Towards modern post-coma care based on neuroscientific evidence

Background: Understanding the mechanisms underlying human consciousness is pivotal to improve the prognostication and treatment of severely brain-injured patients. Consciousness remains an elusive concept and the identification of its neural correlates is an active subject of research, however recent neuroscientific advances have allowed scientists to better characterize disorders of consciousness. These breakthroughs question the historical nomenclature and our current management of post-comatose patients. Method: This review examines the contribution of consciousness neurosciences to the current clinical management of severe brain injury. It investigates the major impact of consciousness disorders on healthcare systems, the scientific frameworks employed to identify their neural correlates and how evidence-based data from neuroimaging research have reshaped the landscape of post-coma care in recent years. Results: Our increased ability to detect behavioral and neurophysiological signatures of consciousness has led to significant changes in taxonomy and clinical practice. We advocate for a multimodal framework for the management of severely brain-injured patients based on precision medicine and evidence-based decisions, integrating epidemiology, health economics and neuroethics. Conclusions: Major progress in brain imaging and clinical assessment have opened the door to a new era of post-coma care based on standardized neuroscientific evidence. We highlight its implications in clinical applications and call for improved collaborations between researchers and clinicians to better translate findings to the bedside. (AU)

Estudio descriptivo de las alteraciones en pruebas de neuroimagen en pacientes con cefalea en un ámbito ambulatorio / Descriptive study of the alterations in neuroimaging tests in patients with headache in an outpatient setting

Introducción: Los estudios disponibles ofrecen una guía limitada acerca del uso de la neuroimagen en pacientes con cefalea en el ámbito ambulatorio. El objetivo de este estudio fue describir y analizar la frecuencia y el porcentaje de alteraciones en neuroimagen en pacientes valorados por cefalea en una consulta de neurología, así como determinar qué datos de alarma clínicos (red flags) se asocian con mayor frecuencia a estas alteraciones. Pacientes y métodos: Realizamos un estudio observacional, descriptivo y transversal de una serie de pacientes con cefalea de la consulta de neurología del Hospital Universitario de Móstoles a los que se les realizó una prueba de neuroimagen. ResultadosSe incluyó a 279 pacientes en el estudio (190 mujeres y 89 hombres). Ningún paciente sin datos de alarma clínicos presentó alteraciones graves en la neuroimagen y, de los que presentaban datos de alarma clínicos (219 pacientes), sólo el 2,3% (cinco pacientes) presentó alteraciones graves. Los dos datos de alarma clínicos que se asociaron de forma significativa a una mayor probabilidad de alteraciones graves en la neuroimagen fueron la cefalea que se desencadena o empeora significativamente con cambios posturales o maniobras de Valsalva y la cefalea crónica diaria desde su inicio. Conclusiones: La prevalencia de alteraciones significativas en neuroimagen en pacientes con cefalea en un ámbito ambulatorio fue baja, incluso en presencia de datos de alarma clínicos, por lo que la utilidad de estos datos de alarma parece ser menor en un contexto ambulatorio. Podría ser razonable intentar reducir el uso de la neuroimagen en pacientes con cefalea en el ámbito ambulatorio.(AU)

Introduction: Available studies provide limited guidance on the use of neuroimaging in patients with headache in the outpatient setting. The aim of this study was to describe and analyse the frequency and the percentage of neuroimaging abnormalities in patients evaluated for headache in a neurology clinic, as well as to determine which red flags are most commonly associated with these abnormalities. Patients and methods: We conducted an observational, descriptive and cross-sectional study of a series of patients with headache from the neurology department of the Hospital Universitario de Móstoles who underwent a neuroimaging test. Results: A total of 279 patients (190 women and 89 men) were included in the study. No patient without any red flags had severe abnormalities in the neuroimaging study and, of those with red flags (219 patients), only 2.3% (five patients) displayed severe abnormalities. The two red flags that were significantly associated with an increased likelihood of severe neuroimaging abnormalities were headache that is triggered or significantly worsened by postural changes or Valsalva manoeuvres and chronic daily headache since onset. Conclusions: The prevalence of significant neuroimaging abnormalities in headache patients in an outpatient setting was low, even in the presence of red flags, and so their utility appears to be lower in an outpatient setting. It may be reasonable to try to reduce the use of neuroimaging with headache patients in the outpatient setting.(AU)

Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network.

Alzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach-"fusion of end-to-end and transfer learning"-to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.

[Post-COVID-19 neuropsychological syndrome]. / Syndrome post-Covid-19 neuropsychologique.

