Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images.

Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation. Unsupervised disease detection methods, such as anomaly detection, can significantly reduce human effort in these scenarios. While anomaly detection typically focuses on learning from images of healthy subjects only, real-world situations often present unannotated datasets with a mixture of healthy and diseased subjects. Recent studies have demonstrated that utilizing such unannotated images can improve unsupervised disease and anomaly detection. However, these methods do not utilize knowledge specific to registered neuroimages, resulting in a subpar performance in neurologic disease detection. To address this limitation, we propose Brainomaly, a GAN-based image-to-image translation method specifically designed for neurologic disease detection. Brainomaly not only offers tailored image-to-image translation suitable for neuroimages but also leverages unannotated mixed images to achieve superior neurologic disease detection. Additionally, we address the issue of model selection for inference without annotated samples by proposing a pseudo-AUC metric, further enhancing Brainomaly's detection performance. Extensive experiments and ablation studies demonstrate that Brainomaly outperforms existing state-of-the-art unsupervised disease and anomaly detection methods by significant margins in Alzheimer's disease detection using a publicly available dataset and headache detection using an institutional dataset. The code is available from https://github.com/mahfuzmohammad/Brainomaly.

Aberrant connectivity in the hippocampus, bilateral insula and temporal poles precedes treatment resistance in first-episode psychosis: a prospective resting-state functional magnetic resonance imaging study with connectivity concordance mapping.

Functional connectivity resting-state functional magnetic resonance imaging has been proposed to predict antipsychotic treatment response in schizophrenia. However, only a few prospective studies have examined baseline resting-state functional magnetic resonance imaging data in drug-naïve first-episode schizophrenia patients with regard to subsequent treatment response. Data-driven approaches to conceptualize and measure functional connectivity patterns vary broadly, and model-free, voxel-wise, whole-brain analysis techniques are scarce. Here, we apply such a method, called connectivity concordance mapping to resting-state functional magnetic resonance imaging data acquired from an Asian sample (n = 60) with first-episode psychosis, prior to pharmaceutical treatment. Using a longitudinal design, 12 months after the resting-state functional magnetic resonance imaging, we measured and classified patients into two groups based on psychometric testing: treatment responsive and treatment resistant. Next, we compared the two groups' connectivity concordance maps that were derived from the resting-state functional magnetic resonance imaging data at baseline. We have identified consistently higher functional connectivity in the treatment-resistant group in a network including the left hippocampus, bilateral insula and temporal poles. These data-driven novel findings can help researchers to consider new regions of interest and facilitate biomarker development in order to identify treatment-resistant schizophrenia patients early, in advance of treatment and at the time of their first psychotic episode.

Brain structural and functional abnormalities associated with acute post-traumatic headache: iron deposition and functional connectivity.

BACKGROUND: The purpose of this study was to interrogate brain iron accumulation in participants with acute post-traumatic headache (PTH) due to mild traumatic brain injury (mTBI), and to determine if functional connectivity is affected in areas with iron accumulation. We aimed to examine the correlations between iron accumulation and headache frequency, post-concussion symptom severity, number of mTBIs, and time since most recent TBI. METHODS: Sixty participants with acute PTH and 60 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging including quantitative T2* maps and resting-state functional connectivity imaging. Between group T2* differences were determined using T-tests (p < 0.005, cluster size threshold of 90 voxels). For regions with T2* differences, two analyses were conducted. First, the correlations with clinical variables including headache frequency, number of lifetime mTBIs, time since most recent mTBI, and Sport Concussion Assessment Tool (SCAT) symptom severity scale scores were investigated using linear regression. Second, the functional connectivity of these regions with the rest of the brain was examined (significance of p < 0.05 with family wise error correction for multiple comparisons). RESULTS: The acute PTH group consisted of 60 participants (22 male, 38 female) with average age of 42 ± 14 years. The HC group consisted of 60 age-matched controls (17 male, 43 female, average age of 42 ± 13). PTH participants had lower T2* values compared to HC in the left posterior cingulate and the bilateral cuneus. Stronger functional connectivity was observed between bilateral cuneus and right cerebellar areas in PTH compared to HC. Within the PTH group, linear regression showed negative associations of T2* in the left posterior cingulate with SCAT symptom severity score (p = 0.05) and T2* in the left cuneus with headache frequency (p = 0.04). CONCLUSIONS: Iron accumulation in posterior cingulate and cuneus was observed in those with acute PTH relative to HC; stronger functional connectivity was detected between the bilateral cuneus and the right cerebellum. The correlations of decreased T2* (suggesting higher iron content) with headache frequency and post mTBI symptom severity suggest that the iron accumulation that results from mTBI might reflect the severity of underlying mTBI pathophysiology and associate with post-mTBI symptom severity including PTH.

