Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning.

Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T1-T2∗-weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.

Increased MRI-based Brain Age in chronic migraine patients.

INTRODUCTION: Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants. METHODS: We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74). RESULTS: CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine. CONCLUSION: The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Randomised placebo-controlled clinical trial evaluating the impact of a new visual rehabilitation program on neuroadaptation in patients implanted with trifocal intraocular lenses.

PURPOSE: To evaluate the efficacy of a new visual training program for improving the visual function in patients implanted with trifocal intraocular lenses (IOLs). METHODS: Randomised placebo-controlled clinical trial enrolling 60 subjects (age, 47-75 years) undergoing cataract surgery with implantation of trifocal diffractive IOL. Home-based active visual training was prescribed immediately after surgery to all of them (20 sessions, 30 min): 31 subjects using a serious game based on Gabor patches (study group) and 29 using a placebo software (placebo group). Visual acuity, contrast sensitivity (CS), and perception of visual disturbances (QoV questionnaire) were evaluated before and after training. Likewise, in a small subgroup, resting-state functional magnetic resonance imaging (rs-fMRI) analysis was performed. RESULTS: No significant differences were found between groups in compliance time (p = 0.70). After training, only significant improvements in monocular uncorrected intermediate visual acuity were found in the study group (p ≤ 0.01), although differences between groups did not reach statistical significance (p ≥ 0.11). Likewise, significantly better binocular far CS values were found in the study group for the spatial frequencies of 6 (p = 0.01) and 12 cpd (p = 0.03). More visual symptoms of the QoV questionnaire experienced a significant change in the level of bothersomeness in the study group. Rs-fMRI revealed the presence significant changes reflecting higher functional connectivity after the training with the serious game. CONCLUSIONS: A 3-week visual training program based on the use of Gabor patches after bilateral implantation of trifocal diffractive IOLs may be beneficial for optimising the visual function, with neural changes associated suggesting an acceleration of neuroadaptation. Trial registration ClinicalTrials.gov, NCT04985097. Registered 02 August 2021, https://clinicaltrials.gov/(NCT04985097 ).

Viability of AMURA biomarkers from single-shell diffusion MRI in clinical studies.

Diffusion Tensor Imaging (DTI) is the most employed method to assess white matter properties using quantitative parameters derived from diffusion MRI, but it presents known limitations that restrict the evaluation of complex structures. The objective of this study was to validate the reliability and robustness of complementary diffusion measures extracted with a novel approach, Apparent Measures Using Reduced Acquisitions (AMURA), with a typical diffusion MRI acquisition from a clinical context in comparison with DTI with application to clinical studies. Fifty healthy controls, 51 episodic migraine and 56 chronic migraine patients underwent single-shell diffusion MRI. Four DTI-based and eight AMURA-based parameters were compared between groups with tract-based spatial statistics to establish reference results. On the other hand, following a region-based analysis, the measures were assessed for multiple subsamples with diverse reduced sample sizes and their stability was evaluated with the coefficient of quartile variation. To assess the discrimination power of the diffusion measures, we repeated the statistical comparisons with a region-based analysis employing reduced sample sizes with diverse subsets, decreasing 10 subjects per group for consecutive reductions, and using 5,001 different random subsamples. For each sample size, the stability of the diffusion descriptors was evaluated with the coefficient of quartile variation. AMURA measures showed a greater number of statistically significant differences in the reference comparisons between episodic migraine patients and controls compared to DTI. In contrast, a higher number of differences was found with DTI parameters compared to AMURA in the comparisons between both migraine groups. Regarding the assessments reducing the sample size, the AMURA parameters showed a more stable behavior than DTI, showing a lower decrease for each reduced sample size or a higher number of regions with significant differences. However, most AMURA parameters showed lower stability in relation to higher coefficient of quartile variation values than the DTI descriptors, although two AMURA measures showed similar values to DTI. For the synthetic signals, there were AMURA measures with similar quantification to DTI, while other showed similar behavior. These findings suggest that AMURA presents favorable characteristics to identify differences of specific microstructural properties between clinical groups in regions with complex fiber architecture and lower dependency on the sample size or assessing technique than DTI.