Recent observations suggest the persistence of neurological and neuropsychological symptoms in the long-term following SARS-CoV-2 infection. Currently described within the post-COVID-19 syndrome. The objective of this article is to discuss recent epidemiological data and data from neuroimaging studies. Finally, a discussion is proposed regarding recent suggestions regarding the existence of distinct phenotypes of post-COVID-19 syndrome.

De récentes observations suggèrent la persistance de symptômes neurologiques et neuropsychologiques à long terme suite à une infection par le SARS-CoV-2, actuellement décrit au sein du syndrome post-Covid-19. L'objectif de cet article est d'aborder les récentes données épidémiologiques et les données provenant d'études en neuro-imagerie. Finalement, une discussion est proposée quant aux récentes suggestions concernant l'existence de phénotypes distincts au sein du syndrome post-Covid-19.

[Research on classification method of multimodal magnetic resonance images of Alzheimer's disease based on generalized convolutional neural networks].

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.

Automatic segmentation of vestibular schwannomas from T1-weighted MRI with a deep neural network.

BACKGROUND: Long-term follow-up using volumetric measurement could significantly assist in the management of vestibular schwannomas (VS). Manual segmentation of VS from MRI for treatment planning and follow-up assessment is labor-intensive and time-consuming. This study aims to develop a deep learning technique to fully automatically segment VS from MRI. METHODS: This study retrospectively analyzed MRI data of 737 patients who received gamma knife radiosurgery for VS. Treatment planning T1-weighted isotropic MR and manually contoured gross tumor volumes (GTV) were used for model development. A 3D convolutional neural network (CNN) was built on ResNet blocks. Spatial attenuation and deep supervision modules were integrated in each decoder level to enhance the training for the small tumor volume on brain MRI. The model was trained and tested on 587 and 150 patient data, respectively, from this institution (n = 495) and a publicly available dataset (n = 242). The model performance were assessed by the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), average symmetric surface (ASSD) and relative absolute volume difference (RAVD) of the model segmentation results against the GTVs. RESULTS: Measured on combined testing data from two institutions, the proposed method achieved mean DSC of 0.91 ± 0.08, ASSD of 0.3 ± 0.4 mm, HD95 of 1.3 ± 1.6 mm, and RAVD of 0.09 ± 0.15. The DSCs were 0.91 ± 0.09 and 0.92 ± 0.06 on 100 testing patients of this institution and 50 of the public data, respectively. CONCLUSIONS: A CNN model was developed for fully automated segmentation of VS on T1-Weighted isotropic MRI. The model achieved good performance compared with physician clinical delineations on a sizeable dataset from two institutions. The proposed method potentially facilitates clinical workflow of radiosurgery for VS patient management.

Hypomagnesemia, a Rare Cause of Reversible Ataxia.

Background: A 61-year-old male patient presented with cerebellar syndrome, which had progressively worsened for 10 days, followed by a tonic-clonic seizure. Phenomenology Shown: Blood analysis showed severe hypomagnesemia and a brain MRI showed T2 hyperintensity in the cerebellar hemispheres (Figure 1). Therefore, the final diagnosis was cerebellar syndrome and epileptic seizures secondary to severe hypomagnesemia. Educational Value: In cases of subacute onset of ataxia, the possibility of ataxia secondary to hypomagnesemia should be considered, as it can be diagnosed with a basic blood test and there are potentially life-threatening outcomes in the absence of treatment, with a reversible course following early supplementation.

In vivo whole-brain imaging of zebrafish larvae using three-dimensional fluorescence microscopy.

As a vertebrate model animal, larval zebrafish are widely used in neuroscience and provide a unique opportunity to monitor whole-brain activity at the cellular resolution. Here, we provide an optimized protocol for performing whole-brain imaging of larval zebrafish using three-dimensional fluorescence microscopy, including sample preparation and immobilization, sample embedding, image acquisition, and visualization after imaging. The current protocol enables in vivo imaging of the structure and neuronal activity of a larval zebrafish brain at a cellular resolution for over 1 h using confocal microscopy and custom-designed fluorescence microscopy. The critical steps in the protocol are also discussed, including sample mounting and positioning, preventing bubble formation and dust in the agarose gel, and avoiding motion in images caused by incomplete solidification of the agarose gel and paralyzation of the fish. The protocol has been validated and confirmed in multiple settings. This protocol can be easily adapted for imaging other organs of a larval zebrafish.

Atypical and isolated posterior reversible encephalopathy syndrome in postpartum without preeclampsia: a case report.

