DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans

Biomarkers of Neurodegeneration in Post-Traumatic Stress Disorder: An Integrative Review

Post-Traumatic Stress Disorder (PTSD) is a chronic psychiatric disorder that occurs following exposure to traumatic events. Recent evidence suggests that PTSD may be a risk factor for the development of subsequent neurodegenerative disorders, including Alzheimer's dementia and Parkinson's disease. Identification of biomarkers known to be associated with neurodegeneration in patients with PTSD would shed light on the pathophysiological mechanisms linking these disorders and would also help in the development of preventive strategies for neurodegenerative disorders in PTSD. With this background, the PubMed and Scopus databases were searched for studies designed to identify biomarkers that could be associated with an increased risk of neurodegenerative disorders in patients with PTSD. Out of a total of 342 citations retrieved, 29 studies were identified for inclusion in the review. The results of these studies suggest that biomarkers such as cerebral cortical thinning, disrupted white matter integrity, specific genetic polymorphisms, immune-inflammatory alterations, vitamin D deficiency, metabolic syndrome, and objectively documented parasomnias are significantly associated with PTSD and may predict an increased risk of subsequent neurodegenerative disorders. The biological mechanisms underlying these changes, and the interactions between them, are also explored. Though requiring replication, these findings highlight a number of biological pathways that plausibly link PTSD with neurodegenerative disorders and suggest potentially valuable avenues for prevention and early intervention.

Volumetric MRI Findings in Mild Traumatic Brain Injury (mTBI) and Neuropsychological Outcome

Region of interest (ROI) volumetric assessment has become a standard technique in quantitative neuroimaging. ROI volume is thought to represent a coarse proxy for making inferences about the structural integrity of a brain region when compared to normative values representative of a healthy sample, adjusted for age and various demographic factors. This review focuses on structural volumetric analyses that have been performed in the study of neuropathological effects from mild traumatic brain injury (mTBI) in relation to neuropsychological outcome. From a ROI perspective, the probable candidate structures that are most likely affected in mTBI represent the target regions covered in this review. These include the corpus callosum, cingulate, thalamus, pituitary-hypothalamic area, basal ganglia, amygdala, and hippocampus and associated structures including the fornix and mammillary bodies, as well as whole brain and cerebral cortex along with the cerebellum. Ventricular volumetrics are also reviewed as an indirect assessment of parenchymal change in response to injury. This review demonstrates the potential role and limitations of examining structural changes in the ROIs mentioned above in relation to neuropsychological outcome. There is also discussion and review of the role that post-traumatic stress disorder (PTSD) may play in structural outcome in mTBI. As emphasized in the conclusions, structural volumetric findings in mTBI are likely just a single facet of what should be a multimodality approach to image analysis in mTBI, with an emphasis on how the injury damages or disrupts neural network integrity. The review provides an historical context to quantitative neuroimaging in neuropsychology along with commentary about future directions for volumetric neuroimaging research in mTBI.

Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus

There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.

Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects

AIM

Diffuse axonal injury (DAI) is one of the most common pathological features of traumatic brain injury (TBI). Diffusion tensor imaging (DTI) indices can be used to identify and quantify white matter microstructural changes following DAI. Recently, many studies have used DTI with various machine learning approaches to predict white matter microstructural changes following TBI. The current study sought to examine whether our classification approach using multiple DTI indices in conjunction with machine learning is a useful tool for diagnosing/classifying TBI patients and healthy controls.

METHODS

Participants were adult patients with chronic TBI (n = 26) with DAI pathology, and age- and sex-matched healthy controls (n = 26). DTI images were obtained from all participants. Tract-based spatial statistics analyses were applied to DTI images. Classification models were built using principal component analysis and support vector machines. Receiver operator characteristic curve analysis and area under the curve were used to assess the classification performance of the different classifiers.

RESULTS

Tract-based spatial statistics revealed significantly decreased fractional anisotropy, as well as increased mean diffusivity, axial diffusivity, and radial diffusivity in patients with TBI compared with healthy controls (all p-values < 0.01). The principal component analysis and support vector machine-based machine learning classification using combined DTI indices classified patients with TBI and healthy controls with an accuracy of 90.5% with an area under the curve of 93 ± 0.09.