Structural brain changes in patients with persistent headache after COVID-19 resolution.

Headache is among the most frequently reported symptoms after resolution of COVID-19. We assessed structural brain changes using T1- and diffusion-weighted MRI processed data from 167 subjects: 40 patients who recovered from COVID-19 but suffered from persistent headache without prior history of headache (COV), 41 healthy controls, 43 patients with episodic migraine and 43 patients with chronic migraine. To evaluate gray matter and white matter changes, morphometry parameters and diffusion tensor imaging-based measures were employed, respectively. COV patients showed significant lower cortical gray matter volume and cortical thickness than healthy subjects (p < 0.05, false discovery rate corrected) in the inferior frontal and the fusiform cortex. Lower fractional anisotropy and higher radial diffusivity (p < 0.05, family-wise error corrected) were observed in COV patients compared to controls, mainly in the corpus callosum and left hemisphere. COV patients showed higher cortical volume and thickness than migraine patients in the cingulate and frontal gyri, paracentral lobule and superior temporal sulcus, lower volume in subcortical regions and lower curvature in the precuneus and cuneus. Lower diffusion metric values in COV patients compared to migraine were identified prominently in the right hemisphere. COV patients present diverse changes in the white matter and gray matter structure. White matter changes seem to be associated with impairment of fiber bundles. Besides, the gray matter changes and other white matter modifications such as axonal integrity loss seemed subtle and less pronounced than those detected in migraine, showing that persistent headache after COVID-19 resolution could be an intermediate state between normality and migraine.

Default mode network components and its relationship with anomalous self-experiences in schizophrenia: A rs-fMRI exploratory study.

Anomalous self-experiences (ASEs) in schizophrenia have been under research for the last 20 years. However, no neuroimage studies have provided insight of the possible biological underpinning of ASEs. In this novel approach, the connectivity within the default mode network, calculated through a ROI-based analysis of functional magnetic resonance imaging data, was correlated to the ASEs scores assessed by the Inventory of Psychotic-Like Anomalous Self-Experiences (IPASE) in a sample of 22 schizophrenia patients. The Pearson's correlation coefficients between IPASE scores and intrahemispheric connectivity of the parahippocampal gyrus with the isthmus cingulate cortex in both hemispheres, and right parahippocampal gyrus with the right rostral anterior cingulate cortex were positive and significant suggesting a relation between hyperactive functional connectivity and anomalous self-experiences intensity. Prior literature reported these areas to have a role in self-processing and consciousness as well as being anatomically connected. Further research with larger sample size and comparison with controls are needed to confirm the relationship of this connectivity with anomalous self-experiences.

Moment-based representation of the diffusion inside the brain from reduced DMRI acquisitions: Generalized AMURA.

AMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in diffusion MRI. It reduces the number of samples needed and the computational complexity of the estimation of diffusion properties of tissues by assuming the diffusion anisotropy is roughly independent on the b-value. This simplification allows the computation of simplified expressions and makes it compatible with standard acquisition protocols commonly used even in clinical practice. The present work proposes an extension of AMURA that allows the calculation of general moments of the diffusion signals that can be applied to describe the diffusion process with higher accuracy. We provide simplified expressions to analytically compute a set of scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The existing metrics previously proposed for AMURA (RTOP, RTPP and RTAP) are now special cases of this generalization. An extensive set of experiments is performed on public data and a clinical clase acquired with a standard type acquisition. The new metrics provide additional information about the diffusion processes inside the brain.

Psychological symptoms of the outbreak of the COVID-19 confinement in Spain.