Brain Structural and Functional Abnormalities Associated with Acute Post-Traumatic Headache: Iron Deposition and Functional Connectivity.

Background: The purpose of this study was to interrogate brain iron accumulation in participants with acute post-traumatic headache (PTH) due to mild traumatic brain injury (mTBI), and to determine if functional connectivity is affected in areas with iron accumulation. We aimed to examine the correlations between iron accumulation and headache frequency, post-concussion symptom severity, number of mTBIs and time since most recent TBI. Methods: Sixty participants with acute PTH and 60 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging including quantitative T2* maps and resting-state functional connectivity imaging. Between group T2* differences were determined using T-tests (p < 0.005, cluster size threshold of 10 voxels). For regions with T2* differences, two analyses were conducted. First, the correlations with clinical variables including headache frequency, number of lifetime mTBIs, time since most recent mTBI, and Sport Concussion Assessment Tool (SCAT) symptom severity scale scores were investigated using linear regression. Second, the functional connectivity of these regions with the rest of the brain was examined (significance of p < 0.05 with family wise error correction for multiple comparisons). Results: The acute PTH group consisted of 60 participants (22 male, 38 female) with average age of 42 ± 14 years. The HC group consisted of 60 age-matched controls (17 male, 43 female, average age of 42 ± 13). PTH participants had lower T2* values compared to HC in the left posterior cingulate and the bilateral cuneus. Stronger functional connectivity was observed between bilateral cuneus and right cerebellar areas in PTH compared to HC. Within the PTH group, linear regression showed negative associations of T2* and SCAT symptom severity score in the left posterior cingulate (p = 0.05) and with headache frequency in the left cuneus (p = 0.04). Conclusions: Iron accumulation in posterior cingulate and cuneus was observed in those with acute PTH relative to HC; stronger functional connectivity was detected between the bilateral cuneus and the right cerebellum. The correlations of decreased T2* (suggesting higher iron content) with headache frequency and post mTBI symptom severity suggest that the iron accumulation that results from mTBI might reflect the severity of underlying mTBI pathophysiology and associate with post-mTBI symptom severity including PTH.

Guidelines of the International Headache Society for controlled trials of pharmacological preventive treatment for persistent post-traumatic headache attributed to mild traumatic brain injury.

BACKGROUND: Persistent headache attributed to traumatic injury to the head is divided into two subtypes, one attributed to moderate or severe traumatic injury and another attributed to mild traumatic injury (i.e., concussion). The latter is much more prevalent, in part because more than 90% of cases with traumatic brain injury are classified as mild. The pathophysiology of persistent post-traumatic headache is poorly understood and the underlying mechanisms are likely multifactorial. There is currently no approved treatment specifically for persistent post-traumatic headache, and management strategies rely on medications used for migraine or tension-type headache. Therefore, high-quality trials are urgently needed to support clinical decision-making and optimize management strategies. International guidelines can facilitate appropriate trial design and ensure the acquisition of high-quality data evaluating the efficacy, tolerability, and safety of available and novel pharmacological therapies for the preventive treatment of persistent post-traumatic headache. METHODS: The development of this guideline was based on a literature review of available studies in MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials, along with a review of previously published guidelines for controlled trials of preventive treatment for episodic and chronic migraine. The identified literature was critically appraised, and due to the scarcity of scientific evidence, recommendations were primarily based on the consensus of experts in the field. OBJECTIVE: To provide guidelines for designing state-of-the-art controlled clinical trials aimed at evaluating the effectiveness of preventive treatments for persistent post-traumatic headache attributed to mild traumatic brain injury.

Photophobia Contributes to Migraine-Associated Disability and Reduced Work Productivity: Results From the American Registry for Migraine Research (ARMR).