CONCLUSION

These results highlight the potential of our approach combining multiple DTI measures to identify patients with TBI.

Molecular and Diffusion Tensor Imaging Biomarkers of Traumatic Brain Injury: Principles for Investigation and Integration

The last 20 years have seen the advent of new technologies that enhance the diagnosis and prognosis of traumatic brain injury (TBI). There is recognition that TBI affects the brain beyond initial injury, in some cases inciting a progressive neuropathology that leads to chronic impairments. Medical researchers are now searching for biomarkers to detect and monitor this condition. Perhaps the most promising developments are in the biomolecular and neuroimaging domains. Molecular assays can identify proteins indicative of neuronal injury and/or degeneration. Diffusion imaging now allows sensitive evaluations of the brain's cellular microstructure. As the pace of discovery accelerates, it is important to survey the research landscape and identify promising avenues of investigation. In this review, we discuss the potential of molecular and diffusion tensor imaging (DTI) biomarkers in TBI research. Integration of these technologies could advance models of disease prognosis, ultimately improving care. To date, however, few studies have explored relationships between molecular and DTI variables in patients with TBI. Here, we provide a short primer on each technology, review the latest research, and discuss how these biomarkers may be incorporated in future studies.

Predictive Accuracy of the Sport Concussion Assessment Tool 3 and Vestibular/Ocular-Motor Screening, Individually and In Combination: A National Collegiate Athletic Association-Department of Defense Concussion Assessment, Research and Education Consortium Analysis

BACKGROUND

Vestibular and ocular symptoms in sport-related concussions are common. The Vestibular/Ocular-Motor Screening (VOMS) tool is a rapid, free, pen-and-paper tool that directly assesses these symptoms and shows consistent utility in concussion identification, prognosis, and management. However, a VOMS validation study in the acute concussion period of a large sample is lacking.

PURPOSE

To examine VOMS validity among collegiate student-athletes, concussed and nonconcussed, from the multisite National Collegiate Athletic Association-Department of Defense Concussion Assessment, Research and Education (CARE) Consortium. A secondary aim was to utilize multidimensional machine learning pattern classifiers to deduce the additive power of the VOMS in relation to components of the Sport Concussion Assessment Tool 3 (SCAT3).

STUDY DESIGN

Cohort study (diagnosis); Level of evidence, 3.

METHODS

Preseason and acute concussion assessments were analyzed for 419 student-athletes. Variables in the analysis included the VOMS, Balance Error Scoring System, Standardized Assessment of Concussion, and SCAT3 symptom evaluation score. Descriptive statistics were calculated for all tools, including Kolmogorov-Smirnov significance and Cohen d effect size. Correlations between tools were analyzed with Spearman r, and predictive accuracy was evaluated through an Ada Boosted Tree machine learning model's generated receiver operating characteristic curves.

RESULTS

Total VOMS scores and SCAT3 symptom scores demonstrated significant increases in the acute concussion time frame (Cohen d = 1.23 and 1.06; P < .0001), whereas the Balance Error Scoring System lacked clinical significance (Cohen d = 0.17). Incorporation of VOMS into the full SCAT3 significantly boosted overall diagnostic ability by 4.4% to an area under the curve of 0.848 (P < .0001) and produced a 9% improvement in test sensitivity over the existing SCAT3 battery.

CONCLUSION

The results from this study highlight the relevance of the vestibular and oculomotor systems to concussion and the utility of the VOMS tool. Given the 3.8 million sports-related and 45,121 military-related concussions per year, the addition of VOMS to the SCAT3 is poised to identify up to an additional 304,000 athletes and 3610 servicemembers annually who are concussed, thereby improving concussion assessment and diagnostic rates. Health care providers should consider the addition of VOMS to their concussion assessment toolkits, as its use can positively affect assessment and management of concussions, which may ultimately improve outcomes for this complex and common injury.