Posterior reversible encephalopathy syndrome (PRES) is a neurological complication frequently found during brain exploration for severe preeclampsia when it is associated with neurological signs. Being a newly discovered entity, its mechanism of genesis is still based on a hypothesis not yet confirmed. The clinical case that we report highlights an atypical PRES syndrome occurring in postpartum without any signs of preeclampsia. The patient had suffered a state of convulsive dysfunction after delivery without hypertension and the diagnosis of PRES syndrome was confirmed based on the results of the brain computed tomography (CT) scan, she showed signs of clinical improvement on the fifth day of postpartum. Our case report calls into question the association between PRES syndrome and preeclampsia that we find in literature and puts a big question mark on the causal link between the two in pregnant women.

[Neuroimaging of cerebral small vessel disease in mild cognitive impairment in older adults.]

Cerebral small vessel disease (CSVD) - is a clinical and radiological phenomenon characteristic of older adults. Currently, the extent of white matter lesions (WML) in patients with moderate cognitive disorders remains uncertain. Also, the relationship of cognitive impairment with the volume of WML has not been sufficiently studied. The aim of the study was to analyze the WML volumes in patients with subcortical vascular mild cognitive impairment (svMCI) and in the control group according to magnetic resonance imaging (MRI). The study included 50 people: 25 patients with svMCI (average age 75,88±4,04 years) and 25 conditionally healthy volunteers (average age 69,96±3,07 years). Significant differences in the volume of WML between the study groups were obtained. The fraction of hypointense WML was 0,74±0,41 in patients with svMCI and 0,15±0,07 in the control group. In the correlation analysis in the svMCI group, only the function of mental control showed a negative relationship with the fraction of WML. The data obtained suggest that the assessment of the volume of WML is important in patients with svMCI, but does not fully explain the decline in cognitive functions.

Brain imaging and morphological plasticity in glioblastoma: a literature review.

This article provides a comprehensive review of the role of functional magnetic resonance imaging (fMRI) in characterizing neural plasticity in glioblastoma patients. Glioblastoma, the most common primary brain tumor, has a rapid growth rate and infiltrative nature that leads to the disorganization of the normal brain network. Neuroplasticity, still not fully understood, is the foundation for the development of brain functions during the growth and recovery of certain brain functions after a brain lesion such as a tumor, trauma, or vascular event. Functional MRI has the capacity to identify the regions that activate at rest or when performing a task. It can determine the extent to which these regions, responsible for a specific function, are impacted by a tumor and eventually after surgical excision. Likewise, it can help evaluate to which extent activation changes when recovery of function occurs. In this article, we aimed to understand the significance of fMRI in the management of glioblastoma by analyzing representative articles from the literature.

Mapping human brain charts cross-sectionally and longitudinally.

Brain scans acquired across large, age-diverse cohorts have facilitated recent progress in establishing normative brain aging charts. Here, we ask the critical question of whether cross-sectional estimates of age-related brain trajectories resemble those directly measured from longitudinal data. We show that age-related brain changes inferred from cross-sectionally mapped brain charts can substantially underestimate actual changes measured longitudinally. We further find that brain aging trajectories vary markedly between individuals and are difficult to predict with population-level age trends estimated cross-sectionally. Prediction errors relate modestly to neuroimaging confounds and lifestyle factors. Our findings provide explicit evidence for the importance of longitudinal measurements in ascertaining brain development and aging trajectories.

Neuroimaging Technology in Exercise Neurorehabilitation Research in Persons with MS: A Scoping Review.

There is increasing interest in the application of neuroimaging technology in exercise neurorehabilitation research among persons with multiple sclerosis (MS). The inclusion and focus on neuroimaging outcomes in MS exercise training research is critical for establishing a biological basis for improvements in functioning and elevating exercise within the neurologist's clinical armamentarium alongside disease modifying therapies as an approach for treating the disease and its consequences. Indeed, the inclusion of selective neuroimaging approaches and sensor-based technology among physical activity, mobility, and balance outcomes in such MS research might further allow for detecting specific links between the brain and real-world behavior. This paper provided a scoping review on the application of neuroimaging in exercise training research among persons with MS based on searches conducted in PubMed, Web of Science, and Scopus. We identified 60 studies on neuroimaging-technology-based (primarily MRI, which involved a variety of sequences and approaches) correlates of functions, based on multiple sensor-based measures, which are typically targets for exercise training trials in MS. We further identified 12 randomized controlled trials of exercise training effects on neuroimaging outcomes in MS. Overall, there was a large degree of heterogeneity whereby we could not identify definitive conclusions regarding a consistent neuroimaging biomarker of MS-related dysfunction or singular sensor-based measure, or consistent neural adaptation for exercise training in MS. Nevertheless, the present review provides a first step for better linking correlational and randomized controlled trial research for the development of high-quality exercise training studies on the brain in persons with MS, and this is timely given the substantial interest in exercise as a potential disease-modifying and/or neuroplasticity-inducing behavior in this population.

