Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning.
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ABSTRACT:
SUBMITTER: Vergara VM
PROVIDER: S-EPMC6051314 | biostudies-other | 2018
REPOSITORIES: biostudies-other
Publications
Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning.
Vergara Victor M VM Mayer Andrew R AR Kiehl Kent A KA Calhoun Vince D VD
NeuroImage. Clinical 20180315
Mild traumatic brain injury (mTBI) can result in symptoms that affect a person's cognitive and social abilities. Improvements in diagnostic methodologies are necessary given that current clinical techniques have limited accuracy and are solely based on self-reports. Recently, resting state functional network connectivity (FNC) has shown potential as an important imaging modality for the development of mTBI biomarkers. The present work explores the use of dynamic functional network connectivity ( ...[more]
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Project description:Traumatic brain injury (TBI) may adversely affect a person's thinking, memory, personality, and behavior. While mild TBI (mTBI) diagnosis is challenging, there is a risk for long-term psychiatric, neurologic, and psychosocial problems in some patients that motivates the search for new and better biomarkers. Recently, diffusion magnetic resonance imaging (dMRI) has shown promise in detecting mTBI, but its validity is still being investigated. Resting state functional network connectivity (rsFNC) is another approach that is emerging as a promising option for the diagnosis of mTBI. The present work investigated the use of rsFNC for mTBI detection compared with dMRI results on the same cohort. Fifty patients with mTBI (25 males) and age-sex matched healthy controls were recruited. Features from dMRI were obtained using all voxels, the enhanced Z-score microstructural assessment for pathology, and the distribution corrected Z-score. Features based on rsFNC were obtained through group independent component analysis and correlation between pairs of resting state networks. A linear support vector machine was used for classification and validated using leave-one-out cross validation. Classification achieved a maximum accuracy of 84.1% for rsFNC and 75.5% for dMRI and 74.5% for both combined. A t test analysis revealed significant increase in rsFNC between cerebellum versus sensorimotor networks and between left angular gyrus versus precuneus in subjects with mTBI. These outcomes suggest that inclusion of both common and unique information is important for classification of mTBI. Results also suggest that rsFNC can yield viable biomarkers that might outperform dMRI and points to connectivity to the cerebellum as an important region for the detection of mTBI.
| S-EPMC5333571 | biostudies-literature
Project description:Mild traumatic brain injury (mTBI) is one of the most common neurological disorders for which a subset of patients develops persistent postconcussive symptoms. Previous studies discovered abnormalities and disruptions in the brain functional networks of mTBI patients principally using static functional connectivity measures which assume that neural communication across the brain is static during resting state conditions. In this study, we examine the differences in dynamic neural communication between mTBI and control participants through the application of a combination of dynamic functional analysis and graph theoretic algorithms. Resting state functional magnetic resonance imaging data was obtained on 47 mTBI patients at the acute stage of injury and 30 demographically matched healthy control participants. Results show unique alterations in both the static and dynamic functional connectivity at the acute stage in mTBI patients who suffer persistent symptoms (≥6 months after injury). In addition, mTBI patients with postconcussion syndrome demonstrated a unique allocation of time in various brain states compared to both control participants and mTBI patients with favorable outcomes. These findings suggest that global damage to the overall communication across the brain in the acute stage may contribute to chronic mTBI symptoms. Dynamic functional analysis is a powerful tool that provides insights into the brain states and the innovative analysis methodology utilized may hold the potential to delineate patients predisposed to poor outcomes upon early presentation following injury.
