Single subject and group whole-brain fMRI mapping of male genital sensation at 7 Tesla

Processing of genital sensations in the central nervous system of humans is still poorly understood. Current knowledge is mainly based on neuroimaging studies using electroencephalography (EEG), magneto-encephalography (MEG), and 1.5- or 3- Tesla (T) functional magnetic resonance imaging (fMRI), all of which suffer from limited spatial resolution and sensitivity, thereby relying on group analyses to reveal significant data. Here, we studied the impact of passive, yet non-arousing, tactile stimulation o

Whole brain 7T-fMRI during pelvic floor muscle contraction in male subjects

Aim: The primary aim of this study is to demonstrate that 7-tesla functional magnetic resonance imaging (7T-fMRI) can visualize the neural representations of the male pelvic floor in the whole brain of a single subject. Methods: In total, 17 healthy male volunteers (age 20-47) were scanned in a 7T-MRI scanner (Philips Achieva). The scanning protocol consisted of two functional runs using a multiband echo planar imaging sequence and a T1-weighted scan. The subjects executed two motor tasks, one involving consecutive pelvic floor muscle contractions (PFMC) and a control task with tongue movements. Results: In single subjects, results of both tasks were visualized in the cortex, putamen, thalamus, and the cerebellum. Activation was seen during PFMC in the superomedial and inferolateral primary motor cortex (M1), supplementary motor area (SMA), insula, midcingulate gyrus (MCG), putamen, thalamus, and in the anterior and posterior lobes of the cerebellum. During tongue movement, activation was seen in the inferolateral M1, SMA, MCG, putamen, thalamus, and anterior and posterior lobes of the cerebellum. Tongue activation was found in the proximity of, but not overlapping with, the PFMC activation. Connectivity analysis demonstrated differences in neural networks involved in PFMC and tongue movement. Conclusion: This study demonstrated that 7T-fMRI can be used to visualize brain areas involved in pelvic floor control in the whole brain of single subjects and defined the specific brain areas involved in PFMC. Distinct differences between brain mechanisms controlling the pelvic floor and tongue movements were demonstrated using connectivity analysis

Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

BACKGROUND: Machine learning algorithms hold the potential to contribute to fast and accurate detection of large vessel occlusion (LVO) in patients with suspected acute ischemic stroke. We assessed the diagnostic performance of an automated LVO detection algorithm on CT angiography (CTA). METHODS: Data from the MR CLEAN Registry and PRESTO were used including patients with and without LVO. CTA data were analyzed by the algorithm for detection and localization of LVO (intracranial internal carotid artery (ICA)/ICA terminus (ICA-T), M1, or M2). Assessments done by expert neuroradiologists were used as reference. Diagnostic performance was assessed for detection of LVO and per occlusion location by means of sensitivity, specificity, and area under the curve (AUC). RESULTS: We analyzed CTAs of 1110 patients from the MR CLEAN Registry (median age (IQR) 71 years (60-80); 584 men; 1110 with LVO) and of 646 patients from PRESTO (median age (IQR) 73 years (62-82); 358 men; 141 with and 505 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and a sensitivity of 72%, specificity of 78%, and AUC of 0.75 in PRESTO. Sensitivity per occlusion location was 88% for ICA/ICA-T, 94% for M1, and 72% for M2 occlusion in the MR CLEAN Registry, and 80% for ICA/ICA-T, 95% for M1, and 49% for M2 occlusion in PRESTO. CONCLUSION: The algorithm provided a high detection rate for proximal LVO, but performance varied significantly by occlusion location. Detection of M2 occlusion needs further improvement

Universal scaling behavior of non-equilibrium phase transitions

One of the most impressive features of continuous phase transitions is the concept of universality, that allows to group the great variety of different critical phenomena into a small number of universality classes. All systems belonging to a given universality class have the same critical exponents, and certain scaling functions become identical near the critical point. It is the aim of this work to demonstrate the usefulness of universal scaling functions for the analysis of non-equilibrium phase transitions. In order to limit the coverage of this article, we focus on a particular class of non-equilibrium critical phenomena, the so-called absorbing phase transitions. These phase transitions arise from a competition of opposing processes, usually creation and annihilation processes. The transition point separates an active phase and an absorbing phase in which the dynamics is frozen. A systematic analysis of universal scaling functions of absorbing phase transitions is presented, including static, dynamical, and finite-size scaling measurements. As a result a picture gallery of universal scaling functions is presented which allows to identify and to distinguish universality classes.Comment: review article, 160 pages, 60 figures include

