Association of admission plasma glucose level and cerebral computed tomographic perfusion deficit volumes

INTRODUCTION

Hyperglycemia in acute ischemic stroke (AIS) is frequent and associated with worse outcome. Yet, strict glycemic control in AIS patients has failed to yield beneficial outcome. So far, the underlying pathophysiological mechanisms of admission hyperglycemia in AIS have remained not fully understood. We aimed to evaluate the yet equivocal association of hyperglycemia with computed tomographic perfusion (CTP) deficit volumes.

PATIENTS AND METHODS

We included 832 consecutive AIS and transient ischemic attack (TIA) patients who underwent CTP as a part of screening for recanalization treatment (stroke code) between 3/2018 and 10/2020, from the prospective cohort of Helsinki Stroke Quality Registry. Associations of admission glucose level (AGL) and CTP deficit volumes, namely ischemic core, defined as relative cerebral blood flow <30%, and hypoperfusion lesions Time-to-maximum (Tmax) >6 s and Tmax >10s, as determined with RAPID® software, were analyzed with a linear regression model adjusted for age, sex, C-reactive protein, and time from symptom onset to imaging.

RESULTS

AGL median was 6.8 mmol/L (interquartile range 5.9-8.0 mmol/L), and 222 (27%) patients were hyperglycemic (glucose >7.8 mmol/L) on admission. In non-diabetic patients (643 [77%]), AGL was significantly associated with volume of Tmax. >6 s (regression coefficient [RC] 4.8, 95% confidence interval [CI] 0.49-9.1), of Tmax >10s (RC 4.6, 95% CI 1.2-8.1), and of ischemic core (RC 2.6, 95% CI 0.64-4.6). No significant associations were shown in diabetic patients.

CONCLUSION

Admission hyperglycemia appears to be associated with both larger volume of hypoperfusion lesions and of ischemic core in non-diabetic stroke code patients with AIS and TIA.

Abstract WP108: Is Infarct Core Growth Truly Linear? Follow-up Infarct Volume Estimation By Rapid Baseline Infarct Growth Rate And Linear Model

Background: Current guidelines for recanalization treatment are based on the time window between symptom onset and treatment in addition to ischemic core and perfusion lesion volumes by computed tomography perfusion imaging (CTP). Linear growth of infarction is commonly assumed.The aim was to test, whether measured follow-up infract volume (FIV) could be approximated from the linear growth model (eFIV) based on CTP baseline infarct growth rate.We assumed the infarct growth to stop, when recanalization was achieved or when the eFIV reached the volume of the perfusion lesion (T max >6s volume).

Methods: All consecutive stroke code patients from 11/2015-9/2019 transferred to Helsinki University Hospital as candidates for endovascular treatment (EVT) were screened; patients with large vessel occlusion (LVO), EVT, CTP and known time of symptom onset were included to study.The infarct growth rate was calculated by dividing the CTP core by the time from symptom onset to baseline imaging.eFIV was calculated by infarct growth rate multiplied with the time from baseline imaging to recanalization or follow-up imaging. We assumed a performance of +/- 19% for the accuracy of the CTP core assessment. FIV was measured from the 24h non-enhanced computed tomography images. Recanalization was defined as modified Treatment in Cerebral Infarction (mTICI) scale as successful (TICI 2b or 3) or futile (TICI 0,1,2a).

Results: Out of 5234 patients, 48 had LVO and EVT, CTP imaging and known time of symptom onset (Figure 1). In 40/48 (83%) patients, infarct growth was not within the 19% margins of linear growth. eFIV exceeded FIV in 25/42 patients with successful recanalization (median absolute difference 25 mL,7-73).

Conclusions: eFIV from linearly approximated growth model did not support linear growth of the infarct.

Comparison of automated infarct core volume measures between non-contrast computed tomography and perfusion imaging in acute stroke code patients evaluated for potential endovascular treatment

INTRODUCTION

Patients with small core infarction and salvageable penumbra are likely to benefit from endovascular treatment (EVT). As computed tomography perfusion imaging (CTP) is not always available 24/7 for patient selection, many patients are transferred to stroke centers for CTP. We compared automatically measured infarct core volume (NCCT_core) from the non-contrast computed tomography (NCCT) with ischemic core volume (CTP_core) from CTP and the outcome of EVT to clarify if NCCT_core measurement alone is sufficient to identify patients that benefit from transfer to stroke centers for EVT.

