JOSA: Joint surface-based registration and atlas construction of brain geometry and function

Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding areas across subjects has been widely reported, particularly in higher-order cognitive areas. In this work, we present JOSA, a novel cortical registration framework that jointly models the mismatch between geometry and function while simultaneously learning an unbiased population-specific atlas. Using a semi-supervised training strategy, JOSA achieves superior registration performance in both geometry and function to the state-of-the-art methods but without requiring functional data at inference. This learning framework can be extended to any auxiliary data to guide spherical registration that is available during training but is difficult or impossible to obtain during inference, such as parcellations, architectonic identity, transcriptomic information, and molecular profiles. By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of the brain structure and function.Comment: A. V. Dalca and B. Fischl are co-senior authors with equal contribution. arXiv admin note: text overlap with arXiv:2303.0159

The Brainstem in Emotion: A Review

Emotions depend upon the integrated activity of neural networks that modulate arousal, autonomic function, motor control, and somatosensation. Brainstem nodes play critical roles in each of these networks, but prior studies of the neuroanatomic basis of emotion, particularly in the human neuropsychological literature, have mostly focused on the contributions of cortical rather than subcortical structures. Given the size and complexity of brainstem circuits, elucidating their structural and functional properties involves technical challenges. However, recent advances in neuroimaging have begun to accelerate research into the brainstem’s role in emotion. In this review, we provide a conceptual framework for neuroscience, psychology and behavioral science researchers to study brainstem involvement in human emotions. The “emotional brainstem” is comprised of three major networks – Ascending, Descending and Modulatory. The Ascending network is composed chiefly of the spinothalamic tracts and their projections to brainstem nuclei, which transmit sensory information from the body to rostral structures. The Descending motor network is subdivided into medial projections from the reticular formation that modulate the gain of inputs impacting emotional salience, and lateral projections from the periaqueductal gray, hypothalamus and amygdala that activate characteristic emotional behaviors. Finally, the brainstem is home to a group of modulatory neurotransmitter pathways, such as those arising from the raphe nuclei (serotonergic), ventral tegmental area (dopaminergic) and locus coeruleus (noradrenergic), which form a Modulatory network that coordinates interactions between the Ascending and Descending networks. Integration of signaling within these three networks occurs at all levels of the brainstem, with progressively more complex forms of integration occurring in the hypothalamus and thalamus. These intermediary structures, in turn, provide input for the most complex integrations, which occur in the frontal, insular, cingulate and other regions of the cerebral cortex. Phylogenetically older brainstem networks inform the functioning of evolutionarily newer rostral regions, which in turn regulate and modulate the older structures. Via these bidirectional interactions, the human brainstem contributes to the evaluation of sensory information and triggers fixed-action pattern responses that together constitute the finely differentiated spectrum of possible emotions

Common Data Elements for COVID-19 Neuroimaging: A GCS-NeuroCOVID Proposal

Funder: National Institute of Neurological Disorders and Stroke; doi: http://dx.doi.org/10.13039/100000065Funder: James S. McDonnell Foundation; doi: http://dx.doi.org/10.13039/100000913Funder: National Institute of Health ResearchFunder: United Kingdom Research and InnovationFunder: Addenbrooke’s Charitable Trus

Correction: Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Neuroimaging.

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Diagnosing Level of Consciousness: Limits of the Glasgow Coma Scale Total Score

In nearly all clinical and research contexts, the initial severity of a traumatic brain injury (TBI) is measured using the Glasgow Coma Scale (GCS) total score. The GCS total score however, may not accurately reflect level of consciousness, a critical indicator of injury severity. We investigated the relationship between GCS total scores and level of consciousness in a consecutive sample of 2455 adult subjects assessed with the GCS 69,487 times as part of the multi-center Transforming Research and Clinical Knowledge in TBI (TRACKTBI) study. We assigned each GCS subscale score combination a level of consciousness rating based on published criteria for the following disorders of consciousness (DoC) diagnoses: coma, vegetative state/ unresponsive wakefulness syndrome, minimally conscious state, and post-traumatic confusional state, and present our findings using summary statistics and four illustrative cases. Participants had the following characteristics: mean (standard deviation) age 41.9 (17.6) years, 69% male, initial GCS 3–8 = 13%; 9–12 = 5%; 13–15 = 82%. All GCS total scores between 4–14 were associated with more than one DoC diagnosis; the greatest variability was observed for scores of 7–11. Further, a wide range of total scores was associated with identical DoC diagnoses. Importantly, a diagnosis of coma was only possible with GCS total scores of 3–6. The GCS total score does not accurately reflect level of consciousness based on published DoC diagnostic criteria. To improve the classification of patients with TBI and to inform the design of future clinical trials, clinicians and investigators should consider individual subscale behaviors and more comprehensive assessments when evaluating TBI severityTRACK-TB

Multimodal characterization of the late effects of traumatic brain injury: a methodological overview of the Late Effects of Traumatic Brain Injury Project

