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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.

Funding Information: None. Publisher Copyright: © 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved. 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 (NCCTcore) from the non-contrast computed tomography (NCCT) with ischemic core volume (CTPcore) from CTP and the outcome of EVT to clarify if NCCTcore 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. NCCTcore and CTPcore 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 NCCTcore could be used to identify patients eligible to EVT, we sub-grouped patients based on NCCTcore volumes (>50 mL and ≥ 70 mL). Results: Out of 1743 patients, baseline NCCTcore, CTPcore 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 NCCTcore was 20 mL (10–42), CTPcore 4 mL (0–16), and FIV 5 mL (1–49). Out of 288 patients, 23 had NCCTcore ≥ 70 mL and 26 had CTPcore ≥ 70 mL. NCCTcore and CTPcore performed similarly well in predicting large FIV (≥70 ml). Conclusion: NCCTcore is a promising tool to identify patients that are not eligible to EVT due to large ischemic cores at baseline imaging.

Biomarkers sensitive to prodromal or early pathophysiological changes in Alzheimer’s disease (AD) symptoms could improve disease detection and enable timely interventions. Changes in brain hemodynamics may be associated with the main clinical AD symptoms. To test this possibility, we measured the variability of blood oxygen level-dependent (BOLD) signal in individuals from three independent datasets (totaling 80 AD patients and 90 controls). We detected a replicable increase in brain BOLD signal variability in the AD populations, which constituted a robust biomarker for clearly differentiating AD cases from controls. Fast BOLD scans showed that the elevated BOLD signal variability in AD arises mainly from cardiovascular brain pulsations. Manifesting in abnormal cerebral perfusion and cerebrospinal fluid convection, present observation presents a mechanism explaining earlier observations of impaired glymphatic clearance associated with AD in humans.

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

Recently discovered glymphatic brain clearance mechanisms utilizing physiological pulsations have been shown to fail at removing waste materials such as amyloid and tau plaques in neurodegenerative diseases. Since cardiovascular pulsations are a main driving force of the clearance, this research investigates if commonly available blood oxygen level-dependent (BOLD) signals at 1.5 and 3 T could detect abnormal physiological pulsations in neurodegenerative diseases. Coefficient of variation in BOLD signal (CVBOLD) was used to estimate contribution of physiological signals in Alzheimer’s disease (AD) and behavioural variant frontotemporal dementia (bvFTD). 17 AD patients and 18 bvFTD patients were compared to 24 control subjects imaged with a 1.5 T setup from a local institute. AD results were further verified with 3 T data from the Alzheimer’s disease neuroimaging initiative (ADNI) repository with 30 AD patients and 40 matched controls. Effect of motion and gray matter atrophy was evaluated and receiver operating characteristic (ROC) analyses was performed.The CVBOLD was higher in both AD and bvFTD groups compared to controls (p < 0.0005). The difference was not explained by head motion or gray matter atrophy. In AD patients, the CVBOLD alterations were localized in overlapping structures in both 1.5 T and 3 T data. Localization of the CVBOLD alterations was different in AD than in bvFTD. Areas where CVBOLD is higher in patient groups than in control group involved periventricular white matter, basal ganglia and multiple cortical structures. Notably, a robust difference between AD and bvFTD groups was found in the CVBOLD of frontal poles. In the analysis of diagnostic accuracy, the CVBOLD metrics area under the ROC for detecting disease ranged 0.85 – 0.96.ConclusionsThe analysis of brain physiological pulsations measured using CVBOLD reveals disease-specific alterations in both AD and bvFTD.

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.

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., published version, peerReviewed

• The Arab region is dealing with an intensifying threat of water scarcity which is being exacerbated by climate change and a growing population.

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.

Light is considered to modulate human brain function only via the retinal pathway, a way of thinking that we aimed to challenge in the present study. Literature provides evidence of inherent phototransduction for instance in the rat brain and there are potentially photosensitive opsin proteins like melanopsin and panopsin in the human brain too. In order to investigate a short term response, functional connectivity changes of the brain were studied in the resting state with functional magnetic resonance imaging during bright light stimulus via the ear canal. Lateral visual and sensorimotor networks showed increased functional connectivity in the light stimulus group compared to sham controls. The lateral visual network demonstrated slowly increasing functional connectivity on average and the same temporal characteristic was shared by diverse cerebellar brain regions. Hypothetical phototransduction signal pathways leading to responses in brain function are discussed as well as some observed effects and their possible link to the findings. Findings from this study together with the plausible photoreceptor candidates suggest that the brain possesses photosensitive properties, which will have interesting implications for the modulation of brain function and understanding the basic physiology of the brain.