Mouse Cardiac Arrest Model for Brain Imaging and Brain Physiology Monitoring During Ischemia and Resuscitation.

Most cardiac arrest (CA) survivors experience varying degrees of neurologic deficits. To understand the mechanisms that underpin CA-induced brain injury and, subsequently, develop effective treatments, experimental CA research is essential. To this end, a few mouse CA models have been established. In most of these models, the mice are placed in the supine position in order to perform chest compression for cardiopulmonary resuscitation (CPR). However, this resuscitation procedure makes the real-time imaging/monitoring of brain physiology during CA and resuscitation challenging. To obtain such critical knowledge, the present protocol presents a mouse asphyxia CA model that does not require the chest compression CPR step. This model allows for the study of dynamic changes in blood flow, vascular structure, electrical potentials, and brain tissue oxygen from the pre-CA baseline to early post-CA reperfusion. Importantly, this model applies to aged mice. Thus, this mouse CA model is expected to be a critical tool for deciphering the impact of CA on brain physiology.

Incidental Meningiomas: Potential Predictors of Growth and Current State of Management.

The rise in availability of neuroimaging has led to an increase in incidentally discovered meningiomas. These tumors are typically asymptomatic and tend to display slow growth. Treatment options include observation with serial monitoring, radiation, and surgery. Although optimal management is unclear, clinicians recommend a conservative approach, which preserves quality of life and limits unnecessary intervention. Several risk factors have been investigated for their potential utility in the development of prognostic models for risk assessment. Herein, the authors review the current literature on incidental meningiomas, focusing their discussion on potential predictive factors for tumor growth and appropriate management practices.

Neuroimaging of central nervous system tuberculosis.

A 20-month-old female, not immunized with Bacillus Calmette-Guérin (BCG) vaccine, was admitted due to a four-day history of fever and cough. In the past three months, she presented respiratory infections, weight loss and enlarged cervical lymph nodes. On day two of admission, she displayed drowsiness and positive Romberg's sign; cerebrospinal fluid (CSF) workout revealed 107/ul cells, low glucose and high protein levels. Ceftriaxone and acyclovir were initiated, and she was transferred to our tertiary hospital. Brain magnetic resonance imaging showed punctiform focal areas of restricted diffusion in left capsular lenticular region suggestive of vasculitis secondary to infection. Tuberculin skin test and interferon-gamma release assay were positive. She started tuberculostatic therapy, but two days later she presented tonic-clonic seizures and impaired consciousness. Cerebral computed tomography (CT) revealed tetrahydrocephalus (Figure 1), needing external ventricular derivation. She had a slow clinical improvement, requiring several neurosurgical interventions and developing a syndrome of inappropriate antidiuretic secretion alternating with cerebral salt wasting. Positive results for Mycobacterium tuberculosis were obtained by CSF culture and by polymerase chain reaction in CSF, bronchoalveolar lavage and gastric aspirate specimens. Repeated brain CT showed a large-vessel vasculitis with basal meningeal enhancement, typical of central nervous system (CNS) tuberculosis (Figure 2). She completed one month of corticosteroids and maintained antituberculosis treatment. At two years of age, she has spastic paraparesis and no language skills. Portugal had 1836 cases of tuberculosis (17.8 per 100000) in 2016 and was considered a low-incidence country; consequently, BCG vaccination is not universal (1). We present a severe case of CNS tuberculosis with intracranial hypertension, vasculitis and hyponatremia, associated with poorer outcomes (2). A high index of suspicion allowed prompt start of antituberculosis treatment. Diagnosis was corroborated by microbiological positivity and a typical triad in neuroimaging (hydrocephalus, vasculitis and basal meningeal enhancement) (3), which we wish to emphasize.

Visual review of acute stroke neuroimaging prior to transfer acceptance increases likelihood of endovascular therapy.

