Physiological instability is linked to mortality in primary central nervous system lymphoma: A case-control fMRI study.

Primary central nervous system lymphoma (PCNSL) is an aggressive brain disease where lymphocytes invade along perivascular spaces of arteries and veins. The invasion markedly changes (peri)vascular structures but its effect on physiological brain pulsations has not been previously studied. Using physiological magnetic resonance encephalography (MREGBOLD ) scanning, this study aims to quantify the extent to which (peri)vascular PCNSL involvement alters the stability of physiological brain pulsations mediated by cerebral vasculature. Clinical implications and relevance were explored. In this study, 21 PCNSL patients (median 67y; 38% females) and 30 healthy age-matched controls (median 63y; 73% females) were scanned for MREGBOLD signal during 2018-2021. Motion effects were removed. Voxel-by-voxel Coefficient of Variation (CV) maps of MREGBOLD signal was calculated to examine the stability of physiological brain pulsations. Group-level differences in CV were examined using nonparametric covariate-adjusted tests. Subject-level CV alterations were examined against control population Z-score maps wherein clusters of increased CV values were detected. Spatial distributions of clusters and findings from routine clinical neuroimaging were compared [contrast-enhanced, diffusion-weighted, fluid-attenuated inversion recovery (FLAIR) data]. Whole-brain mean CV was linked to short-term mortality with 100% sensitivity and 100% specificity, as all deceased patients revealed higher values (n = 5, median 0.055) than surviving patients (n = 16, median 0.028) (p < .0001). After adjusting for medication, head motion, and age, patients revealed higher CV values (group median 0.035) than healthy controls (group median 0.024) around arterial territories (p ≤ .001). Abnormal clusters (median 1.10 × 105 mm3 ) extended spatially beyond FLAIR lesions (median 0.62 × 105 mm3 ) with differences in volumes (p = .0055).

Maternal prepregnancy body mass index and offspring white matter microstructure: results from three birth cohorts.

BACKGROUND AND AIMS: Prepregnancy maternal obesity is a global health problem and has been associated with offspring metabolic and mental ill-health. However, there is a knowledge gap in understanding potential neurobiological factors related to these associations. This study explored the relation between maternal prepregnancy body mass index (BMI) and offspring brain white matter microstructure at the age of 6, 10, and 26 years in three independent cohorts. SUBJECTS AND METHODS: The study used data from three European birth cohorts (n = 116 children aged 6 years, n = 2466 children aged 10 years, and n = 437 young adults aged 26 years). Information on maternal prepregnancy BMI was obtained before or during pregnancy and offspring brain white matter microstructure was measured at age 6, 10, or 26 years. We used magnetic resonance imaging-derived fractional anisotropy (FA) and mean diffusivity (MD) as measures of white matter microstructure in the brainstem, callosal, limbic, association, and projection tracts. Linear regressions were fitted to examine the association of maternal BMI and offspring white matter microstructure, adjusting for several socioeconomic and lifestyle-related confounders, including education, smoking, and alcohol use. RESULTS: Maternal BMI was associated with higher FA and lower MD in multiple brain tracts, for example, association and projection fibers, in offspring aged 10 and 26 years, but not at 6 years. In each cohort maternal BMI was related to different white matter tract and thus no common associations across the cohorts were found. CONCLUSIONS: Maternal BMI was associated with higher FA and lower MD in multiple brain tracts in offspring aged 10 and 26 years, but not at 6 years of age. Future studies should examine whether our observations can be replicated and explore the potential causal nature of the findings.

Fluctuations of the EEG-fMRI correlation reflect intrinsic strength of functional connectivity in default mode network.

Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting-state functional connectivity is temporally variable in human brain. Combined full-band electroencephalography-fMRI (fbEEG-fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood-oxygen-level-dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG-fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting-state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short-time-window comparisons of infra-slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG-BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG-fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG-BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on-average low correlations between infra-slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.

Brain structural deficits and working memory fMRI dysfunction in young adults who were diagnosed with ADHD in adolescence.