We studied the short-term psychological effects of the COVID-19 crisis and the quarantine on 3550 adults from the Spanish population in a cross-sectional survey. Symptoms of anxiety, depression, and stress were analyzed using the 21-item version of the Depression Anxiety Stress Scale. Symptoms of posttraumatic stress disorder were analyzed using the Impact of Event Scale. Symptomatic scores of anxiety, depression, and stress were observed in 20% to 30% of respondents. Symptomatic scores indicating psychological stress were found in 47.5% of respondents. Similar to the findings of other multiple studies, confinement has been found to have significant emotional impact in the Spanish population.

Perceived quality of life (QOLIE-31-P), depression (NDDI-E), anxiety (GAD-7), and insomnia in patients with epilepsy attended at a refractory epilepsy unit in real-life clinical practice.

OBJECTIVES: This study aims to evaluate the relationship between psychiatric comorbidity (anxiety and depression), somnolence, and quality of life, using validated scales in patients with epilepsy in real-life clinical practice and clinical and demographic variables. METHODS: A cross-sectional observational study was conducted. Self-administered scales of anxiety disorders (GAD-7), depression (NDDI-E), somnolence (Epworth Sleepiness Scale (ESS)), and quality of life (QOLIE-31-P) in patients with epilepsy treated in the refractory epilepsy unit of a tertiary hospital were employed. RESULTS: Eighty-four patients, 44.3 ± 17.4 years, 48.2% women, epilepsy duration 21.5 ± 15.9 years, and number of antiepileptic drugs 1.9 ± 1.2 were included. Severe anxiety was present in 14.3%, depression in 20.2%, and somnolence in 14.3% of patients. QOLIE-31-P score was 62.0 ± 19.2. Depression and focal epilepsy (OR = 4.5[1.3, 20.7], p = 0.029), as well as anxiety and temporal lobe epilepsy (OR = 4.3 [1.0, 18.1], p = 0.044), were associated. Moreover, relationships between worse quality of life and higher scores from NDDI-E (ß = - 1.42, adjusted p = 0.006) and GAD-7 (ß = - 1.21, adjusted p = 0.006), especially in drug-resistant epilepsy (ß = - 8.08, adjusted p = 0.045) and female sex (ß = - 7.83, adjusted p = 0.034), were identified. Statistically significant negative associations were observed between problems to fall asleep and overall quality of life score (ß = - 11.64, adjusted p = 0.022), sleep disturbance and energy (ß = - 14.78, adjusted p = 0.027), and mood (ß = 12.40, adjusted p = 0.027) scores. CONCLUSIONS: The multidimensional evaluation revealed that higher levels of anxiety and depression are associated with worse quality of life in real clinical practice in patients with epilepsy, especially in females and drug-resistant epilepsy. In addition, sleep disturbances are associated with particular aspects of the quality of life. Further studies with longitudinal follow-up would be useful to adequately manage these comorbidities in patients with epilepsy.

Search for schizophrenia and bipolar biotypes using functional network properties.

INTRODUCTION: Recent studies support the identification of valid subtypes within schizophrenia and bipolar disorder using cluster analysis. Our aim was to identify meaningful biotypes of psychosis based on network properties of the electroencephalogram. We hypothesized that these parameters would be more altered in a subgroup of patients also characterized by more severe deficits in other clinical, cognitive, and biological measurements. METHODS: A clustering analysis was performed using the electroencephalogram-based network parameters derived from graph-theory obtained during a P300 task of 137 schizophrenia (of them, 35 first episodes) and 46 bipolar patients. Both prestimulus and modulation of the electroencephalogram were included in the analysis. Demographic, clinical, cognitive, structural cerebral data, and the modulation of the spectral entropy of the electroencephalogram were compared between clusters. Data from 158 healthy controls were included for further comparisons. RESULTS: We identified two clusters of patients. One cluster presented higher prestimulus connectivity strength, clustering coefficient, path-length, and lower small-world index compared to controls. The modulation of clustering coefficient and path-length parameters was smaller in the former cluster, which also showed an altered structural connectivity network and a widespread cortical thinning. The other cluster of patients did not show significant differences with controls in the functional network properties. No significant differences were found between patients´ clusters in first episodes and bipolar proportions, symptoms scores, cognitive performance, or spectral entropy modulation. CONCLUSION: These data support the existence of a subgroup within psychosis with altered global properties of functional and structural connectivity.

