Distinct patterns of resting-state connectivity in U.S. service members with mild traumatic brain injury versus posttraumatic stress disorder

Philippi, Carissa L.; Vélez, Carmen; Wade, Benjamin; Drennon, Ann Marie; Cooper, Douglas B.; Kennedy, Jan E.; Bowles, Amy O.; Lewis, Jeffrey D.; Reid, Matthew; York, Gerald E.; Newsome, Mary R.; Wilde, Elisabeth A.; Tate, David F.

doi:10.1007/s11682-021-00464-1

Cited by 12 publications

(7 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In both networks, there was a reduction in the number of edges connecting the frontal nodes to the temporal nodes (for the CO) or parietal nodes (for the FP) in the mTBI group. These findings are consistent with those reported by others in cognitive control networks (e.g., Han et al, 2016 ; Philippi et al, 2021 ) as well as in other intrinsic brain networks (e.g., default mode network, Santhanam et al, 2019 ; Philippi et al, 2021 ). These regional disconnections, separating frontal from either temporal or parietal nodes, provide additional evidence for the disconnection hypothesis of Fagerholm et al (2015) that network “hubs” (nodes within a network that efficiently connect other nodes) are disconnected in TBI.…”

Section: Discussionsupporting

confidence: 93%

Cingulo-Opercular and Frontoparietal Network Control of Effort and Fatigue in Mild Traumatic Brain Injury

Ramage

Ray

Franz

et al. 2022

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

Neural substrates of fatigue in traumatic brain injury (TBI) are not well understood despite the considerable burden of fatigue on return to productivity. Fatigue is associated with diminishing performance under conditions of high cognitive demand, sense of effort, or need for motivation, all of which are associated with cognitive control brain network integrity. We hypothesize that the pathophysiology of TBI results in damage to diffuse cognitive control networks, disrupting coordination of moment-to-moment monitoring, prediction, and regulation of behavior. We investigate the cingulo-opercular (CO) and frontoparietal (FP) networks, which are engaged to sustain attention for task and maintain performance. A total of 61 individuals with mild TBI and 42 orthopedic control subjects participated in functional MRI during performance of a constant effort task requiring altering the amount of effort (25, 50, or 75% of maximum effort) utilized to manually squeeze a pneumostatic bulb across six 30-s trials. Network-based statistics assessed within-network organization and fluctuation with task manipulations by group. Results demonstrate small group differences in network organization, but considerable group differences in the evolution of task-related modulation of connectivity. The mild TBI group demonstrated elevated CO connectivity throughout the task with little variation in effort level or time on task (TOT), while CO connectivity diminished over time in controls. Several interregional CO connections were predictive of fatigue in the TBI group. In contrast, FP connectivity fluctuated with task manipulations and predicted fatigue in the controls, but connectivity fluctuations were delayed in the mild traumatic brain injury (mTBI) group and did not relate to fatigue. Thus, the mTBI group’s hyper-connectivity of the CO irrespective of task demands, along with hypo-connectivity and delayed peak connectivity of the FP, may allow for attainment of task goals, but also contributes to fatigue. Findings are discussed in relation to performance monitoring of prediction error that relies on internal cues from sensorimotor feedback during task performance. Delay or inability to detect and respond to prediction errors in TBI, particularly evident in bilateral insula-temporal CO connectivity, corresponds to day-to-day fatigue and fatigue during task performance.

show abstract

Section: Discussionsupporting

confidence: 93%

Cingulo-Opercular and Frontoparietal Network Control of Effort and Fatigue in Mild Traumatic Brain Injury

Ramage

Ray

Franz

et al. 2022

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in neuroscience for detecting intrinsic brain functional architecture as well as interactions between and within neural networks as the biomarkers of cognitive and neurological disorders ( Jeter et al, 2013 ; Sours et al, 2015 ). Various approaches have been proposed for analyzing rs-fMRI data in mTBI cohorts including independent component analysis (ICA) ( Bittencourt-Villalpando et al, 2021 ), graph theory ( van der Horn et al, 2017 ), seed-based FC ( Madhavan et al, 2019 ; Lemme et al, 2021 ; Philippi et al, 2021 ), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) ( Zhan et al, 2015 ; Vedaei et al, 2021 ), degree centrality (DC) ( Li et al, 2019 ), voxel-mirrored homotopic connectivity (VMHC) ( Puig et al, 2020 ; Song et al, 2022 ). The growing body of studies of functional neuroimaging of the resting brain has shown that mTBI is accompanied by alterations of resting-state functional connectivity between and within intrinsic brain networks including the default mode network (DMN), fronto-parietal, motor, dorsal attention, and visual networks ( Shumskaya et al, 2012 ; Stevens et al, 2012 ; Zhou et al, 2012 , 2014 ; Dall’Acqua et al, 2017 ; Palacios et al, 2017 ; Liu et al, 2018 ; Li et al, 2019 ; Madhavan et al, 2019 ; Meier et al, 2020 ; Song et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

et al. 2023

View full text Add to dashboard Cite

Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67–100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42–93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level.Clinical trial registration[clinicaltrials.gov], identifier [NCT03241732].