BACKGROUND: Photosensitivity, often called "photophobia" in the migraine literature, is a common and bothersome symptom for most people during their migraine attacks. This study aimed to investigate the association of photophobia severity with work productivity, activity impairment, and migraine-associated disability using data from a large cohort of patients with migraine who were enrolled into the American Registry for Migraine Research (ARMR). METHODS: This study used Photosensitivity Assessment Questionnaire (PAQ) scores to investigate the relationship between photophobia severity with work productivity and activity impairment (using the Work Productivity and Activity Impairment [WPAI] questionnaire) and migraine-related disability (using the Migraine Disability Assessment [MIDAS]) among those with migraine. Summary statistics are presented as means and standard deviations for variables that were normally distributed and as medians and interquartile ranges for variables that were not normally distributed. Multiple linear regression models were developed to measure the relationships between photophobia scores with work productivity and activity impairment and migraine-associated disability, controlling for age, sex, headache frequency, headache intensity, anxiety (using the generalized anxiety disorder [GAD-7]), and depression (using the Patient Health Questionnaire [PHQ-2]). RESULTS: One thousand eighty-four participants were included. Average age was 46.1 (SD 13.8) years, 87.2% (n = 945) were female, average headache frequency during the previous 90 days was 44.3 (SD 29.9), average headache intensity was 5.9 (SD 1.7), median PHQ-2 score was 1 (IQR 0-2), and median GAD-7 was 5 (IQR 2-8). Mean PAQ score was 0.47 (SD 0.32), and median MIDAS score was 38 (IQR 15.0-80.0). Among the 584 employed participants, 47.4% (n = 277) reported missing work in the past week because of migraine, mean overall work impairment was 42.8% (SD 26.7), mean activity impairment was 42.5% (SD 26.2), mean presenteeism score was 38.4% (SD 24.4), and median absenteeism was 0 (IQR 0-14.5). After controlling for age, sex, headache frequency, average headache intensity, PHQ-2 score, and GAD-7 score, there was a statistically significant association between photophobia scores with: a) MIDAS scores (F[7,1028] = 127.42, P < 0.001, R2 = 0.461, n = 1,036); b) overall work impairment (F[7,570] = 29.23, P < 0.001, R2 = 0.255, n = 578); c) activity impairment (F[7,570] = 27.42, P < 0.001, R2 = 0.243, n = 578); d) presenteeism (F[7,570] = 29.17, P < 0.001, R2 = 0.255, n = 578); and e) absenteeism for the zero-inflated (P = 0.003) and negative binomial (P = 0.045) model components (P < 0.001, n = 578). CONCLUSIONS: In those with migraine, severe photophobia is associated with reduced work productivity and higher presenteeism, absenteeism, activity impairment, and migraine-related disability.

Harmonizing Healthy Cohorts to Support Multicenter Studies on Migraine Classification using Brain MRI Data.

Multicenter and multi-scanner imaging studies might be needed to provide sample sizes large enough for developing accurate predictive models. However, multicenter studies, which likely include confounding factors due to subtle differences in research participant characteristics, MRI scanners, and imaging acquisition protocols, might not yield generalizable machine learning models, that is, models developed using one dataset may not be applicable to a different dataset. The generalizability of classification models is key for multi-scanner and multicenter studies, and for providing reproducible results. This study developed a data harmonization strategy to identify healthy controls with similar (homogenous) characteristics from multicenter studies to validate the generalization of machine-learning techniques for classifying individual migraine patients and healthy controls using brain MRI data. The Maximum Mean Discrepancy (MMD) was used to compare the two datasets represented in Geodesic Flow Kernel (GFK) space, capturing the data variabilities for identifying a "healthy core". A set of homogeneous healthy controls can assist in overcoming some of the unwanted heterogeneity and allow for the development of classification models that have high accuracy when applied to new datasets. Extensive experimental results show the utilization of a healthy core. One dataset consists of 120 individuals (66 with migraine and 54 healthy controls) and another dataset consists of 76 (34 with migraine and 42 healthy controls) individuals. A homogeneous dataset derived from a cohort of healthy controls improves the performance of classification models by about 25% accuracy improvements for both episodic and chronic migraineurs.

Prevalence of and Risk Factors for Post-traumatic Headache in Civilian Patients After Mild Traumatic Brain Injury: A TRACK-TBI Study.