| S-EPMC6648233 | biostudies-literature
Project description:IntroductionDynamic functional network connectivity (dFNC), derived from magnetic resonance imaging (fMRI), is an important technique in the search for biomarkers of brain diseases such as mild traumatic brain injury (mTBI). At the individual level, mTBI can affect cognitive functions and change personality traits. Previous research aimed at detecting significant changes in the dFNC of mTBI subjects. However, one of the main concerns in dFNC analysis is the appropriateness of methods used to correct for subject movement. In this work, we focus on the effect that rearranging movement correction at different points of the processing pipeline has in dFNC analysis utilizing mTBI data.MethodsThe sample cohort consists of 50 mTBI patients and matched healthy controls. A 5-min resting-state run was completed by each participant. Data were preprocessed using different pipeline alternatives varying with the place where motion-related variance was removed. In all pipelines, group-independent component analysis (gICA) followed by dFNC analysis was performed. Additional tests were performed varying the detection of temporal spikes, the number of gICA components, and the sliding-window size. A linear support vector machine was used to test how each pipeline affects classification accuracy.ResultsResults suggest that correction for motion variance before spatial smoothing, but leaving correction for spiky time courses after gICA produced the best mean classification performance. The number of gICA components and the sliding-window size were also important in determining classification performance. Variance in spikes correction affected some pipelines more than others with fewer significant differences than the other parameters.ConclusionThe sequence of preprocessing steps motion regression, smoothing, gICA, and despiking produced data most suitable for differentiating mTBI from healthy subjects. However, the selection of optimal preprocessing parameters strongly affected the final results.
| S-EPMC5651393 | biostudies-literature
Project description:ObjectivesResearch suggests that the majority of mild traumatic brain injury (mTBI) patients exhibit both cognitive and emotional dysfunction within the first weeks of injury, followed by symptom resolution 3-6 months postinjury. The neuronal correlates of said dysfunction are difficult to detect with standard clinical neuroimaging, complicating differential diagnosis and early identification of patients who may not recover. This study examined whether resting state functional magnetic resonance imaging (fMRI) provides objective markers of injury and predicts cognitive, emotional, and somatic complaints in mTBI patients semiacutely (<3 weeks postinjury) and in late recovery (3-5 month) phases.MethodsTwenty-seven semiacute mTBI patients and 26 gender, age, and education-matched controls were studied. Fifteen of 27 patients returned for a follow-up visit 3-5 months postinjury. The main dependent variables were spontaneous fluctuations (temporal correlation) in the default-mode (DMN) and fronto-parietal task-related networks as measured by fMRI.ResultsSignificant differences in self-reported cognitive, emotional, and somatic complaints were observed (all P < 0.05), despite normal clinical (T1 and T2) imaging and neuropsychological testing results. Mild TBI patients demonstrated decreased functional connectivity within the DMN and hyper-connectivity between the DMN and lateral prefrontal cortex. Measures of functional connectivity exhibited high levels of sensitivity and specificity for patient classification and predicted cognitive complaints in the semi-acute injury stage. However, no changes in functional connectivity were observed across a 4-month recovery period.ConclusionsAbnormal connectivity between the DMN and frontal cortex may provide objective biomarkers of mTBI and underlie cognitive impairment.
| S-EPMC3204375 | biostudies-literature
Project description:Mild traumatic brain injury (mTBI) is the most common neurological disorder and is typically characterized by temporally limited cognitive impairment and emotional symptoms. Previous examinations of intrinsic resting state networks in mTBI have primarily focused on abnormalities in static functional connectivity, and deficits in dynamic functional connectivity have yet to be explored in this population. Resting-state data was collected on 48 semi-acute (mean = 14 days post-injury) mTBI patients and 48 matched healthy controls. A high-dimensional independent component analysis (N = 100) was utilized to parcellate intrinsic connectivity networks (ICN), with a priori hypotheses focusing on the default-mode network (DMN) and sub-cortical structures. Dynamic connectivity was characterized using a sliding window approach over 126 temporal epochs, with standard deviation serving as the primary outcome measure. Finally, distribution-corrected z-scores (DisCo-Z) were calculated to investigate changes in connectivity in a spatially invariant manner on a per-subject basis. Following appropriate correction for multiple comparisons, no significant group differences were evident on measures of static or dynamic connectivity within a priori ICN. Reduced (HC > mTBI patients) static connectivity was observed in the DMN at uncorrected (p < 0.005) thresholds. Finally, a trend (p = 0.07) for decreased dynamic connectivity in patients across all ICN was observed during spatially invariant analyses (DisCo-Z). In the semi-acute phase of recovery, mTBI was not reliably associated with abnormalities in static or dynamic functional connectivity within the DMN or sub-cortical structures.
