectivity (Gconn) of grey matter, along with fractional anisotropy (FA) and mean diffusivity (MD) of white matter. An analysis comparing acutely concussed athletes and controls showed limited evidence for reliable mean effects of acute concussion, with only MD showing spatially extensive differences between groups. We subsequently demonstrated that the number of days post-injury explained a significant proportion of inter-subject variability in MRI markers of acutely concussed athletes. Athletes scanned at early acute injury (1-3 days) had elevated CBF and Gconn and reduced FA, but those scanned at late acute injury (5-7 days) had the opposite response. In contrast, MD showed a more complex, spatially-dependent relationship with days post-injury. These novel findings highlight the variability of MRI markers during the acute phase of concussion and the critical importance of considering the acute injury time interval, which has significant implications for studies relating acute MRI data to concussion outcomes.

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YNIMG Journal 2014 Journal Article

Does resting-state connectivity reflect depressive rumination? A tale of two analyses

Marc G. Berman
Bratislav Misic
Martin Buschkuehl
Ethan Kross
Patricia J. Deldin
Scott Peltier
Nathan W. Churchill
Susanne M. Jaeggi

Major Depressive Disorder (MDD) is characterized by rumination. Prior research suggests that resting-state brain activation reflects rumination when depressed individuals are not task engaged. However, no study has directly tested this. Here we investigated whether resting-state epochs differ from induced ruminative states for healthy and depressed individuals. Most previous research on resting-state networks comes from seed-based analyses with the posterior cingulate cortex (PCC). By contrast, we examined resting state connectivity by using the complete multivariate connectivity profile (i. e. , connections across all brain nodes) and by comparing these results to seeded analyses. We find that unconstrained resting-state intervals differ from active rumination states in strength of connectivity and that overall connectivity was higher for healthy vs. depressed individuals. Relationships between connectivity and subjective mood (i. e. , behavior) were strongly observed during induced rumination epochs. Furthermore, connectivity patterns that related to subjective mood were strikingly different for MDD and healthy control (HC) groups suggesting different mood regulation mechanisms.

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YNIMG Journal 2014 Journal Article

Pattern classification of fMRI data: Applications for analysis of spatially distributed cortical networks

Grigori Yourganov
Tanya Schmah
Nathan W. Churchill
Marc G. Berman
Cheryl L. Grady
Stephen C. Strother

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e. g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI.

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YNIMG Journal 2013 Journal Article

PHYCAA+: An optimized, adaptive procedure for measuring and controlling physiological noise in BOLD fMRI

Nathan W. Churchill
Stephen C. Strother

The presence of physiological noise in functional MRI can greatly limit the sensitivity and accuracy of BOLD signal measurements, and produce significant false positives. There are two main types of physiological confounds: (1) high-variance signal in non-neuronal tissues of the brain including vascular tracts, sinuses and ventricles, and (2) physiological noise components which extend into gray matter tissue. These physiological effects may also be partially coupled with stimuli (and thus the BOLD response). To address these issues, we have developed PHYCAA+, a significantly improved version of the PHYCAA algorithm (Churchill et al. , 2011) that (1) down-weights the variance of voxels in probable non-neuronal tissue, and (2) identifies the multivariate physiological noise subspace in gray matter that is linked to non-neuronal tissue. This model estimates physiological noise directly from EPI data, without requiring external measures of heartbeat and respiration, or manual selection of physiological components. The PHYCAA+ model significantly improves the prediction accuracy and reproducibility of single-subject analyses, compared to PHYCAA and a number of commonly-used physiological correction algorithms. Individual subject denoising with PHYCAA+ is independently validated by showing that it consistently increased between-subject activation overlap, and minimized false-positive signal in non gray-matter loci. The results are demonstrated for both block and fast single-event task designs, applied to standard univariate and adaptive multivariate analysis models.

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YNIMG Journal 2012 Journal Article

PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI

Nathan W. Churchill
Grigori Yourganov
Robyn Spring
Peter M. Rasmussen
Wayne Lee
Jon E. Ween
Stephen C. Strother

The effects of physiological noise may significantly limit the reproducibility and accuracy of BOLD fMRI. However, physiological noise evidences a complex, undersampled temporal structure and is often non-orthogonal relative to the neuronally-linked BOLD response, which presents a significant challenge for identifying and removing such artifact. This paper presents a multivariate, data-driven method for the characterization and removal of physiological noise in fMRI data, termed PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis). The method identifies high frequency, autocorrelated physiological noise sources with reproducible spatial structure, using an adaptation of Canonical Correlation Analysis performed in a split-half resampling framework. The technique is able to identify physiological effects with vascular-linked spatial structure, and an intrinsic dimensionality that is task- and subject-dependent. We also demonstrate that increasing dimensionality of such physiological noise is correlated with increasing variability in externally-measured respiratory and cardiac processes. Using PHYCAA as a denoising technique significantly improves simulated signal detection with physiological noise, and real data-driven model prediction and reproducibility, for both block and event-related task designs. This is demonstrated compared to no physiological noise correction, and to the widely used RETROICOR (Glover et al. , 2000) physiological denoising algorithm, which uses externally measured cardiac and respiration signals.

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