Medium-term changes in patients with epilepsy during the COVID-19 pandemic.

OBJECTIVES: The novel coronavirus disease (COVID-19) pandemic has led to social distancing measures and impaired medical care of chronic neurological diseases, including epilepsy, which may have adversely affected well-being and quality of life of patients with epilepsy (PWE). The objective of this study is to evaluate the impact of the COVID-19 pandemic in the levels of anxiety, depression, somnolence, and quality of life using validated scales in PWE in real-life clinical practice. MATERIALS & METHODS: Self-administered scales of anxiety disorders (GAD-7), depression (NDDI-E), somnolence (Epworth Sleepiness Scale; ESS), and quality of life (QOLIE-31-P) in PWE treated in a Refractory Epilepsy Unit were longitudinally analyzed. Data were collected before the beginning (December 2019 - March 2020) and during the COVID-19 pandemic (September 2020-January 2021). RESULTS: 158 patients (85 from the first round and 73 from the second round) 45.0 ± 17.3 years of age, 43.2% women, epilepsy duration 23.0 ± 14.9 years, number of antiepileptic drugs 2.1 ± 1.4, completed the survey. Significant longitudinal reduction of QOLIE-31-P (from 58.9 ± 19.7 to 56.2 ± 16.2, p = .035) and GAD-7 scores (from 8.8 ± 6.2 to 8.3 ± 5.9, corrected p = .024) was identified. No statistically significant longitudinal changes in the number of seizures (from 0.9 ± 1.9 to 2.5 ± 6.2, p = .125) or NDDI-E scores (from 12.3 ± 4.3 to 13.4 ± 4.4, p = .065) were found. Significant longitudinal increase of ESS (from 4.9 ± 3.7 to 7.4 ± 4.9, p = .001) was found. CONCLUSIONS: During the COVID-19 pandemic, quality of life and anxiety levels were lower in PWE, and sleepiness levels were raised, without seizure change.

Temporal distribution of emergency room visits in patients with migraine and other headaches.

Background: Headache is a leading reason for presentation to the emergency department (ED) with migraine being the most frequently headache. To ensure the adequate staffing of healthcare providers during peak times of headache visits, we analyzed the temporal distribution of emergency department visits in patients presenting with headache and/or migraine.Research design and methods: The authors conducted an ecological study, including all consecutive visits to the ED for headache. Patients were classified according to the IHS Classification. We analyzed circadian, circaseptan and circannual patterns for number of visits, comparing migraine patients with other headache patients.Results: There were 2132 ED visits for headache, including primary headache in 1367 (64.1%) cases; migraine in 963 (45.2%); secondary headache in 404 (18.9%); and unspecified headache in 366 (17.1%). The circadian pattern showed peaks around 11:00-13:00 and 17:00-19:00, with visits during the night shift 45% less frequent (p < 0.001). The circaseptan pattern showed a peak on Monday-Tuesday and a low point on Sunday (p < 0.007). The circannual pattern peaked in March and decreased in June.Conclusions: ED visits for headache showed specific circadian, circaseptan and circannual variations. No differences were found in these patterns when comparing migraine patients to other headache patients.

Evaluation of the Impact of the COVID-19 Lockdown in the Clinical Course of Migraine.