OBJECTIVE: To ascertain the prevalence of and risk factors for post-traumatic headache (PTH) attributed to mild traumatic brain injury (mTBI). PATIENTS AND METHODS: A prospective, longitudinal, multicenter cohort study of patients with mTBI and orthopedic trauma controls who were enrolled from February 26, 2014, to August 8, 2018. The baseline assessment was conducted as soon as possible following evaluation at the emergency department. Follow-ups were scheduled at 2 weeks, 3 months, 6 months, and 12 months postinjury. Eligible patients with mTBI included those 18 years of age or older who presented to the emergency department within 24 hours of head injury warranting evaluation by noncontrast head computed tomography scan. Acute PTH was considered present when a patient reported a headache score of greater than or equal to 2 on the Rivermead Post-concussion Questionnaire at 2 weeks postinjury (ie, headache is at least a mild problem compared with pre-injury). Persistent PTH was defined when a patient with acute PTH reported a Rivermead Post-concussion Questionnaire headache score of greater than or equal to 2 at the scheduled follow-up examinations. RESULTS: Acute PTH was reported by 963 (60.4%) of 1594 patients with mTBI at 2 weeks postinjury. Among those with acute PTH, 439 (52.4%) of 837 patients reported persistent PTH at 3 months postinjury. This figure decreased over time and 278 (37.5%) of 742 patients continued to report persistent PTH at 6 months, whereas 187 (28.9%) of 646 patients did so as well at 12 months postinjury. Risk factors for acute PTH included younger age, female sex, fewer years of formal education, computed tomography-positive scans, alteration of consciousness, psychiatric history, and history of migraine. Risk factors for persistent PTH included female sex, fewer years of formal education, and history of migraine. CONCLUSION: Post-traumatic headache is a prevalent sequela of mTBI that persists for at least 12 months in a considerable proportion of affected individuals. The attributable burden necessitates better patient follow-up, disease characterization, improved awareness of PTH in clinical practice, and identification of effective therapies.

Questionnaire and structural imaging data accurately predict headache improvement in patients with acute post-traumatic headache attributed to mild traumatic brain injury.

BACKGROUND: Our prior work demonstrated that questionnaires assessing psychosocial symptoms have utility for predicting improvement in patients with acute post-traumatic headache following mild traumatic brain injury. In this cohort study, we aimed to determine whether prediction accuracy can be refined by adding structural magnetic resonance imaging (MRI) brain measures to the model. METHODS: Adults with acute post-traumatic headache (enrolled 0-59 days post-mild traumatic brain injury) underwent T1-weighted brain MRI and completed three questionnaires (Sports Concussion Assessment Tool, Pain Catastrophizing Scale, and the Trait Anxiety Inventory Scale). Individuals with post-traumatic headache completed an electronic headache diary allowing for determination of headache improvement at three- and at six-month follow-up. Questionnaire and MRI measures were used to train prediction models of headache improvement and headache trajectory. RESULTS: Forty-three patients with post-traumatic headache (mean age = 43.0, SD = 12.4; 27 females/16 males) and 61 healthy controls were enrolled (mean age = 39.1, SD = 12.8; 39 females/22 males). The best model achieved cross-validation Area Under the Curve of 0.801 and 0.805 for predicting headache improvement at three and at six months. The top contributing MRI features for the prediction included curvature and thickness of superior, middle, and inferior temporal, fusiform, inferior parietal, and lateral occipital regions. Patients with post-traumatic headache who did not improve by three months had less thickness and higher curvature measures and notably greater baseline differences in brain structure vs. healthy controls (thickness: p < 0.001, curvature: p = 0.012) than those who had headache improvement. CONCLUSIONS: A model including clinical questionnaire data and measures of brain structure accurately predicted headache improvement in patients with post-traumatic headache and achieved improvement compared to a model developed using questionnaire data alone.

Headache classification and automatic biomarker extraction from structural MRIs using deep learning.