| S-EPMC4504345 | biostudies-literature
Project description: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].
| S-EPMC9869678 | biostudies-literature
Project description:PURPOSE:To investigate whether there is imaging evidence of hypothalamic injury in patients with mild traumatic brain injury (MTBI), which is a major public health problem due to the high prevalence and difficulty in diagnosis and treatment. MATERIALS AND METHODS:Twenty-four patients (mean age 34.2, range, 18-56 years) with symptomatic MTBI and 22 age-matched healthy controls (mean age 37.0, range 20-61 years) participated in the study. Diffusion kurtosis imaging was performed with diffusion-weighted images acquired along 30 gradient directions and three b-values (b?=?0, 1000, 2000 s/mm2 ) based on a twice-refocused spin-echo sequence with a 3T magnetic resonance imaging (MRI) scanner. Resting-state functional (f)MRI with standard echo planar imaging (EPI) were performed to localize the resting-state networks (RSN) and hypothalamic functional connectivity. RESULTS:There were significantly reduced mean kurtosis (P?=?0.0092) and radial kurtosis (P?=?0.0078) in patients as compared to controls in the hypothalamus. Furthermore, there was a significant negative correlation (r?=?-0.675, P?=?0.0007) between radial kurtosis in the hypothalamus and fatigue severity scale in patients. The MTBI group also showed disrupted hypothalamic RSNs, with significantly decreased positive connectivity in medial prefrontal cortex, inferior posterior parietal, and cingulate regions but increased connectivity in the peri-hypothalamic regions and cerebellum, together with significantly decreased negative RSNs in visual and bilateral premotor areas (cluster corrected P < 0.05). CONCLUSION:Our results show disruption of functional and structural hypothalamic connectivity in patients with MTBI, and might further the understanding of an array of clinical symptoms in MTBI such as sleep disturbance and fatigue. LEVEL OF EVIDENCE:2 J. Magn. Reson. Imaging 2017;45:1105-1112.
| S-EPMC5274605 | biostudies-literature
Project description:Mental fatigue in healthy individuals is typically observed under conditions of high cognitive demand, particularly when effort is required to perform a task for a long period of time-thus the concepts of fatigue and effort are closely related. In brain injured individuals, mental fatigue can be a persistent and debilitating symptom. Presence of fatigue after brain injury is prognostic for return to work/school and engagement in activities of daily life. As such, it should be a high priority for treatment in this population, but because there is little understanding of its behavioral and neural underpinnings, the target for such treatment is unknown. Here, the neural underpinnings of fatigue and effort are investigated in active duty military service members with mild traumatic brain injury (mTBI) and demographically-matched orthopedic controls. Participants performed a Constant Effort task for which they were to hold a pre-defined effort level constant for long durations during fMRI scanning. The task allowed for investigation of the neural systems underlying fatigue and their relationship with sense of effort. While brain activation associated with effort and fatigue did not differentiate the mTBI and controls, functional connectivity amongst active brain regions did. The mTBI group demonstrated immediate hyper-connectivity that increased with effort level but diminished quickly when there was a need to maintain effort. Controls, in contrast, demonstrated a similar pattern of hyper-connectivity, but only when maintaining effort over time. Connectivity, particularly between the left anterior insula, rostral anterior cingulate cortex, and right-sided inferior frontal regions, correlated with effort-level and state fatigue in mTBI participants. These connections also correlated with effort level in the Control group, but only the connection between the left insula and superior medial frontal gyrus correlated with fatigue, suggesting a differing pattern of connectivity. These findings align, in part, with the dopamine imbalance, and neural efficiency hypotheses that pose key roles for medial frontal connections with insular or striatal regions in motivating or optimizing performance. Sense of effort and fatigue are closely related. As people fatigue, sense of effort increases systematically. The data propose a complex link between sense of effort, fatigue, and mTBI that is centered in what may be an inefficient neural system due to brain trauma that warrants further investigation.
| S-EPMC6345685 | biostudies-literature
Project description:Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI.
| S-EPMC4632071 | biostudies-literature
Project description:Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24?h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified cross-validation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middle-income countries.
| S-EPMC6881446 | biostudies-literature