Intelligent diagnostic analysis based on pattern recognition of DTI image

Traditional brain imaging usually does not show anomalies. Based on this, this study used DTI to find evidence that the brain structure microstructure may be abnormal, and to study the BOLD signal changes of functional magnetic resonance imaging and the changes of DTI microstructure in patients with mild traumatic brain injury. At the same time, based on literature collection and actual data, the current status of nuclear magnetic resonance diagnosis of brain trauma was collected. Moreover, this study combines the problem to improve the algorithm and propose an image diagnosis method for brain trauma to improve the cluster quality and stability. In addition, the experiment was designed to analyze the performance of the algorithm in this study. Finally, in this study, resting state functional magnetic resonance imaging was used to study the resting brain function in patients with mild cognitive impairment within one week after traumatic brain injury. The results show that the method proposed in this study has certain effects and can provide theoretical reference for related research.

Relationship between depression and dorsolateral prefronto-thalamic tract injury in patients with mild traumatic brain injury

The prefrontal lobe has been considered to be closely related to depression. This study examined the relationship between depression and three prefronto-thalamic tract (PF-TT) regions (the dorsolateral prefronto-thalamic tract [DLPF-TT], ventrolateral prefronto-thalamic tract [VLPF-TT], and the orbitofronto-thalamic tract [OF-TT]) in patients with mild traumatic brain injury (TBI), using diffusion tensor tractography (DTT). Thirty-seven patients with depression following mild TBI were recruited based on Beck Depression Inventory-II (BDI-II) scores. Thirty-one normal control subjects were also recruited. The three regions of the PF-TTs were reconstructed using probabilistic tractography and DTT parameters for each of the three PF-TT regions were determined. The tract volume of the DLPF-TT and OF-TT in the patient group showed a significant decrease compared to that of the control group (p < 0.05). The BDI-II score of the patient group showed a moderate negative correlation with the tract volume value of the right (r =  − 0.33) and left (r =  − 0.41) DLPF-TT (p < 0.05). On the other hand, no significant correlations were detected between the BDI-II score of the patient group and the values of the other DTT parameters values for the three PF-TT regions (p > 0.05). Using DTT, depression was found to be closely related to a DLPF-TT injury in patients with mild TBI. We believe that evaluation of the DLPF-TT using DTT would be helpful when assessing patients with depression following mild TBI. These results can provide useful information regarding the proper application of neuromodulation in the management of depression.

Acute Dilation of Venous Sinuses in Animal Models of Mild Traumatic Brain Injury Detected Using 9.4T MRI

Mild traumatic brain injury (mTBI) is a debilitating but extremely common form of brain injury that affects a substantial number of people each year. mTBI is especially common in children and adolescents. Our understanding of mTBI pathophysiology is limited, and there is currently no accepted marker for disease severity. A potential marker for disease severity may be cerebrovascular dysfunction. Recent findings have implicated cerebrovascular alteration as an important component of mTBI and suggest it contributes to the development of persistent, long-term symptoms. In this paper, we conducted two studies to investigate whether mTBI affects venous drainage patterns in the central nervous system using alterations in the size of venous sinuses as a marker of changes in drainage. Using a closed head vertical weight-drop model and a lateral impact injury model of mTBI, we imaged and quantified the size of three major draining vessels in the adolescent rat brain using 9.4T MRI. Areas and volumes were quantified in the superior sagittal sinus and left and right transverse sinuses using images acquired from T2w MRI in one study and post-gadolinium T1w MRI in another. Our results indicated that the three venous sinuses were significantly larger in mTBI rats as compared to sham rats 1-day post injury but recovered to normal size 2 weeks after. Acutely enlarged sinuses post-mTBI may indicate abnormal venous drainage, and this could be suggestive of a cerebrovascular response to trauma.

Cerebral white matter microstructural changes in isolated maxillofacial trauma and associated neuropsychological outcomes

Isolated traumatic maxillofacial injury without concomitant brain injury may cause delayed post-concussive symptoms. Early identification allows optimal diagnosis, prognostication, and therapeutic intervention. The aim of this prospective observational study was to investigate longitudinal microstructural changes of the white matter (WM) tracts based on diffusion tensor imaging (DTI) indices in patients with isolated maxillofacial injuries, immediately and 6 months post-trauma, and to correlate these DTI indices with neuropsychological changes observed. Twenty-one patients with isolated maxillofacial injuries and 21 age-matched controls were recruited. DTI was performed and indices were calculated for 50 WM tracts. The neuropsychological evaluation was done using the screening module of the Neuropsychological Assessment Battery. Patients were subjected to repeat DTI and neuropsychological evaluation at 6 months post-trauma. Reduced fractional anisotropy (FA) and increased median (MD) and radial diffusivity (RD) in the acute phase were seen in major association, projection, and commissural fibre bundles, indicative of vasogenic oedema. These changes correlated with attention and executive function deficits in the acute phase, as well as improvement in memory and visuospatial function in the chronic phase. Isolated maxillofacial trauma patients develop WM microstructural damage, which may impair cognitive performance acutely and over time. DTI indices can serve as predictive imaging biomarkers for long-term cognitive deficits in isolated maxillofacial injuries.