PATIENTS AND METHODS

We included all consecutive stroke-code patients imaged with both NCCT and CTP at Helsinki University Hospital during 9/2016-01/2018. NCCT_core and CTP_core volumes were automatically calculated from the acute NCCT images. Follow-up infarct volume (FIV) was measured from 24 h follow-up NCCT to evaluate efficacy of EVT. To study whether NCCT_core could be used to identify patients eligible to EVT, we sub-grouped patients based on NCCT_core volumes (>50 mL and ≥ 70 mL).

RESULTS

Out of 1743 patients, baseline NCCT_core, CTP_core and follow-up NCCT was available for 288 patients. Median time from symptom onset to baseline imaging was 74 min (IQR 52-118), and time to follow-up imaging 24.15 h (22.25-26.33). Baseline NCCT_core was 20 mL (10-42), CTP_core 4 mL (0-16), and FIV 5 mL (1-49). Out of 288 patients, 23 had NCCT_core ≥ 70 mL and 26 had CTP_core ≥ 70 mL. NCCT_core and CTP_core performed similarly well in predicting large FIV (≥70 ml).

CONCLUSION

NCCT_core is a promising tool to identify patients that are not eligible to EVT due to large ischemic cores at baseline imaging.

Characteristics and Long-Term Outcome of 127 Children With Cerebral Arteriovenous Malformations

BACKGROUND

Population-based long-term data on pediatric patients with cerebral arteriovenous malformations (AVMs) are limited.

OBJECTIVE

To clarify the characteristics and long-term outcome of pediatric patients with AVM.

METHODS

A retrospective analysis was performed on 805 consecutive brain AVM patients admitted to a single center between 1942 and 2014. The patients were defined as children if they were under 18 yr at admission. Children were compared to an adult cohort. Changing patterns of presentation were also analyzed by decades of admission.

RESULTS

The patients comprised 127 children with a mean age of 12 yr. The mean follow-up time was 21 yr (range 0-62). Children presented more often with intracerebral hemorrhage (ICH) but less often with epilepsy than adults. Basal ganglia, cerebellar, and posterior paracallosal AVMs were more common in pediatric than in adult patients. Frontal and temporal AVMs, in contrast, were more common in adult than in pediatric patients. As the number of incidentally and epilepsy-diagnosed AVMs increased, ICH rates dropped in both cohorts. In total, 22 (82%) pediatric and 108 (39%) adult deaths were assessed as AVM related. After multivariate analysis, small AVM size and surgical treatment correlated with a favorable long-term outcome.

CONCLUSION

Hemorrhagic presentation was more common in children than in adults. This was also reflected as lower prevalence of epileptic presentation in the pediatric cohort. Lobar and cortical AVM locations were less frequent, whereas deep and cerebellar AVMs were more common in children. Hemorrhagic presentation correlated negatively with incidentally and epilepsy-diagnosed AVMs. In children, AVM was a major cause of death, but in adults, other factors contributed more commonly to mortality.

The Application of the Novel Grading Scale (Lawton-Young Grading System) to Predict the Outcome of Brain Arteriovenous Malformation

BACKGROUND

A supplementary grading scale (Supplemented Spetzler-Martin grade, Supp-SM) was introduced in 2010 as a refinement of the SM system to improve preoperative risk prediction of brain arteriovenous malformations (AVMs).

OBJECTIVE

To determine the ability to predict surgical outcomes using the Supp-SM grading scale.

METHODS

This retrospective study was conducted on 200 patients admitted to the Helsinki University Hospital between 2000 and 2014. The validity of the Supp-SM and SM grading systems was compared using the area under the receiver operating characteristic (AUROC) curves, with respect to the change between preoperative and early (3-4 mo) as well as final postoperative modified Rankin Scale (mRS) scores.

RESULTS

The performance of the Supp-SM was superior to that of the SM grading scale in the early follow-up (3-4 mo): AUROC = 0.57 (95% confidence interval [CI]: 0.49-0.65) for SM and AUROC = 0.67 (95% CI: 0.60-0.75) for Supp-SM. The Supp-SM performance continued improving over SM at the late follow-up: AUROC = 0.63 (95% CI: 0.55-0.71) for SM and AUROC = 0.70 (95% CI: 0.62-0.77) for Supp-SM. The perforating artery supply, which is not part of either grading system, plays an important role in the early follow-up outcome (P = .008; odds ratio: 2.95; 95% CI: 1.32-6.55) and in the late follow-up outcome (P < .001; odds ratio: 5.89; 95% CI: 2.49-13.91).

CONCLUSION

The Supp-SM grading system improves the outcome prediction accuracy and is a feasible alternative to the SMS, even for series with higher proportion of high-grade AVMs. However, perforators play important role on the outcome.