OBJECTIVE: Previous studies have demonstrated that emotional stress, changes in lifestyle habits and infections can worsen the clinical course of migraine. We hypothesize that changes in habits and medical care during coronavirus disease 2019 (COVID-19) lockdown might have worsened the clinical course of migraine. DESIGN: Retrospective survey study collecting online responses from migraine patients followed-up by neurologists at three tertiary hospitals between June and July 2020. METHODS: We used a web-based survey that included demographic data, clinical variables related with any headache (frequency) and migraine (subjective worsening, frequency, and intensity), lockdown, and symptoms of post-traumatic stress. RESULTS: The response rate of the survey was 239/324 (73.8%). The final analysis included 222 subjects. Among them, 201/222 (90.5%) were women, aged 42.5 ± 12.0 (mean±SD). Subjective improvement of migraine during lockdown was reported in 31/222 participants (14.0%), while worsening in 105/222 (47.3%) and was associated with changes in migraine triggers such as stress related to going outdoors and intake of specific foods or drinks. Intensity of attacks increased in 67/222 patients (30.2%), and it was associated with the subjective worsening, female sex, recent insomnia, and use of acute medication during a headache. An increase in monthly days with any headache was observed in 105/222 patients (47.3%) and was related to symptoms of post-traumatic stress, older age and living with five or more people. CONCLUSIONS: Approximately half the migraine patients reported worsening of their usual pain during the lockdown. Worse clinical course in migraine patients was related to changes in triggers and the emotional impact of the lockdown.

Effects of the onabotulinumtoxinA follow-up delay in migraine course during the COVID-19 lockdown.

BACKGROUND: Face-to-face procedures have been postponed during COVID-19 pandemic. We aim to evaluate the impact of onabotulinumtoxinA follow-up delay in migraine during COVID-19 pandemic. METHODS: Subjective worsening, intensity of migraine attacks, and frequency of headache and migraine were retrospectively compared between patients with unmodified and interrupted onabotulinumtoxinA follow-up in Headache Units. RESULTS: We included 67 patients with chronic migraine or high-frequency episodic migraine under onabotulinumtoxinA treatment, 65 (97.0%) female, 44.5 ± 12.1 years old. Treatment administration was voluntarily delayed in 14 (20.9%) patients and nine (13.4%) were unable to continue follow-up. Patients with uninterrupted follow-up during lockdown presented 7.6 and 8.1 less monthly days with headache (adjusted p = 0.017) and migraine attacks (adjusted p = 0.009) compared to patients whose follow-up was interrupted, respectively. CONCLUSION: Involuntary delay of onabotulinumtoxinA follow-up in patients with migraine due to COVID-19 pandemic was associated with a higher frequency of headache and migraine attacks. Safe administration of onabotulinumtoxinA during lockdown should be promoted.

Real world effectiveness and tolerability of candesartan in the treatment of migraine: a retrospective cohort study.

To date, two randomized, controlled studies support the use of candesartan for migraine prophylaxis but with limited external validity. We aim to evaluate the effectiveness and tolerability of candesartan in clinical practice and to explore predictors of patient response. Retrospective cohort study including all patients with migraine who received candesartan between April 2008-February 2019. The primary endpoint was the number of monthly headache days during weeks 8-12 of treatment compared to baseline. Additionally, we evaluated the frequency during weeks 20-24. We analysed the percentage of patients with 50% and 75% response rates and the retention rates after three and 6 months of treatment. 120/4121 patients were eligible, aged 45.9 [11.5]; 100 (83.3%) female. Eighty-four patients (70%) had chronic migraine and 53 (42.7%) had medication-overuse headache. The median number of prior prophylactics was 3 (Inter-quartile range 2-5). At baseline, patients had 20.5 ± 8.5 headache days per month, decreasing 4.3 ± 8.4 days by 3 months (weeks 12-16) and by 4.7 ± 8.7 days by 6 months (paired Student's t-test, p < 0.001). The percentage of patients with a 50% response was 32.5% at 3 months and 31.7% at 6 months, while the retention rate was 85.0% and 58.3%. The number of prior treatments (Odds ratio 0.79, 95% CI 0.64-0.97) and the presence of daily headache (Odds ratio 0.39, 95% CI 0.16-0.97) were associated with a lower probability of response. Candesartan showed beneficial effects in the preventive treatment of migraine in clinical practice, including patients with chronic migraine, medication-overuse headache and resistance to prior prophylactics.