The diagnostic performance of artificial intelligence algorithms for identifying M2 segment middle cerebral artery occlusions: A systematic review and meta-analysis

BACKGROUND: Artificial intelligence (AI)-based algorithms have been developed to facilitate rapid and accurate computed tomography angiography (CTA) assessment in proximal large vessel occlusion (LVO) acute ischemic stroke, including internal carotid artery and M1 occlusions. In clinical practice, however, the detection of medium vessel occlusion (MeVO) represents an ongoing diagnostic challenge in which the added value of AI remains unclear. PURPOSE: To assess the diagnostic performance of AI platforms for detecting M2 occlusions. METHODS: Studies that report the diagnostic performance of AI-based detection of M2 occlusions were screened, and sensitivity and specificity data were extracted using the semi-automated AutoLit software (Nested Knowledge, MN) platform. STATA (version 16 IC; Stata Corporation, College Station, Texas, USA) was used to conduct all analyses. RESULTS: Eight studies with a low risk of bias and significant heterogeneity were included in the quantitative and qualitative synthesis. The pooled estimates of sensitivity and specificity of AI platforms for M2 occlusion detection were 64% (95% CI, 53 to 74%) and 97% (95% CI, 84 to 100%), respectively. The area under the curve (AUC) in the SROC curve was 0.79 (95% CI, 0.74 to 0.83). CONCLUSION: The current performance of the AI-based algorithm makes it more suitable as an adjunctive confirmatory tool rather than as an independent one for M2 occlusions. With the rapid development of such algorithms, it is anticipated that newer generations will likely perform much better

Comparison of diffusion weighted imaging b0 with T2*-weighted gradient echo or susceptibility weighted imaging for intracranial hemorrhage detection after reperfusion therapy for ischemic stroke

Purpose: Diffusion-weighted imaging (DWI) b0 may be able to substitute T2*-weighted gradient echo (GRE) or susceptibility-weighted imaging (SWI) in case of comparable detection of intracranial hemorrhage (ICH), thereby reducing MRI examination time. We evaluated the diagnostic accuracy of DWI b0 compared to T2*GRE or SWI for detection of ICH after reperfusion therapy for ischemic stroke. Methods: We pooled 300 follow-up MRI scans acquired within 1 week after reperfusion therapy. Six neuroradiologists each rated DWI images (b0 and b1000; b0 as index test) of 100 patients and, after a minimum of 4 weeks, T2*GRE or SWI images (reference standard) paired with DWI images of the same patients. Readers assessed the presence of ICH (yes/no) and type of ICH according to the Heidelberg Bleeding Classification. We determined the sensitivity and specificity of DWI b0 for detection of any ICH, and the sensitivity for detection of hemorrhagic infarction (HI1 &amp; HI2) and parenchymal hematoma (PH1 &amp; PH2). Results: We analyzed 277 scans of ischemic stroke patients with complete image series and sufficient image quality (median age 65 years [interquartile range, 54–75], 158 [57%] men). For detection of any ICH on DWI b0, the sensitivity was 62% (95% CI: 50–76) and specificity 96% (95% CI: 93–99). The sensitivity of DWI b0 was 52% (95% CI: 28–68) for detection of hemorrhagic infarction and 84% (95% CI: 70–92) for parenchymal hematoma. Conclusion: DWI b0 is inferior for detection of ICH compared to T2*GRE/SWI, especially for smaller and more subtle hemorrhages. Follow-up MRI protocols should include T2*GRE/SWI for detection of ICH after reperfusion therapy.</p

Brain atrophy and endovascular treatment effect in acute ischemic stroke: a secondary analysis of the MR CLEAN trial