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.

A non-invasive device for measuring blood oxygen variations in human brain is designed, implemented, and tested for MRI compatibility. The device is based on principles of near-IR reflectometry; power LEDs serve as sources of probing radiation delivered to patient skin surface through optical fibres. Numerical Monte Carlo simulations of probing radiation propagation in a multilayer brain model are performed to evaluate signal levels at different source — detector separations at three operation wavelengths and an additional wavelength of 915 nm. It is shown that the device can be applied for brain activity studies using power LEDs operating at 830 and 915 nm, while employment of wavelength of 660 nm requires an increased probing power. Employment of the wavelength of 592 nm in the current configuration is unreasonable.

Ongoing neuronal activity in the CNS waxes and wanes continuously across widespread spatial and temporal scales. In the human brain, these spontaneous fluctuations are salient in blood oxygenation level-dependent (BOLD) signals and correlated within specific brain systems or “intrinsic-connectivity networks.” In electrophysiological recordings, both the amplitude dynamics of fast (1–100 Hz) oscillations and the scalp potentials per se exhibit fluctuations in the same infra-slow (0.01–0.1 Hz) frequency range where the BOLD fluctuations are conspicuous. While several lines of evidence show that the BOLD fluctuations are correlated with fast-amplitude dynamics, it has remained unclear whether the infra-slow scalp potential fluctuations in full-band electroencephalography (fbEEG) are related to the resting-state BOLD signals. We used concurrent fbEEG and functional magnetic resonance imaging (fMRI) recordings to address the relationship of infra-slow fluctuations (ISFs) in scalp potentials and BOLD signals. We show here that independent components of fbEEG recordings are selectively correlated with subsets of cortical BOLD signals in specific task-positive and task-negative, fMRI-defined resting-state networks. This brain system-specific association indicates that infra-slow scalp potentials are directly associated with the endogenous fluctuations in neuronal activity levels. fbEEG thus yields a noninvasive, high-temporal resolution window into the dynamics of intrinsic connectivity networks. These results support the view that the slow potentials reflect changes in cortical excitability and shed light on neuronal substrates underlying both electrophysiological and behavioral ISFs.

Functional MRI studies have revealed changes in default-mode and salience networks in neurodegenerative dementias, especially in Alzheimer’s disease. The purpose of this study was to analyze the whole brain cortex resting state networks in patients with behavioral variant frontotemporal dementia by using resting state functional MRI. The group specific resting state networks were identified by high model order independent component analysis and a dual regression technique was used to detect between-group differences in the resting state networks with p

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.

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. Hum Brain Mapp 35:161–172, 2014. © 2012 Wiley Periodicals, Inc.

Independent component analysis (ICA) of functional MRI data is sensitive to model order selection. There is a lack of knowledge about the effect of increasing model order on independent components' (ICs) characteristics of resting state networks (RSNs). Probabilistic group ICA (group PICA) of 55 healthy control subjects resting state data was repeated 100 times using ICASSO repeatability software and after clustering of components, centrotype components were used for further analysis. Visual signal sources (VSS), default mode network (DMN), primary somatosensory (S(1)), secondary somatosensory (S(2)), primary motor cortex (M(1)), striatum, and precuneus (preC) components were chosen as components of interest to be evaluated by varying group probabilistic independent component analysis (PICA) model order between 10 and 200. At model order 10, DMN and VSS components fuse several functionally separate sources that at higher model orders branch into multiple components. Both volume and mean z‐score of components of interest showed significant (P < 0.05) changes as a function of model order. In conclusion, model order has a significant effect on ICs characteristics. Our findings suggest that using model orders ≤20 provides a general picture of large scale brain networks. However, detection of some components (i.e., S(1), S(2), and striatum) requires higher model order estimation. Model orders 30–40 showed spatial overlapping of some IC sources. Model orders 70 ± 10 offer a more detailed evaluation of RSNs in a group PICA setting. Model orders > 100 showed a decrease in ICA repeatability, but added no significance to either volume or mean z‐score results. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.

This report focuses on designing and implementing a non-invasive blood pressure measuring device capable of being used during magnetic resonance imaging. This device is based on measuring pulse wave velocity in arterial blood and using the obtained result to estimate diastolic blood pressure. Pulse transit times are measured by two fibre optical accelerometers placed over chest and carotid artery. The fabricated accelerometer contains two static fibres and a cantilever beam. The free end of the beam 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. Acceleration applied to the sensor causes deflection of the beam, whereupon the amount of reflected light changes. The sensor 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 conditional. No artifacts or distortions were detected.