Tractography in Neurosurgery: A Systematic Review of Current Applications

The ability to visualize the brain's fiber connections noninvasively in vivo is relatively young compared with other possibilities of functional magnetic resonance imaging. Although many studies showed tractography to be of promising value for neurosurgical care, the implications remain inconclusive. An overview of current applications is presented in this systematic review. A search was conducted for (("tractography" or "fiber tracking" or "fibre tracking") and "neurosurgery") that produced 751 results. We identified 260 relevant articles and added 20 more from other sources. Most publications concerned surgical planning for resection of tumors (n = 193) and vascular lesions (n = 15). Preoperative use of transcranial magnetic stimulation was discussed in 22 of these articles. Tractography in skull base surgery presents a special challenge (n = 29). Fewer publications evaluated traumatic brain injury (TBI) (n = 25) and spontaneous intracranial bleeding (n = 22). Twenty-three articles focused on tractography in pediatric neurosurgery. Most authors found tractography to be a valuable addition in neurosurgical care. The accuracy of the technique has increased over time. There are articles suggesting that tractography improves patient outcome after tumor resection. However, no reliable biomarkers have yet been described. The better rehabilitation potential after TBI and spontaneous intracranial bleeding compared with brain tumors offers an insight into the process of neurorehabilitation. Tractography and diffusion measurements in some studies showed a correlation with patient outcome that might help uncover the neuroanatomical principles of rehabilitation itself. Alternative corticofugal and cortico-cortical networks have been implicated in motor recovery after ischemic stroke, suggesting more complex mechanisms in neurorehabilitation that go beyond current models. Hence tractography may potentially be able to predict clinical deficits and rehabilitation potential, as well as finding possible explanations for neurologic disorders in retrospect. However, large variations of the results indicate a lack of data to establish robust diagnostical concepts at this point. Therefore, in vivo tractography should still be interpreted with caution and by experienced surgeons.

Investigating Gray and White Matter Structural Substrates of Sex Differences in the Narrative Abilities of Healthy Adults

Linguistic aspects of narration have been investigated in healthy populations, in a wide variety of languages and speech genres with very different results. There is some evidence indicating that linguistic elements, such as speech rate (i.e., the measure indicating the amount of speech produced in a certain time period), mean length of utterance (MLU) (i.e., the index reflecting sentence grammatical structure), frequency of nouns and verbs, might be affected by non-linguistic factors such as sex. On the other hand, despite the existence of neuroimaging evidence of structural differences between males and females, it is yet unknown how such differences could explain between-sex disparities in linguistic abilities in natural speech contexts. To date, no study has evaluated discourse production elements in relation to sex differences and their neural correlates in terms of brain structure, a topic that could provide unique insights on the relationship between language and the brain. The aim of the present study was to determine sex differences in narrative skills in healthy adults and to investigate white and gray matter structural correlates of linguistic skills in each group. Twenty-seven male and 30 female (N = 57) right-handed, neurologically intact, monolingual Greek speakers, matched for age and years of education, participated. Narrations of a personal medical event were elicited. Linguistic elements of speech rate (words per minute), MLUs, frequency of nouns and verbs were calculated for each speech sample, by two independent raters. Structural 3D T1 images were segmented and parcellated using FreeSurfer and whole-brain between-sex differences in cortical thickness, cortical volume and surface area, were obtained. Between-group differences in white matter diffusion tensor scalars were examined via Tract-Based Spatial-Statistics and whole-brain tractography and automated tract delineation using Automated Fiber Quantification. Speech rate and noun frequency were significantly lower for men, while verb frequency was significantly higher for women, but no differences were identified for MLU. Regarding cortical measures, males demonstrated increased volume, surface area and cortical thickness in several bilateral regions, while no voxel-wise or tractography-based between-group differences in white matter metrics were observed. Regarding the relationship between sex and speech variables, hierarchical regression analyses showed that the superior/middle frontal cluster in surface area may serve as a significant predictor of speech rate variance, but only in females. We discuss several possible interpretations of how sex-related speech abilities could be represented differently in men and women in gray matter structures within the broad language network.