When adolescents with ADHD enter adulthood, some no longer meet disorder diagnostic criteria but it is unknown if biological and cognitive abnorma lities persist. We tested the hypothesis that people diagnosed with ADHD during adolescence present residual brain abnormalities both in brain structure and in working memory brain function. 83 young adults (aged 20-24 years) from the Northern Finland 1986 Birth Cohort were classified as diagnosed with ADHD in adolescence (adolescence ADHD, n = 49) or a control group (n = 34). Only one patient had received medication for ADHD. T1-weighted brain scans were acquired and processed in a voxel-based analysis using permutation-based statistics. A sub-sample of both groups (ADHD, n = 21; controls n = 23) also performed a Sternberg working memory task whilst acquiring fMRI data. Areas of structural difference were used as a region of interest to evaluate the implications that structural abnormalities found in the ADHD group might have on working memory function. There was lower grey matter volume bilaterally in adolescence ADHD participants in the caudate (p < 0.05 FWE corrected across the whole brain) at age 20-24. Working memory was poorer in adolescence ADHD participants, with associated failure to show normal load-dependent caudate activation. Young adults diagnosed with ADHD in adolescence have structural and functional deficits in the caudate associated with abnormal working memory function. These findings are not secondary to stimulant treatment, and emphasise the importance of taking a wider perspective on ADHD outcomes than simply whether or not a particular patient meets diagnostic criteria at any given point in time.

Attention and Working Memory in Adolescents with Autism Spectrum Disorder: A Functional MRI Study.

The present study examined attention and memory load-dependent differences in the brain activation and deactivation patterns between adolescents with autism spectrum disorders (ASDs) and typically developing (TD) controls using functional magnetic resonance imaging. Attentional (0-back) and working memory (WM; 2-back) processing and load differences (0 vs. 2-back) were analysed. WM-related areas activated and default mode network deactivated normally in ASDs as a function of task load. ASDs performed the attentional 0-back task similarly to TD controls but showed increased deactivation in cerebellum and right temporal cortical areas and weaker activation in other cerebellar areas. Increasing task load resulted in multiple responses in ASDs compared to TD and in inadequate modulation of brain activity in right insula, primary somatosensory, motor and auditory cortices. The changes during attentional task may reflect compensatory mechanisms enabling normal behavioral performance. The inadequate memory load-dependent modulation of activity suggests diminished compensatory potential in ASD.

Infra-slow EEG fluctuations are correlated with resting-state network dynamics in fMRI.

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.

DTI abnormalities in adults with past history of attention deficit hyperactivity disorder: a tract-based spatial statistics study.

BACKGROUND: Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique enabling visualization and measurement of white matter tracts. Attention deficit hyperactivity disorder (ADHD) has been studied with DTI earlier with variable results, yet there is little research on remitted ADHD. PURPOSE: To compare the brain white matter between ADHD drug naïve subjects whose ADHD symptoms have mostly subsided and healthy controls. MATERIAL AND METHODS: Tract-based spatial statistics (TBSS) was used to compare 30 subjects with adolescent ADHD with control subjects at the age of 22-23 years. The study population was derived from a population-based Northern Finland Birth Cohort 1986. Fractional anisotropy (FA), mean diffusivity (MD), and measures of diffusion direction (λ1-3) were calculated. Permutation testing was used to test for differences in mean values of FA, MD, and λ1-3 between the groups. The results were corrected for multiple comparisons across the whole white matter skeleton. RESULTS: The ADHD group showed increased FA related to decreased radial diffusivity in the left forceps minor (P < 0.05). In the vicinity along the same tract, axial diffusion was significantly decreased without any significant effect on FA. No between-group difference in MD was observed. Regressor analysis revealed no gender-, IQ- or GAF-related changes. After removal of left handed subjects the statistical significance was only barely lost. CONCLUSION: In a setting with remitted ADHD, the results may represent a compensatory mechanism in the left forceps minor.

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.

The relative brain signal variability increases in the behavioral variant of frontotemporal dementia and Alzheimer's disease but not in schizophrenia.