The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer's Disease and bvFTD

Resting-state fMRI results in neurodegenerative diseases have been somewhat conflicting. This may be due to complex partial volume effects of CSF in BOLD signal in patients with brain atrophy. To encounter this problem, we used a coefficient of variation (CV) map to highlight artifacts in the data, followed by analysis of gray matter voxels in order to minimize brain volume effects between groups. The effects of these measures were compared to whole brain ICA dual regression results in Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). 23 AD patients, 21 bvFTD patients and 25 healthy controls were included. The quality of the data was controlled by CV mapping. For detecting functional connectivity (FC) differences whole brain ICA (wbICA) and also segmented gray matter ICA (gmICA) followed by dual regression were conducted, both of which were performed both before and after data quality control. Decreased FC was detected in posterior DMN in the AD group and in the Salience network in the bvFTD group after combining CV quality control with gmICA. Before CV quality control, the decreased connectivity finding was not detectable in gmICA in neither of the groups. Same finding recurred when exclusion was based on randomization. The subjects excluded due to artifacts noticed in the CV maps had significantly lower temporal signal-to-noise ratio than the included subjects. Data quality measure CV is an effective tool in detecting artifacts from resting state analysis. CV reflects temporal dispersion of the BOLD signal stability and may thus be most helpful for spatial ICA, which has a blind spot in spatially correlating widespread artifacts. CV mapping in conjunction with gmICA yields results suiting previous findings both in AD and bvFTD.

Altered resting-state activity in seasonal affective disorder

At present, our knowledge about seasonal affective disorder (SAD) is based mainly up on clinical symptoms, epidemiology, behavioral characteristics and light therapy. Recently developed measures of resting-state functional brain activity might provide neurobiological markers of brain disorders. Studying functional brain activity in SAD could enhance our understanding of its nature and possible treatment strategies. Functional network connectivity (measured using ICA-dual regression), and amplitude of low-frequency fluctuations (ALFF) were measured in 45 antidepressant-free patients (39.78 ± 10.64, 30 ♀, 15 ♂) diagnosed with SAD and compared with age-, gender- and ethnicity-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging. After correcting for Type 1 error at high model orders (inter-RSN correction), SAD patients showed significantly increased functional connectivity in 11 of the 47 identified RSNs. Increased functional connectivity involved RSNs such as visual, sensorimotor, and attentional networks. Moreover, our results revealed that SAD patients compared with HCs showed significant higher ALFF in the visual and right sensorimotor cortex. Abnormally altered functional activity detected in SAD supports previously reported attentional and psychomotor symptoms in patients suffering from SAD. Further studies, particularly under task conditions, are needed in order to specifically investigate cognitive deficits in SAD.

GroupICA dual regression analysis of resting state networks in a behavioral variant of frontotemporal dementia

Functional MRI studies have revealed changes in default-mode and salience networks in neurodegenerative dementias, especially in Alzheimer's disease (AD). The purpose of this study was to analyze the whole brain cortex resting state networks (RSNs) in patients with behavioral variant frontotemporal dementia (bvFTD) by using resting state functional MRI (rfMRI). The group specific RSNs were identified by high model order independent component analysis (ICA) and a dual regression technique was used to detect between-group differences in the RSNs with p < 0.05 threshold corrected for multiple comparisons. A y-concatenation method was used to correct for multiple comparisons for multiple independent components, gray matter differences as well as the voxel level. We found increased connectivity in several networks within patients with bvFTD compared to the control group. The most prominent enhancement was seen in the right frontotemporal area and insula. A significant increase in functional connectivity was also detected in the left dorsal attention network (DAN), in anterior paracingulate—a default mode sub-network as well as in the anterior parts of the frontal pole. Notably the increased patterns of connectivity were seen in areas around atrophic regions. The present results demonstrate abnormal increased connectivity in several important brain networks including the DAN and default-mode network (DMN) in patients with bvFTD. These changes may be associated with decline in executive functions and attention as well as apathy, which are the major cognitive and neuropsychiatric defects in patients with frontotemporal dementia.