Voxel-based global-brain functional connectivity alterations in first-episode drug-naive patients with somatization disorder

BACKGROUND

Altered functional connectivity (FC) is associated with the pathophysiology of patients with somatization disorder (SD). However, inconsistent results were obtained due to different selections of regions of interest (ROIs) in previous researches. This study aims to examine voxel-wise brain-wide FC alterations in patients with first-episode, drug-naive SD in an unbiased way.

METHODS

A total of 25 patients with SD and 28 age-, sex-, and education-matched healthy controls underwent resting-state functional magnetic resonance imaging. Global-brain FC (GFC) was applied to analyze the images. Receiver operating characteristic curves and support vector machine were used to differentiate the patients from the controls.

RESULTS

Compared with healthy controls, patients with SD exhibited increased GFC in the right inferior temporal gyrus (t-value = 4.0663, p < 0.001) and left superior occipital gyrus (t-value = 3.8197, p < 0.001). Decreased GFC in the right insula (t-value = ‒4.1667, p < 0.001) was observed in the patients relative to the controls. The GFC values in the right insula of the patients were positively correlated to their scores of the sleep subscale of the Hamilton Depression Scale (r = 0.455, p = 0.022) and the lie subscale of the Eysenck Personality Questionnaire (r = 0.436, p = 0.029). A combination of GFC values in the right insula and left superior occipital gyrus can be applied to discriminate the patients from the controls with optimal sensitivity, specificity, and accuracy of 88.00%, 85.71%, and 86.79%, respectively.

CONCLUSIONS

Our study indicates that patients with SD show abnormal GFC in the brain areas of insula-centered sensorimotor network, and thus providing a new perspective for understanding the pathological changes of FC in SD. Furthermore, a combination of the GFC values in the right insula and left superior occipital gyrus may be used as a potential biomarker to identify the patients from the controls.

The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.

Contrasting Effects of Wild Arachis Dehydrin Under Abiotic and Biotic Stresses

Plant dehydrins (DNHs) belong to the LEA (Late Embryogenesis Abundant) protein family and are involved in responses to multiple abiotic stresses. DHNs are classified into five subclasses according to the organization of three conserved motifs (K-; Y-; and S-segments). In the present study, the DHN protein family was characterized by molecular phylogeny, exon/intron organization, protein structure, and tissue-specificity expression in eight Fabaceae species. We identified 20 DHN genes, encompassing three (YnSKn, SKn, and Kn) subclasses sharing similar gene organization and protein structure. Two additional low conserved DHN Φ-segments specific to the legume SKn-type of proteins were also found. The in silico expression patterns of DHN genes in four legume species (Arachis duranensis, A. ipaënsis, Glycine max, and Medicago truncatula) revealed that their tissue-specific regulation is associated with the presence or absence of the Y-segment. Indeed, DHN genes containing a Y-segment are mainly expressed in seeds, whereas those without the Y-segment are ubiquitously expressed. Further qRT-PCR analysis revealed that, amongst stress responsive dehydrins, a SKn-type DHN gene from A. duranensis (AdDHN1) showed opposite response to biotic and abiotic stress with a positive regulation under water deficit and negative regulation upon nematode infection. Furthermore, transgenic Arabidopsis lines overexpressing (OE) AdDHN1 displayed improved tolerance to multiple abiotic stresses (freezing and drought) but increased susceptibility to the biotrophic root-knot nematode (RKN) Meloidogyne incognita. This contradictory role of AdDHN1 in responses to abiotic and biotic stresses was further investigated by qRT-PCR analysis of transgenic plants using a set of stress-responsive genes involved in the abscisic acid (ABA) and jasmonic acid (JA) signaling pathways and suggested an involvement of DHN overexpression in these stress-signaling pathways.