Data-driven machine-learning methods on neuroimaging (e.g. MRI) are of great interest for the investigation and classification of neurological diseases. However, traditional machine learning requires domain knowledge to delineate the brain regions first, followed by feature extraction from the regions. Compared with this semi-automated approach, recently developed deep learning methods have advantages since they do not require such prior knowledge; instead, deep learning methods can automatically find features that differentiate MRIs from different cohorts. In the present study, we developed a deep learning-based classification pipeline distinguishing brain MRIs of individuals with one of three types of headaches [migraine (n = 95), acute post-traumatic headache (n = 48) and persistent post-traumatic headache (n = 49)] from those of healthy controls (n = 532) and identified the brain regions that most contributed to each classification task. Our pipeline included: (i) data preprocessing; (ii) binary classification of healthy controls versus headache type using a 3D ResNet-18; and (iii) biomarker extraction from the trained 3D ResNet-18. During the classification at the second step of our pipeline, we resolved two common issues in deep learning methods, limited training data and imbalanced samples from different categories, by incorporating a large public data set and resampling among the headache cohorts. Our method achieved the following classification accuracies when tested on independent test sets: (i) migraine versus healthy controls-75% accuracy, 66.7% sensitivity and 83.3% specificity; (2) acute post-traumatic headache versus healthy controls-75% accuracy, 66.7% sensitivity and 83.3% specificity; and (3) persistent post-traumatic headache versus healthy controls-91.7% accuracy, 100% sensitivity and 83.3% specificity. The most significant biomarkers identified by the classifier for migraine were caudate, caudal anterior cingulate, superior frontal, thalamus and ventral diencephalon. For acute post-traumatic headache, lateral occipital, cuneus, lingual, pericalcarine and superior parietal regions were identified as most significant biomarkers. Finally, for persistent post-traumatic headache, the most significant biomarkers were cerebellum, middle temporal, inferior temporal, inferior parietal and superior parietal. In conclusion, our study shows that the deep learning methods can automatically detect aberrations in the brain regions associated with different headache types. It does not require any human knowledge as input which significantly reduces human effort. It uncovers the great potential of deep learning methods for classification and automatic extraction of brain imaging-based biomarkers for these headache types.

Longitudinal differences in iron deposition in periaqueductal gray matter and anterior cingulate cortex are associated with response to erenumab in migraine.

OBJECTIVES: The objective of this longitudinal study was to determine whether brain iron accumulation, measured using magnetic resonance imaging magnetic transverse relaxation rates (T2*), is associated with response to erenumab for the treatment of migraine. METHODS: Participants (n = 28) with migraine, diagnosed using international classification of headache disorders 3rd edition criteria, were eligible if they had six to 25 migraine days during a four-week headache diary run-in phase. Participants received two treatments with 140 mg erenumab, one immediately following the pre-treatment run-in phase and a second treatment four weeks later. T2* data were collected immediately following the pre-treatment phase, and at two weeks and eight weeks following the first erenumab treatment. Patients were classified as erenumab responders if their migraine-day frequency at five-to-eight weeks post-initial treatment was reduced by at least 50% compared to the pre-treatment run-in phase. A longitudinal Sandwich estimator approach was used to compare longitudinal group differences (responders vs non-responders) in T2* values, associated with iron accumulation. Group visit effects were calculated with a significance threshold of p = 0.005 and cluster forming threshold of 250 voxels. T2* values of 19 healthy controls were used for a reference. The average of each significant region was compared between groups and visits with Bonferroni corrections for multiple comparisons with significance defined as p < 0.05. RESULTS: Pre- and post-treatment longitudinal imaging data were available from 28 participants with migraine for a total of 79 quantitative T2* images. Average subject age was 42 ± 13 years (25 female, three male). Of the 28 subjects studied, 53.6% were erenumab responders. Comparing longitudinal T2* between erenumab responders vs non-responders yielded two comparisons which survived the significance threshold of p < 0.05 after correction for multiple comparisons: the difference at eight weeks between the erenumab-responders and non-responders in the periaqueductal gray (mean ± standard error; responders 43 ± 1 ms vs non-responders 32.5 ± 1 ms, p = 0.002) and the anterior cingulate cortex (mean ± standard error; responders 50 ± 1 ms vs non-responders 40 ± 1 ms, p = 0.01). CONCLUSIONS: Erenumab response is associated with higher T2* in the periaqueductal gray and anterior cingulate cortex, regions that participate in pain processing and modulation. T2* differences between erenumab responders vs non-responders, a measure of brain iron accumulation, are seen at eight weeks post-treatment. Less iron accumulation in the periaqueductal gray and anterior cingulate cortex might play a role in the therapeutic mechanisms of migraine reduction associated with erenumab.

