Headache neuroimaging: A survey of current practice, barriers, and facilitators to optimal use

Improving quantitative susceptibility mapping for the identification of traumatic brain injury neurodegeneration at the individual level

BACKGROUND

Emerging evidence suggests that traumatic brain injury (TBI) is a major risk factor for developing neurodegenerative disease later in life. Quantitative susceptibility mapping (QSM) has been used by an increasing number of studies in investigations of pathophysiological changes in TBI. However, generating artefact-free quantitative susceptibility maps in brains with large focal lesions, as in the case of moderate-to-severe TBI (ms-TBI), is particularly challenging. To address this issue, we utilized a novel two-pass masking technique and reconstruction procedure (two-pass QSM) to generate quantitative susceptibility maps (QSMxT; Stewart et al., 2022, Magn Reson Med.) in combination with the recently developed virtual brain grafting (VBG) procedure for brain repair (Radwan et al., 2021, NeuroImage) to improve automated delineation of brain areas. We used QSMxT and VBG to generate personalised QSM profiles of individual patients with reference to a sample of healthy controls.

METHODS

Chronic ms-TBI patients (N = 8) and healthy controls (N = 12) underwent (multi-echo) GRE, and anatomical MRI (MPRAGE) on a 3T Siemens PRISMA scanner. We reconstructed the magnetic susceptibility maps using two-pass QSM from QSMxT. We then extracted values of magnetic susceptibility in grey matter (GM) regions (following brain repair via VBG) across the whole brain and determined if they deviate from a reference healthy control group [Z-score < -3.43 or > 3.43, relative to the control mean], with the aim of obtaining personalised QSM profiles.

RESULTS

Using two-pass QSM, we achieved susceptibility maps with a substantial increase in quality and reduction in artefacts irrespective of the presence of large focal lesions, compared to single-pass QSM. In addition, VBG minimised the loss of GM regions and exclusion of patients due to failures in the region delineation step. Our findings revealed deviations in magnetic susceptibility measures from the HC group that differed across individual TBI patients. These changes included both increases and decreases in magnetic susceptibility values in multiple GM regions across the brain.

CONCLUSIONS

We illustrate how to obtain magnetic susceptibility values at the individual level and to build personalised QSM profiles in ms-TBI patients. Our approach opens the door for QSM investigations in more severely injured patients. Such profiles are also critical to overcome the inherent heterogeneity of clinical populations, such as ms-TBI, and to characterize the underlying mechanisms of neurodegeneration at the individual level more precisely. Moreover, this new personalised QSM profiling could in the future assist clinicians in assessing recovery and formulating a neuroscience-guided integrative rehabilitation program tailored to individual TBI patients.

Genetic algorithms based optimized hybrid deep learning model for explainable Alzheimer's prediction based on temporal multimodal cognitive data.. [DOI: 10.21203/rs.3.rs-3250006/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test their neuroimages. Cognitive scores provide an medically acceptable and cost-effective alternative for the neuroimages to predict AD progression. Each score is calculated from a collection of sub-scores which provide a deeper insight about patient conditions. No study in the literature have explored the role of these multimodal time series sub-scores to predict AD progression. We propose a hybrid CNN-LSTM DL model for predicting AD progression based on the fusion of four longitudinal cognitive sub-scores modalities. Bayesian optimizer has been used to select the best DL architecture. A genetic algorithms based feature selection optimization step has been added to the pipeline to select the best features from extracted deep representations of CNN-LSTM. The SoftMax classifier has been replaced by a robust and optimized random forest classifier. Extensive experiments using the ADNI dataset investigated the role of each optimization step, and the proposed model achieved the best results compared to other DL and classical machine learning models. The resulting model is robust, but it is a black box and it is difficult to understand the logic behind its decisions. Trustworthy AI models must be robust and explainable. We used SHAP and LIME to provide explainability features for the proposed model. The resulting trustworthy model has a great potential to be used to provide decision support in the real environments.

Acute hemicranial pain accompanied with a pearl and string type dissection of intracranial vertebral artery: Consideration for the time when to finish the medical observation

The International Classification of Headache Disorder (ICHD) clearly guides the suspicion of intracranial vertebral artery dissection (ICVAD) in headache patients, but guidelines on how observational or imaging studies should be performed to detect dangerous progression early are unclear. Fifty-six cases with pearl and string type intracranial vertebral artery dissection were divided into 3 groups: 39 in the headache group, 6 in the infarction group, and 11 in the hemorrhagic group. Clinical and angiographic data were analyzed and compared. Most headaches resolved within 2 weeks and did not exceed 8 weeks. Of the 33 patients (84.6%) who underwent continuous follow-up imaging, 18 (54.5%) returned to normal, but 3 (9%) had deteriorated. All the patients survived without subsequent bleeding or infarction. Image changes started before 3rd month and ended after 6 to 7 months. In acute ICVADs, image changes occur at the same time as the headache resolves and continue for several months after the headache has subsided. Since the dissection is likely to worsen even after the headache disappears, the image changes continue over several months, and prediction of rupture of unruptured ICVAD is unpredictable, it is desirable to conduct continuous imaging studies regularly after the initiation of dissection until stabilization is confirmed.

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.