On applicability of PCA, voxel-wise variance normalization and dimensionality assumptions for sliding temporal window sICA in resting-state fMRI

Subject-level resting-state fMRI (RS-fMRI) spatial independent component analysis (sICA) may provide new ways to analyze the data when performed in the sliding time window. However, whether principal component analysis (PCA) and voxel-wise variance normalization (VN) are applicable pre-processing procedures in the sliding-window context, as they are for regular sICA, has not been addressed so far. Also model order selection requires further studies concerning sliding-window sICA. In this paper we have addressed these concerns. First, we compared PCA-retained subspaces concerning overlapping parts of consecutive temporal windows to answer whether in-window PCA and VN can confound comparisons between sICA analyses in consecutive windows. Second, we compared the PCA subspaces between windowed and full data to assess expected comparability between windowed and full-data sICA results. Third, temporal evolution of dimensionality estimates in RS-fMRI data sets was monitored to identify potential challenges in model order selection in a sliding-window sICA context. Our results illustrate that in-window VN can be safely used, in-window PCA is applicable with most window widths and that comparisons between windowed and full data should not be performed from a subspace similarity point of view. In addition, our studies on dimensionality estimates demonstrated that there are sustained, periodic and very case-specific changes in signal-to-noise ratio within RS-fMRI data sets. Consequently, dimensionality estimation is needed for well-founded model order determination in the sliding-window case. The observed periodic changes correspond to a frequency band of ≤0.1 Hz, which is commonly associated with brain activity in RS-fMRI and become on average most pronounced at window widths of 80 and 60 time points (144 and 108 s, respectively). Wider windows provided only slightly better comparability between consecutive windows, and 60 time point or shorter windows also provided the best comparability with full-data results. Further studies are needed to determine the cause for dimensionality variations.

A sliding time-window ICA reveals spatial variability of the default mode network in time

Recent evidence on resting-state networks in functional (connectivity) magnetic resonance imaging (fcMRI) suggests that there may be significant spatial variability of activity foci over time. This study used a sliding time window approach with the spatial domain-independent component analysis (SliTICA) to detect spatial maps of resting-state networks over time. The study hypothesis was that the spatial distribution of a functionally connected network would present marked variability over time. The spatial stability of successive sliding-window maps of the default mode network (DMN) from fcMRI data of 12 participants imaged in the resting state was analyzed. Control measures support previous findings on the stability of independent component analysis in measuring sliding-window sources accurately. The spatial similarity of successive DMN maps varied over time at low frequencies and presented a 1/f power spectral pattern. SliTICA maps show marked temporal variation within the DMN; a single voxel was detected inside a group DMN map in maximally 82% of time windows. Mapping of incidental connectivity reveals centrifugally increasing connectivity to the brain cortex outside the DMN core areas. In conclusion, SliTICA shows marked spatial variance of DMN activity in time, which may offer a more comprehensive measurement of the overall functional activity of a network.

Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity

Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.

Fibre optic sensor for non-invasive monitoring of blood pressure during MRI scanning

This report focuses on designing and implementing a non-invasive blood pressure (NIBP) measuring device capable of being used during magnetic resonance imaging (MRI). Based on measuring pulse wave velocity in arterial blood, the device uses the obtained result to estimate diastolic blood pressure. Pulse transit times are measured by two fibre optical accelerometers placed over the chest and carotid artery. The fabricated accelerometer contains two static fibres and a cantilever beam, whose free end is angled at 90 degrees to act as a reflecting surface. Optical fibres are used for both illuminating the surface and receiving the reflected light. When acceleration is applied to the sensor, it causes a deflection in the beam, thereby changing the amount of reflected light. The sensor's output voltage is proportional to the intensity of the reflected light. Tests conducted on the electronics and sensors inside an MRI room during scanning proved that the device is MR- compatible. No artifacts or distortions were detected.

Age-Related Differences in Functional Nodes of the Brain Cortex - A High Model Order Group ICA Study

Functional MRI measured with blood oxygen dependent (BOLD) contrast in the absence of intermittent tasks reflects spontaneous activity of so-called resting state networks (RSN) of the brain. Group level independent component analysis (ICA) of BOLD data can separate the human brain cortex into 42 independent RSNs. In this study we evaluated age-related effects from primary motor and sensory, and, higher level control RSNs. One hundred sixty-eight healthy subjects were scanned and divided into three groups: 55 adolescents (ADO, 13.2 ± 2.4 years), 59 young adults (YA, 22.2 ± 0.6 years), and 54 older adults (OA, 42.7 ± 0.5 years), all with normal IQ. High model order group probabilistic ICA components (70) were calculated and dual-regression analysis was used to compare 21 RSN's spatial differences between groups. The power spectra were derived from individual ICA mixing matrix time series of the group analyses for frequency domain analysis. We show that primary sensory and motor networks tend to alter more in younger age groups, whereas associative and higher level cognitive networks consolidate and re-arrange until older adulthood. The change has a common trend: both spatial extent and the low frequency power of the RSN's reduce with increasing age. We interpret these result as a sign of normal pruning via focusing of activity to less distributed local hubs.