Thalamic subfield iron accumulation after acute mild traumatic brain injury as a marker of future post-traumatic headache intensity.

OBJECTIVE: To explore alterations in thalamic subfield volume and iron accumulation in individuals with post-traumatic headache (PTH) relative to healthy controls. BACKGROUND: The thalamus plays a pivotal role in the pathomechanism of pain and headache, yet the role of the thalamus in PTH attributed to mild traumatic brain injury (mTBI) remains unclear. METHODS: A total of 107 participants underwent multimodal T1-weighted and T2* brain magnetic resonance imaging. Using a clinic-based observational study, thalamic subfield volume and thalamic iron accumulation were explored in 52 individuals with acute PTH (mean age = 41.3; standard deviation [SD] = 13.5), imaged on average 24 days post mTBI, and compared to 55 healthy controls (mean age = 38.3; SD = 11.7) without history of mTBI or migraine. Symptoms of mTBI and headache characteristics were assessed at baseline (0-59 days post mTBI) (n = 52) and 3 months later (n = 46) using the Symptom Evaluation of the Sports Concussion Assessment Tool (SCAT-5) and a detailed headache history questionnaire. RESULTS: Relative to controls, individuals with acute PTH had significantly less volume in the lateral geniculate nucleus (LGN) (mean volume: PTH = 254.1, SD = 43.4 vs. controls = 278.2, SD = 39.8; p = 0.003) as well as more iron deposition in the left LGN (PTH: T2* signal = 38.6, SD = 6.5 vs. controls: T2* signal = 45.3, SD = 2.3; p = 0.048). Correlations in individuals with PTH revealed a positive relationship between left LGN T2* iron deposition and SCAT-5 symptom severity score at baseline (r = -0.29, p = 0.019) and maximum headache intensity at the 3-month follow-up (r = -0.47, p = 0.002). CONCLUSION: Relative to healthy controls, individuals with acute PTH had less volume and higher iron deposition in the left LGN. Higher iron deposition in the left LGN might reflect mTBI severity and poor headache recovery.

Developing multivariable models for predicting headache improvement in patients with acute post-traumatic headache attributed to mild traumatic brain injury: A preliminary study.

OBJECTIVES/BACKGROUND: Post-traumatic headache (PTH) is a common symptom after mild traumatic brain injury (mTBI). Although there have been several studies that have used clinical features of PTH to attempt to predict headache recovery, currently no accurate methods exist for predicting individuals' improvement from acute PTH. This study investigated the utility of clinical questionnaires for predicting (i) headache improvement at 3 and 6 months, and (ii) headache trajectories over the first 3 months. METHODS: We conducted a clinic-based observational longitudinal study of patients with acute PTH who completed a battery of clinical questionnaires within 0-59 days post-mTBI. The battery included headache history, symptom evaluation, cognitive tests, psychological tests, and scales assessing photosensitivity, hyperacusis, insomnia, cutaneous allodynia, and substance use. Each participant completed a web-based headache diary, which was used to determine headache improvement. RESULTS: Thirty-seven participants with acute PTH (mean age = 42.7, standard deviation [SD] = 12.0; 25 females/12 males) completed questionnaires at an average of 21.7 (SD = 13.1) days post-mTBI. The classification of headache improvement or non-improvement at 3 and 6 months achieved cross-validation area under the curve (AUC) of 0.72 (95% confidence interval [CI] 0.55 to 0.89) and 0.84 (95% CI 0.66 to 1.00). Sub-models trained using only the top five features still achieved 0.72 (95% CI 0.55 to 0.90) and 0.77 (95% CI 0.52 to 1.00) AUC. The top five contributing features were from three questionnaires: Pain Catastrophizing Scale total score and helplessness sub-domain score; Sports Concussion Assessment Tool Symptom Evaluation total score and number of symptoms; and the State-Trait Anxiety Inventory score. The functional regression model achieved R = 0.64 for modeling headache trajectory over the first 3 months. CONCLUSION: Questionnaires completed following mTBI have good utility for predicting headache improvement at 3 and 6 months in the future as well as the evolving headache trajectory. Reducing the battery to only three questionnaires, which assess post-concussive symptom load and biopsychosocialecologic factors, was helpful to determine a reasonable prediction accuracy for headache improvement.