Neurobiological underpinnings of cognitive subtypes in psychoses: A cross-diagnostic cluster analysis.

Schizophrenia and bipolar disorder include patients with different characteristics, which may hamper the definition of biomarkers. One of the dimensions with greater heterogeneity among these patients is cognition. Recent studies support the identification of different patients' subgroups along the cognitive domain using cluster analysis. Our aim was to validate clusters defined on the basis of patients' cognitive status and to assess its relation with demographic, clinical and biological measurements. We hypothesized that subgroups characterized by different cognitive profiles would show differences in an array of biological data. Cognitive data from 198 patients (127 with chronic schizophrenia, 42 first episodes of schizophrenia and 29 bipolar patients) were analyzed by a K-means cluster approach and were compared on several clinical and biological variables. We also included 155 healthy controls for further comparisons. A two-cluster solution was selected, including a severely impaired group and a moderately impaired group. The severely impaired group was associated with higher illness duration and symptoms scores, lower thalamus and hippocampus volume, lower frontal connectivity and basal hypersynchrony in comparison to controls and the moderately impaired group. Moreover, both patients' groups showed lower cortical thickness and smaller functional connectivity modulation than healthy controls. This study supports the existence of different cognitive subgroups within the psychoses with different neurobiological underpinnings.

Predicción de la respuesta al tratamiento preventivo en migraña crónica mediante la medición de la sustancia gris en resonancia magnética: estudio piloto / Response prediction for chronic migraine preventive treatment by gray matter morphometry in magnetic resonance imaging: a pilot study

INTRODUCCIÓN: El topiramato es el único tratamiento preventivo oral con nivel de evidencia I para la migraña crónica. OBJETIVO: Evaluar los parámetros de la sustancia gris, obtenidos mediante resonancia magnética, como marcadores de respuesta al tratamiento con topiramato en pacientes con migraña crónica. PACIENTES Y MÉTODOS: La muestra se compuso de 57 pacientes con migraña crónica atendidos por primera vez en una unidad de cefaleas como consecuencia de migraña crónica, a los que se realizó una resonancia magnética de 3 T. Posteriormente, se inició el tratamiento preventivo con topiramato. Se evaluaron la respuesta y la tolerancia a los tres meses y se definió respuesta como disminución de al menos un 50% en el número de días de cefalea al mes. Mediante procesamiento de imágenes de resonancia magnética ponderadas en T1 y difusión, se obtuvieron los parámetros de la sustancia gris (68 estructuras corticales y 16 subcorticales). Se obtuvo un modelo de regresión logística para la valoración predictiva. RESULTADOS: Se analizó a 42 pacientes que toleraron el tratamiento, con respuesta terapéutica en 23 de ellos (54,7%). El modelo final de predicción se construyó con parámetros de la sustancia gris con resultados significativos. En dicho modelo, a mayor curvatura del cúneo izquierdo y área de la ínsula derecha, mayor probabilidad de respuesta, y menor probabilidad a mayor volumen de la corteza inferior parietal derecha y área del giro temporal superior izquierdo. La precisión del modelo predictivo fue del 95%. CONCLUSIÓN: Los parámetros de la sustancia gris pueden ser marcadores útiles de respuesta al tratamiento preventivo con topiramato en la migraña crónica Historia y Humanidades Origen y evolución histórica del término «prefrontal»