Overlapping symptoms between Alzheimer's disease (AD), behavioral variant of frontotemporal dementia (bvFTD), and schizophrenia (SZ) can lead to misdiagnosis and delays in appropriate treatment, especially in cases of early-onset dementia. To determine the potential of brain signal variability as a diagnostic tool, we assessed the coefficient of variation of the BOLD signal (CVBOLD) in 234 participants spanning bvFTD (n = 53), AD (n = 17), SZ (n = 23), and controls (n = 141). All underwent functional and structural MRI scans. Data unveiled a notable increase in CVBOLD in bvFTD patients across both datasets (local and international, p < 0.05), revealing an association with clinical scores (CDR and MMSE, r = 0.46 and r = -0.48, p < 0.0001). While SZ and control group demonstrated no significant differences, a comparative analysis between AD and bvFTD patients spotlighted elevated CVBOLD in the frontopolar cortices for the latter (p < 0.05). Furthermore, CVBOLD not only presented excellent diagnostic accuracy for bvFTD (AUC 0.78-0.95) but also showcased longitudinal repeatability. During a one-year follow-up, the CVBOLD levels increased by an average of 35% in the bvFTD group, compared to a 2% increase in the control group (p < 0.05). Our findings suggest that CVBOLD holds promise as a biomarker for bvFTD, offering potential for monitoring disease progression and differentiating bvFTD from AD and SZ.

Cerebral tissue oxygenation response to brain irradiation measured during clinical radiotherapy.

Significance: Cancer therapy treatments produce extensive changes in the physiological and morphological properties of tissues, which are also individual dependent. Currently, a key challenge involves developing more tailored cancer therapy, and consequently, individual biological response measurement during therapy, such as tumor hypoxia, is of high interest. This is the first time human cerebral haemodynamics and cerebral tissue oxygenation index (TOI) changes were measured during the irradiation in clinical radiotherapy and functional near-infrared spectroscopy (fNIRS) technique was demonstrated as a feasible technique for clinical use in radiotherapy, based on 34 online patient measurements. Aim: Our aim is to develop predictive biomarkers and noninvasive real-time methods to establish the effect of radiotherapy during treatment as well as to optimize radiotherapy dose planning for individual patients. In particular, fNIRS-based technique could offer an effective and clinically feasible online technique for continuous monitoring of brain tissue hypoxia and responses to chemo- and radiotherapy, which involves modulating tumor oxygenation to increase or decrease tumor hypoxia. We aim to show that fNIRS is feasible for repeatability measuring in patient radiotherapy, the temporal alterations of tissue oxygenation induced by radiation. Approach: Fiber optics setup using multiwavelength fNIRS was built and combined with a medical linear accelerator to measure cerebral tissue oxygenation changes during the whole-brain radiotherapy treatment, where the radiation dose is given in whole brain area only preventing dosage to eyes. Correlation of temporal alterations in cerebral haemodynamics and TOI response to brain irradiation was quantified. Results: Online fNIRS patient measurement of cerebral haemodynamics during clinical brain radiotherapy is feasible in clinical environment, and results based on 34 patient measurements show strong temporal alterations in cerebral haemodynamics and decrease in TOI during brain irradiation and confirmed the repeatability. Our proof-of-concept study shows evidently that irradiation causes characteristic immediate changes in brain tissue oxygenation. Conclusions: In particular, TOI seems to be a sensitive parameter to observe the tissue effects of radiotherapy. Monitoring the real-time interactions between the subjected radiation dose and corresponding haemodynamic effects may provide important tool for the researchers and clinicians in the field of radiotherapy. Eventually, presented fNIRS technique could be used for improving dose planning and safety control for individual patients.

Effects of repeatability measures on results of fMRI sICA: a study on simulated and real resting-state effects.

Spatial independent components analysis (sICA) has become a widely applied data-driven method for fMRI data, especially for resting-state studies. These sICA approaches are often based on iterative estimation algorithms and there are concerns about accuracy due to noise. Repeatability measures such as ICASSO, RAICAR and ARABICA have been introduced as remedies but information on their effects on estimates is limited. The contribution of this study was to provide more of such information and test if the repeatability analyses are necessary. We compared FastICA-based ordinary and repeatability approaches concerning mixing vector estimates. Comparisons included original FastICA, FSL4 Melodic FastICA and original and modified ICASSO. The effects of bootstrapping and convergence threshold were evaluated. The results show that there is only moderate improvement due to repeatability measures and only in the bootstrapping case. Bootstrapping attenuated power from time courses of resting-state network related ICs at frequencies higher than 0.1 Hz and made subsets of low frequency oscillations more emphasized IC-wise. The convergence threshold did not have a significant role concerning the accuracy of estimates. The performance results suggest that repeatability measures or strict converge criteria might not be needed in sICA analyses of fMRI data. Consequently, the results in existing sICA fMRI literature are probably valid in this sense. A decreased accuracy of original bootstrapping ICASSO was observed and corrected by using centrotype mixing estimates but the results warrant for thorough evaluations of data-driven methods in general. Also, given the fMRI-specific considerations, further development of sICA methods is strongly encouraged.