Effect of high-endurance exercise intervention on sleep-dependent procedural memory consolidation in individuals with schizophrenia: a randomized controlled trial.

BACKGROUND: Little is known about the effects of physical exercise on sleep-dependent consolidation of procedural memory in individuals with schizophrenia. We conducted a randomized controlled trial (RCT) to assess the effectiveness of physical exercise in improving this cognitive function in schizophrenia. METHODS: A three-arm parallel open-labeled RCT took place in a university hospital. Participants were randomized and allocated into either the high-intensity-interval-training group (HIIT), aerobic-endurance exercise group (AE), or psychoeducation group for 12 weeks, with three sessions per week. Seventy-nine individuals with schizophrenia spectrum disorder were contacted and screened for their eligibility. A total of 51 were successfully recruited in the study. The primary outcome was sleep-dependent procedural memory consolidation performance as measured by the finger-tapping motor sequence task (MST). Assessments were conducted during baseline and follow-up on week 12. RESULTS: The MST performance scored significantly higher in the HIIT (n = 17) compared to the psychoeducation group (n = 18) after the week 12 intervention (p < 0.001). The performance differences between the AE (n = 16) and the psychoeducation (p = 0.057), and between the AE and the HIIT (p = 0.999) were not significant. Yet, both HIIT (p < 0.0001) and AE (p < 0.05) showed significant within-group post-intervention improvement. CONCLUSIONS: Our results show that HIIT and AE were effective at reverting the defective sleep-dependent procedural memory consolidation in individuals with schizophrenia. Moreover, HIIT had a more distinctive effect compared to the control group. These findings suggest that HIIT may be a more effective treatment to improve sleep-dependent memory functions in individuals with schizophrenia than AE alone.

Working memory deterioration as an early warning sign for relapse in remitted psychosis: A one-year naturalistic follow-up study.

BACKGROUND: Relapse prevention is an important goal in the clinical management of psychosis. Cognitive deficits/deterioration can provide useful insights for monitoring relapse in psychosis patients. METHODS: This was a prospective, naturalistic 1-year follow-up study involving 110 psychosis patients with full clinical remission. Relapse, defined as the recurrence of psychotic symptoms, was monitored monthly along with digital tracking of verbal and visual working memory using a mobile app developed for this study. Cognitive deterioration was defined as worsening performance over 2 months prior to relapse or study termination, whichever was earlier. Other clinical, cognitive, functioning, and psychosocial variables were also collected. RESULTS: At 1 year, 18 (16.36%) patients relapsed, of which 6 (33.33%) required hospitalization. Relapse was predicted by verbal working memory deterioration 2 months prior to relapse (p = 0.029), worse medication adherence (p = 0.018), and less resilience (p = 0.014). CONCLUSIONS: Verbal working memory deterioration is a novel early sign of relapse. It is a clearly defined, objectively measurable, and reproducible marker that can help clinicians and healthcare workers identify patients at risk of relapse and make decisions about maintenance therapy. Moreover, digital monitoring is a viable tool in the management of relapse.

Longitudinal changes in functional connectivity and pain-induced brain activations in patients with migraine: a functional MRI study pre- and post- treatment with Erenumab.