OBJECTIVES: Demand for thrombectomy, and interhospital transfer to comprehensive stroke centers (CSCs), for acute stroke is increasing. There is an urgent need to identify patients most likely to benefit from transfer. We evaluated whether CSC providers' review of neuroimaging prior to transfer acceptance improved patient selection for thrombectomy and correlated with higher rates of treatment. MATERIALS AND METHODS: A retrospective database of all patients transferred to Stanford's CSC for thrombectomy between 2015-2019 was used. Pre-acceptance images, when available for visual review, were reviewed by the CSC stroke team via virtual PACS, RAPID software, or LifeImage platforms. RESULTS: 525 patients met inclusion criteria. 147 (28%) had neuroimaging available for review prior to transfer. Of those who did not recanalize en route, 267 (50.8%) underwent thrombectomy. Patients with imaging available for review prior to acceptance were significantly more likely to receive thrombectomy (68% vs 54%, RR 1.26; p=0.006, 95% CI 1.09-1.48). Patient images that were reviewed via RAPID were CT-based perfusion studies; these were more likely to receive thrombectomy (70% vs 54%, RR 1.30; p=0.01, 1.09-1.56). Patients who received EVT were more likely to have had pre-transfer vessel imaging, regardless of availability for visual review (76% vs 59%, RR 1.44; p<0.001, 1.18-1.76). CONCLUSIONS: Patients with concern for acute stroke transferred for consideration of thrombectomy who had neuroimaging visually reviewed prior to transfer acceptance and did not recanalize by time of arrival were significantly more likely to undergo thrombectomy. Additional prospective studies are needed to confirm our findings.

Association of Body Mass Index and Waist Circumference With Imaging Metrics of Brain Integrity and Functional Connectivity in Children Aged 9 to 10 Years in the US, 2016-2018.

Importance: Aside from widely known cardiovascular implications, higher weight in children may have negative associations with brain microstructure and neurodevelopment. Objective: To evaluate the association of body mass index (BMI) and waist circumference with imaging metrics that approximate brain health. Design, Setting, and Participants: This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) study to examine the association of BMI and waist circumference with multimodal neuroimaging metrics of brain health in cross-sectional and longitudinal analyses over 2 years. From 2016 to 2018, the multicenter ABCD study recruited more than 11 000 demographically representative children aged 9 to 10 years in the US. Children without any history of neurodevelopmental or psychiatric disorders were included in this study, and a subsample of children who completed 2-year follow-up (34%) was included for longitudinal analysis. Exposures: Children's weight, height, waist circumference, age, sex, race and ethnicity, socioeconomic status, handedness, puberty status, and magnetic resonance imaging scanner device were retrieved and included in the analysis. Main Outcomes and Measures: Association of preadolescents' BMI z scores and waist circumference with neuroimaging indicators of brain health: cortical morphometry, resting-state functional connectivity, and white matter microstructure and cytostructure. Results: A total of 4576 children (2208 [48.3%] female) at a mean (SD) age of 10.0 years (7.6 months) were included in the baseline cross-sectional analysis. There were 609 (13.3%) Black, 925 (20.2%) Hispanic, and 2565 (56.1%) White participants. Of those, 1567 had complete 2-year clinical and imaging information at a mean (SD) age of 12.0 years (7.7 months). In cross-sectional analyses at both time points, higher BMI and waist circumference were associated with lower microstructural integrity and neurite density, most pronounced in the corpus callosum (fractional anisotropy for BMI and waist circumference at baseline and second year: P < .001; neurite density for BMI at baseline: P < .001; neurite density for waist circumference at baseline: P = .09; neurite density for BMI at second year: P = .002; neurite density for waist circumference at second year: P = .05), reduced functional connectivity in reward- and control-related networks (eg, within the salience network for BMI and waist circumference at baseline and second year: P < .002), and thinner brain cortex (eg, for the right rostral middle frontal for BMI and waist circumference at baseline and second year: P < .001). In longitudinal analysis, higher baseline BMI was most strongly associated with decelerated interval development of the prefrontal cortex (left rostral middle frontal: P = .003) and microstructure and cytostructure of the corpus callosum (fractional anisotropy: P = .01; neurite density: P = .02). Conclusions and Relevance: In this cross-sectional study, higher BMI and waist circumference among children aged 9 to 10 years were associated with imaging metrics of poorer brain structure and connectivity as well as hindered interval development. Future follow-up data from the ABCD study can reveal long-term neurocognitive implications of excess childhood weight. Imaging metrics that had the strongest association with BMI and waist circumference in this population-level analysis may serve as target biomarkers of brain integrity in future treatment trials of childhood obesity.