Characteristics of a suicide attempt predict who makes another attempt after hospital discharge: A decision-tree investigation

The year following discharge from psychiatric hospitalization is a high-risk period for suicidal behavior, particularly among patients initially hospitalized after a suicide attempt. Demographic and clinical correlates have been identified; however, characteristics of the initial attempt may provide insight into risk for subsequent attempts as well. This investigation examined whether individual or a combination of suicide attempt characteristics predicted future attempts. Two hundred and eighteen psychiatric inpatients from the MacArthur Violence Risk Assessment Study with a recent suicide attempt were administered items from the Suicide Intent Scale and followed one year after discharge. Sixty-nine (31.65%) made a subsequent attempt. Data were analyzed by a stepwise logistic regression, followed by an iterative receiver operator curve (IROC) analysis, a recursive partitioning classification tree. The cross-validated IROC, but not logistic regression, predicted subsequent suicide attempts. Furthermore, the IROC found that participants who made definite plans and underwent extensive preparation were at highest risk for subsequent attempts. These findings suggest that suicide attempt characteristics preceding psychiatric hospitalization can help identify patients at elevated risk for another attempt post-discharge.

Differentiation of High-Grade Glioma and Intracranial Metastasis Using Volumetric Diffusion Tensor Imaging Tractography

OBJECTIVE

A reliable, noninvasive method to differentiate high-grade glioma (HGG) and intracranial metastasis (IM) has remained elusive. The aim of this study was to differentiate between HGG and IM using tumoral and peritumoral diffusion tensor imaging characteristics.

METHODS

A semiautomated script generated volumetric regions of interest (ROIs) for the tumor and a peritumoral shell at a predetermined voxel thickness. ROI differences in diffusion tensor imaging-related metrics between HGG and IM groups were estimated, including fractional anisotropy, mean diffusivity, total fiber tract counts, and tract density.

RESULTS

The HGG group (n = 46) had a significantly higher tumor-to-brain volume ratio than the IM group (n = 35) (P < 0.001). The HGG group exhibited significantly higher mean fractional anisotropy and significantly lower mean diffusivity within peritumoral ROI than the IM group (P < 0.05). The HGG group exhibited significantly higher total tract count and higher tract density in tumoral and peritumoral ROIs than the IM group (P < 0.05). Tumoral tract count and peritumoral tract density were the most optimal metrics to differentiate the groups based on receiver operating characteristic curve analysis. Predictive analysis using receiver operating characteristic curve thresholds was performed on 13 additional participants. Compared with correct clinical diagnoses, the 2 thresholds exhibited equal specificities (66.7%), but the tumoral tract count (85.7%) seemed more sensitive in differentiating the 2 groups.

CONCLUSIONS

Tract count and tract density were significantly different in tumoral and peritumoral regions between HGG and IM. Differences in microenvironmental interactions between the tumor types may cause these tract differences.

A browser-based tool for visualization and analysis of diffusion MRI data

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable-it runs in any web-browser-it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.

Concussion As a Multi-Scale Complex System: An Interdisciplinary Synthesis of Current Knowledge

Traumatic brain injury (TBI) has been called "the most complicated disease of the most complex organ of the body" and is an increasingly high-profile public health issue. Many patients report long-term impairments following even "mild" injuries, but reliable criteria for diagnosis and prognosis are lacking. Every clinical trial for TBI treatment to date has failed to demonstrate reliable and safe improvement in outcomes, and the existing body of literature is insufficient to support the creation of a new classification system. Concussion, or mild TBI, is a highly heterogeneous phenomenon, and numerous factors interact dynamically to influence an individual's recovery trajectory. Many of the obstacles faced in research and clinical practice related to TBI and concussion, including observed heterogeneity, arguably stem from the complexity of the condition itself. To improve understanding of this complexity, we review the current state of research through the lens provided by the interdisciplinary field of systems science, which has been increasingly applied to biomedical issues. The review was conducted iteratively, through multiple phases of literature review, expert interviews, and systems diagramming and represents the first phase in an effort to develop systems models of concussion. The primary focus of this work was to examine concepts and ways of thinking about concussion that currently impede research design and block advancements in care of TBI. Results are presented in the form of a multi-scale conceptual framework intended to synthesize knowledge across disciplines, improve research design, and provide a broader, multi-scale model for understanding concussion pathophysiology, classification, and treatment.