BACKGROUND: Migraine involves central and peripheral nervous system mechanisms. Erenumab, an anti-calcitonin gene-related peptide (CGRP) receptor monoclonal antibody with little central nervous system penetrance, is effective for migraine prevention. The objective of this study was to determine if response to erenumab is associated with alterations in brain functional connectivity and pain-induced brain activations. METHODS: Adults with 6-25 migraine days per month during a 4-week headache diary run-in phase underwent pre-treatment brain functional MRI (fMRI) that included resting-state functional connectivity and BOLD measurements in response to moderately painful heat stimulation to the forearm. This was followed by two treatments with 140 mg erenumab, at baseline and 4 weeks later. Post-treatment fMRI was performed 2 weeks and 8 weeks following the first erenumab treatment. A longitudinal Sandwich estimator analysis was used to identify pre- to post-treatment changes in resting-state functional connectivity and brain activations in response to thermal pain. fMRI findings were compared between erenumab treatment-responders vs. erenumab non-responders. RESULTS: Pre- and post-treatment longitudinal imaging data were available from 32 participants. Average age was 40.3 (+/- 13) years and 29 were female. Pre-treatment average migraine day frequency was 13.8 (+/- 4.7) / 28 days and average headache day frequency was 15.8 (+/- 4.4) / 28 days. Eighteen of 32 (56%) were erenumab responders. Compared to erenumab non-responders, erenumab responders had post-treatment differences in 1) network functional connectivity amongst pain-processing regions, including higher global efficiency, clustering coefficient, node degree, regional efficiency, and modularity, 2) region-to-region functional connectivity between several regions including temporal pole, supramarginal gyrus, and hypothalamus, and 3) pain-induced activations in the middle cingulate, posterior cingulate, and periaqueductal gray matter. CONCLUSIONS: Reductions in migraine day frequency accompanying erenumab treatment are associated with changes in resting state functional connectivity and central processing of extracranial painful stimuli that differ from erenumab non-responders. TRIAL REGISTRATION: clinicaltrials.gov (NCT03773562).

Migraine and Posttraumatic Headache: Similarities and Differences in Brain Network Connectivity.

Posttraumatic headache (PTH) is the most common symptom following mild traumatic brain injury (mTBI) (also known as concussion). Migraine and PTH have similar phenotypes, and a migraine-like phenotype is common in PTH. The similarities between both headache types are intriguing and challenge a better understanding of the pathophysiological commonalities involved in migraine and PTH due to mTBI. Here, we review the PTH resting-state functional connectivity literature and compare it to migraine to assess overlap and differences in brain network function between both headache types. Migraine and PTH due to mTBI have overlapping and disease-specific widespread alterations of static and dynamic functional networks involved in pain processing as well as dysfunctional network connections between frontal regions and areas of pain modulation and pain inhibition. Although the PTH functional network literature is still limited, there is some evidence that dysregulation of the top-down pain control system underlies both migraine and PTH. However, disease-specific differences in the functional circuitry are observed as well, which may reflect unique differences in brain architecture and pathophysiology underlying both headache disorders.

The Characteristics of White Matter Hyperintensities in Patients With Migraine.

Background: The presence of white matter hyperintensities (WMHs) in migraine is well-documented, but the location of WMH in patients with migraine is insufficiently researched. This study assessed WMH in patients with migraine using a modified version of the Scheltens visual rating scale, a semiquantitative scale for categorizing WMH in periventricular, lobar, basal ganglia, and infratentorial regions. Methods: In total, 263 patients with migraine (31 men and232 women) enrolled in the American Registry for Migraine Research (ARMR) from Mayo Clinic Arizona and who had clinical brain magnetic resonance imaging (MRI) were included in this study. Those with imaging evidence for gross anatomical abnormalities other than WMHs were excluded. A board-certified neuroradiologist identified WMHs on axial T2 and fluid-attenuated inversion recovery (FLAIR) sequences. WMHs were characterized via manual inspection and categorized according to the scale's criteria. Results: Results showed that 95 patients (36.1%, mean age: 41.8 years) had no WMHs on axial T2 and FLAIR imaging and 168 patients (63.9%, mean age: 51.4 year) had WMHs. Of those with WMHs, 94.1% (n = 158) had lobar hyperintensities (frontal: 148/158, 93.7%; parietal: 57/158, 36.1%; temporal: 35/158, 22.1%; and occipital: 9/158, 5.7%), 13/168, 7.7% had basal ganglia WMHs, 49/168, 29.1% had periventricular WMHs, and 17/168, 10.1% had infratentorial WMHs. In addition, 101/168 patients (60.1%) had bilateral WMHs and 67/168 (39.9%) had unilateral WMHs (34 right hemisphere/33 left hemisphere). Discussion: Among ARMR participants who were enrolled by Mayo Clinic Arizona and who had clinical brain MRIs, nearly two-thirds had WMHs. The WMHs were the most common in the frontal lobes. Describing the features of WMHs in those with migraine, and comparing them with WMHs attributable to other etiologies, might be useful for developing classifiers that differentiate between migraine-specific WMH and other causes of WMH.