INTRODUCTION. Topiramate is the only oral preventative with level of evidence I for the treatment of chronic migraine. AIM. To evaluate gray matter parameters, obtained with magnetic resonance imaging (MRI), as biomarkers of the response to topiramate in chronic migraine patients. PATIENTS AND METHODS. The sample was composed by 57 chronic migraine patients, screened for first time in a Headache Unit due to chronic migraine. MRI acquisitions were performed at a 3 T unit. Afterwards, topiramate preventive treatment began. Response and tolerability were evaluated after three months, defining response as at least 50% reduction in headache days per month. We included patients that tolerated topiramate. T1- and diffusion-weighted MRI were processed to obtain gray matter (68 cortical and 16 subcortical regions) descriptive parameters. A logistic regression model was employed for the predictive assessment. RESULTS. Forty-two patients tolerated the treatment and were analyzed, responding 23 of them (54.7%). The final prediction model was built with gray matter parameters with significant results. In this model, higher left cuneus curvature and right insula area values were associated with a higher probability of response, while higher right inferior parietal cortex volume and left superior temporal gyrus area values were associated with a lower probability. The accuracy of the predictive model was 95%. CONCLUSION. The gray matter parameters may be useful biomarkers of preventive treatment response with topiramate in chronic migraine

Phenotypic characterization of acute headache attributed to SARS-CoV-2: An ICHD-3 validation study on 106 hospitalized patients.

INTRODUCTION: Headache is a common symptom of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In this study, we aimed to characterize the phenotype of headache attributed to SARS-CoV-2 infection and to test the International Classification of Headache Disorders (ICHD-3) phenotypic criteria for migraine and tension-type headache. METHODS: The study design was a cross-sectional study nested in a cohort. We screened all consecutive patients that were hospitalized and had a positive SARS-CoV-2 test. We included patients that described headache if the headache was not better explained by another ICHD-3 diagnosis. Patients were interviewed by two neurologists. RESULTS: We screened 580 patients and included 130 (mean age 56 years, 64% female). Headache was the first symptom of the infection in 26% of patients and appeared within 24 hours in 62% of patients. The headache was bilateral in 85%, frontal in 83%, and with pressing quality in 75% of patients. Mean intensity was 7.1, being severe in 64%. Hypersensitivity to stimuli occurred in 57% of patients. ICHD-3 criteria for headache attributed to systemic viral infection were fulfilled by 94% of patients; phenotypic criteria for migraine were fulfilled by 25% of patients, and tension-type headache criteria by 54% of patients. CONCLUSION: Headache attributed to SARS-CoV-2 infection in hospitalized patients has severe intensity, frontal predominance and oppressive quality. It occurs early in the course of the disease. Most patients fulfilled ICHD-3 criteria for headache attributed to systemic viral infection; however, the phenotype might resemble migraine in a quarter of cases and tension-type headache in half of the patients.

Alternative Microstructural Measures to Complement Diffusion Tensor Imaging in Migraine Studies with Standard MRI Acquisition.

The white matter state in migraine has been investigated using diffusion tensor imaging (DTI) measures, but results using this technique are conflicting. To overcome DTI measures, we employed ensemble average diffusion propagator measures obtained with apparent measures using reduced acquisitions (AMURA). The AMURA measures were return-to-axis (RTAP), return-to-origin (RTOP) and return-to-plane probabilities (RTPP). Tract-based spatial statistics was used to compare fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity from DTI, and RTAP, RTOP and RTPP, between healthy controls, episodic migraine and chronic migraine patients. Fifty healthy controls, 54 patients with episodic migraine and 56 with chronic migraine were assessed. Significant differences were found between both types of migraine, with lower axial diffusivity values in 38 white matter regions and higher RTOP values in the middle cerebellar peduncle in patients with a chronic migraine (p < 0.05 family-wise error corrected). Significantly lower RTPP values were found in episodic migraine patients compared to healthy controls in 24 white matter regions (p < 0.05 family-wise error corrected), finding no significant differences using DTI measures. The white matter microstructure is altered in a migraine, and in chronic compared to episodic migraine. AMURA can provide additional results with respect to DTI to uncover white matter alterations in migraine.