Epidemiological studies suggest that a single moderate-to-severe traumatic brain injury (TBI) is associated with an increased risk of neurodegenerative disease, including Alzheimer’s and Parkinson’s disease (AD and PD). Histopathological studies describe complex neurodegenerative pathologies in individuals exposed to single moderate-to-severe TBI or repetitive mild TBI, including chronic traumatic encephalopathy (CTE). However, the clinicopathological links between TBI and post-traumatic neurodegenerative diseases such as AD, PD, and CTE remain poorly understood. Here we describe the methodology of the Late Effects of TBI (LETBI) study, whose goals are to characterize chronic post-traumatic neuropathology and to identify in vivo biomarkers of post-traumatic neurodegeneration. LETBI participants undergo extensive clinical evaluation using National Institutes of Health TBI Common Data Elements, proteomic and genomic analysis, structural and functional MRI, and prospective consent for brain donation. Selected brain specimens undergo ultra-high resolution ex vivo MRI and histopathological evaluation including whole mount analysis. Co-registration of ex vivo and in vivo MRI data enables identification of ex vivo lesions that were present during life. In vivo signatures of postmortem pathology are then correlated with cognitive and behavioral data to characterize the clinical phenotype(s) associated with pathological brain lesions. We illustrate the study methods and demonstrate proof of concept for this approach by reporting results from the first LETBI participant, who despite the presence of multiple in vivo and ex vivo pathoanatomic lesions had normal cognition and was functionally independent until her mid-80s. The LETBI project represents a multidisciplinary effort to characterize post-traumatic neuropathology and identify in vivo signatures of postmortem pathology in a prospective study

Predicting Long-Term Recovery of Consciousness in Prolonged Disorders of Consciousness Based on Coma Recovery Scale-Revised Subscores: Validation of a Machine Learning-Based Prognostic Index.

peer reviewedPrognosis of prolonged Disorders of Consciousness (pDoC) is influenced by patients' clinical diagnosis and Coma Recovery Scale-Revised (CRS-R) total score. We compared the prognostic accuracy of a novel Consciousness Domain Index (CDI) with that of clinical diagnosis and CRS-R total score, for recovery of full consciousness at 6-, 12-, and 24-months post-injury. The CDI was obtained by a combination of the six CRS-R subscales via an unsupervised machine learning technique. We retrospectively analyzed data on 143 patients with pDoC (75 in Minimally Conscious State; 102 males; median age = 53 years; IQR = 35; time post-injury = 1-3 months) due to different etiologies enrolled in an International Brain Injury Association Disorders of Consciousness Special Interest Group (IBIA DoC-SIG) multicenter longitudinal study. Univariate and multivariate analyses were utilized to assess the association between outcomes and the CDI, compared to clinical diagnosis and CRS-R. The CDI, the clinical diagnosis, and the CRS-R total score were significantly associated with a good outcome at 6, 12 and 24 months. The CDI showed the highest univariate prediction accuracy and sensitivity, and regression models including the CDI provided the highest values of explained variance. A combined scoring system of the CRS-R subscales by unsupervised machine learning may improve clinical ability to predict recovery of consciousness in patients with pDoC

Risk factors for 2-year mortality in patients with prolonged disorders of consciousness: An international multicentre study.

peer reviewedBACKGROUND AND PURPOSE: Patients with prolonged disorders of consciousness (pDoC) have a high mortality rate due to medical complications. Because an accurate prognosis is essential for decision-making on patients' management, we analysed data from an international multicentre prospective cohort study to evaluate 2-year mortality rate and bedside predictors of mortality. METHODS: We enrolled adult patients in prolonged vegetative state/unresponsive wakefulness syndrome (VS/UWS) or minimally conscious state (MCS) after traumatic and nontraumatic brain injury within 3 months postinjury. At enrolment, we collected demographic (age, sex), anamnestic (aetiology, time postinjury), clinical (Coma Recovery Scale-Revised [CRS-R], Disability Rating Scale, Nociception Coma Scale-Revised), and neurophysiologic (electroencephalogram [EEG], somatosensory evoked and event-related potentials) data. Patients were followed up to gather data on mortality up to 24 months postinjury. RESULTS: Among 143 traumatic (n = 55) and nontraumatic (n = 88) patients (VS/UWS, n = 68, 19 females; MCS, n = 75, 22 females), 41 (28.7%) died within 24 months postinjury. Mortality rate was higher in VS/UWS (42.6%) than in MCS (16%; p < 0.001). Multivariate regression in VS/UWS showed that significant predictors of mortality were older age and lower CRS-R total score, whereas in MCS female sex and absence of alpha rhythm on EEG at study entry were significant predictors. CONCLUSIONS: This study demonstrated that a feasible multimodal assessment in the postacute phase can help clinicians to identify patients with pDoC at higher risk of mortality within 24 months after brain injury. This evidence can help clinicians and patients' families to navigate the complex clinical decision-making process and promote an international standardization of prognostic procedures for patients with pDoC

Multicenter prospective study on predictors of short-term outcome in disorders of consciousness

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