Background: Brain atrophy is suggested to impair the potential for functional recovery after acute ischemic stroke. We assessed whether the effect of endovascular treatment is modified by brain atrophy in patients with acute ischemic stroke due to large vessel occlusion. Methods: We used data from MR CLEAN, a multicenter trial including patients with acute ischemic stroke due to anterior circulation large vessel occlusion randomized to endovascular treatment plus medical care (intervention) versus medical care alone (control). We segmented total brain volume (TBV) and intracranial volume (ICV) on baseline non-contrast computed tomography (n = 410). Next, we determined the degree of atrophy as the proportion of brain volume in relation to head size (1 − TBV/ICV) × 100%, analyzed as continuous variable and in tertiles. The primary outcome was a shift towards better functional outcome on the modified Rankin Scale expressed as adjusted common odds ratio. Treatment effect modification was tested using an interaction term between brain atrophy (as continuous variable) and treatment allocation. Results: We found that brain atrophy significantly modified the effect of endovascular treatment on functional outcome (P for interaction = 0.04). Endovascular treatment led to larger shifts towards better functional outcome in the higher compared to the lower range of atrophy (adjusted common odds ratio, 1.86 [95% CI: 0.97–3.56] in the lowest tertile vs. 1.97 [95% CI: 1.03–3.74] in the middle tertile vs. 3.15 [95% CI: 1.59–6.24] in the highest tertile). Conclusion: Benefit of endovascular treatment is larger in the higher compared to the lower range of atrophy, demonstrating that advanced atrophy should not be used as an argument to withhold endovascular treatment

Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

BACKGROUND: Machine learning algorithms hold the potential to contribute to fast and accurate detection of large vessel occlusion (LVO) in patients with suspected acute ischemic stroke. We assessed the diagnostic performance of an automated LVO detection algorithm on CT angiography (CTA). METHODS: Data from the MR CLEAN Registry and PRESTO were used including patients with and without LVO. CTA data were analyzed by the algorithm for detection and localization of LVO (intracranial internal carotid artery (ICA)/ICA terminus (ICA-T), M1, or M2). Assessments done by expert neuroradiologists were used as reference. Diagnostic performance was assessed for detection of LVO and per occlusion location by means of sensitivity, specificity, and area under the curve (AUC). RESULTS: We analyzed CTAs of 1110 patients from the MR CLEAN Registry (median age (IQR) 71 years (60–80); 584 men; 1110 with LVO) and of 646 patients from PRESTO (median age (IQR) 73 years (62–82); 358 men; 141 with and 505 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and a sensitivity of 72%, specificity of 78%, and AUC of 0.75 in PRESTO. Sensitivity per occlusion location was 88% for ICA/ICA-T, 94% for M1, and 72% for M2 occlusion in the MR CLEAN Registry, and 80% for ICA/ICA-T, 95% for M1, and 49% for M2 occlusion in PRESTO. CONCLUSION: The algorithm provided a high detection rate for proximal LVO, but performance varied significantly by occlusion location. Detection of M2 occlusion needs further improvement

Type of intracranial hemorrhage after endovascular stroke treatment: association with functional outcome

Background: Intracranial hemorrhage (ICH) is a frequent complication after endovascular stroke treatment. Objective: To assess the association of the occurrence and type of ICH after endovascular treatment (EVT) with functional outcome. Methods: We analyzed data from the MR CLEAN-NO IV and MR CLEAN-MED trials. Both trials included adult patients with ischemic stroke with a large vessel occlusion in the anterior circulation, who were eligible for EVT. ICH was classified (1) as asymptomatic or symptomatic (concomitant neurological deterioration of ≥4 points on the NIHSS, or ≥2 points on 1 NIHSS item), and (2) according to the Heidelberg Bleeding Classification. We used multivariable ordinal logistic regression analyses to assess the association of the occurrence and type of ICH with the modified Rankin Scale score at 90 days. Results: Of 1017 included patients, 331 (33%) had an asymptomatic ICH, and 90 (9%) had a symptomatic ICH. Compared with no ICH, both asymptomatic (adjusted common OR (acOR)=0.76; 95% CI 0.58 to 0.98) and symptomatic (acOR=0.07; 95% CI 0.04 to 0.14) ICH were associated with worse functional outcome. In particular, isolated parenchymal hematoma type 2 (acOR=0.37; 95% CI 0.14 to 0.95), combined parenchymal hematoma with hemorrhage outside infarcted brain tissue (acOR=0.17; 95% CI 0.10 to 0.30), and combined hemorrhages outside infarcted brain tissue (acOR=0.14; 95% CI 0.03 to 0.74) were associated with worse functional outcome than no ICH. Strength of the association of ICH with functional outcome depends on the type of ICH. Although the association is stronger for symptomatic ICH, asymptomatic ICH after EVT is also associated with worse functional outcome