show abstract

“…Another study evaluating individuals with mTBI only relative to those with comorbid PTSD focussed on the DMN and demonstrated greater connectivity for the mTBI only group which was also associated with reduced PTSD symptoms (Santhanam et al, 2019 ). There is only one previous study that has directly compared resting‐state connectivity between PTSD and mTBI without PTSD (Philippi et al, 2021 ). This study used data from military service personnel and employed a seed‐based connectivity analysis; the results indicated connectivity for the DMN and the cognitive control (fronto‐parietal) network was significantly reduced in the PTSD group compared with mTBI and also reduced for both clinical groups relative to controls.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, by measuring PTSD symptoms and PCS, the study also permitted examination of the differential associations of PTSD symptoms and functional connectivity to PCS. Based on previous research our main hypothesis was that mTBI and PTSD would demonstrate some overlap in network connectivity deficits, particularly in the DMN and cognitive control networks (due to overlap of symptoms commonly observed in the two conditions; Philippi et al, 2021 ). We also hypothesised there would be a distinct connectivity profile between the two groups as our mTBI group did not have comorbid PTSD, for example related to the DMN (reduced for PTSD relative to mTBI; Philippi et al, 2021 ; Santhanam et al, 2019 ) and salience network (increased in PTSD relative to mTBI; Akiki et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…Based on previous research our main hypothesis was that mTBI and PTSD would demonstrate some overlap in network connectivity deficits, particularly in the DMN and cognitive control networks (due to overlap of symptoms commonly observed in the two conditions; Philippi et al, 2021 ). We also hypothesised there would be a distinct connectivity profile between the two groups as our mTBI group did not have comorbid PTSD, for example related to the DMN (reduced for PTSD relative to mTBI; Philippi et al, 2021 ; Santhanam et al, 2019 ) and salience network (increased in PTSD relative to mTBI; Akiki et al, 2017 ). We also hypothesised that both mTBI and PTSD would demonstrate altered connectivity profiles in these networks compared with HC and these aberrations would be associated with PCS and PTSD symptoms, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparison of the functional connectome in mild traumatic brain injury and post‐traumatic stress disorder

Klimova

Breukelaar

Bryant

et al. 2022

Human Brain Mapping

View full text Add to dashboard Cite

Post‐traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) often co‐occur in the context of threat to one's life. These conditions also have an overlapping symptomatology and include symptoms of anxiety, poor concentration and memory problems. A major challenge has been articulating the underlying neurobiology of these overlapping conditions. The primary aim of this study was to compare intrinsic functional connectivity between mTBI (without PTSD) and PTSD (without mTBI). The study included functional MRI data from 176 participants: 42 participants with mTBI, 67 with PTSD and a comparison group of 66 age and sex‐matched healthy controls. We used network‐based statistical analyses for connectome‐wide comparisons of intrinsic functional connectivity between mTBI relative to PTSD and controls. Our results showed no connectivity differences between mTBI and PTSD groups. However, we did find that mTBI had significantly reduced connectivity relative to healthy controls within an extensive network of regions including default mode, executive control, visual and auditory networks. The mTBI group also displayed hyperconnectivity between dorsal and ventral attention networks and perceptual regions. The PTSD group also demonstrated abnormal connectivity within these networks relative to controls. Connectivity alterations were not associated with severity of PTSD or post‐concussive symptoms in either clinical group. Taken together, the similar profiles of intrinsic connectivity alterations in these two conditions provide neural evidence that can explain, in part, the overlapping symptomatology between mTBI and PTSD.

show abstract

Distinct patterns of resting-state connectivity in U.S. service members with mild traumatic brain injury versus posttraumatic stress disorder

Cited by 12 publications

References 71 publications

Cingulo-Opercular and Frontoparietal Network Control of Effort and Fatigue in Mild Traumatic Brain Injury

Cingulo-Opercular and Frontoparietal Network Control of Effort and Fatigue in Mild Traumatic Brain Injury

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

A comparison of the functional connectome in mild traumatic brain injury and post‐traumatic stress disorder

Contact Info

Product

Resources

About