Directional connectivity of resting state human fMRI data using cascaded ICA-PDC analysis.

BACKGROUND: Directional connectivity measures, such as partial directed coherence (PDC), give us means to explore effective connectivity in the human brain. By utilizing independent component analysis (ICA), the original data-set reduction was performed for further PDC analysis. PURPOSE: To test this cascaded ICA-PDC approach in causality studies of human functional magnetic resonance imaging (fMRI) data. MATERIAL AND METHODS: Resting state group data was imaged from 55 subjects using a 1.5 T scanner (TR 1800 ms, 250 volumes). Temporal concatenation group ICA in a probabilistic ICA and further repeatability runs (n = 200) were overtaken. The reduced data-set included the time series presentation of the following nine ICA components: secondary somatosensory cortex, inferior temporal gyrus, intracalcarine cortex, primary auditory cortex, amygdala, putamen and the frontal medial cortex, posterior cingulate cortex and precuneus, comprising the default mode network components. Re-normalized PDC (rPDC) values were computed to determine directional connectivity at the group level at each frequency. RESULTS: The integrative role was suggested for precuneus while the role of major divergence region may be proposed to primary auditory cortex and amygdala. CONCLUSION: This study demonstrates the potential of the cascaded ICA-PDC approach in directional connectivity studies of human fMRI.

White matter in autism spectrum disorders - evidence of impaired fiber formation.

BACKGROUND: Diffusion tensor imaging (DTI) enables measurements and visualization of the microstructure of neural fiber tracts. The existing literature on autism spectrum disorders (ASDs) and DTI is heterogenous both regarding methodology and results. PURPOSE: To compare brain white matter of high-functioning individuals with ASDs and controls. MATERIAL AND METHODS: Tract-based spatial statistics (TBSS), a voxel-based approach to DTI, was used to compare 27 subjects with ASDs (mean age 14.7 years, range 11.4-17.6 years, 20 boys, 7 girls) and 26 control subjects (mean age 14.5 years, range 11.7-17.3 years, 17 boys, 9 girls). Mean fractional anisotropy (FA) image (skeleton) was created and each subject's aligned FA data were then projected onto this skeleton. Voxelwise cross-subject statistics on the skeletonized FA data, mean diffusivity (MD), and measures of diffusion direction were calculated. Importantly, the data were corrected across the whole image instead of using ROI-based methods. RESULTS: The ASD group showed significantly greater FA (P < 0.05, corrected) in the area containing clusters of optic radiation and the right inferior fronto-occipital fasciculus (iFOF). In the same area, λ(3) (representing transverse diffusion) was significantly reduced in the ASD group. No age-related changes were found. CONCLUSION: The results suggest that the reduced transverse diffusion within the iFOF is related to abnormal information flow between the insular salience processing areas and occipital visual areas.

The effect of model order selection in group PICA.

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 < or =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.

A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study.

A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. In the AEC group, created contours (prostate, seminal vesicles, bladder, rectum, femoral heads and penile bulb) were edited, whereas the MC group included empty datasets for MC. In one clinic, lymph node CTV delineations were evaluated for interobserver variability. Compared to MC, the mean time saved using the AST was 12 min for the whole data set (46%) and 12 min for the lymph node CTV (60%), respectively. The delineation consistency between MC and AEC groups according to the Dice similarity coefficient (DSC) improved from 0.78 to 0.94 for the whole data set and from 0.76 to 0.91 for the lymph nodes. The mean DSCs between MC and AC for all six clinics were 0.82 for prostate, 0.72 for seminal vesicles, 0.93 for bladder, 0.84 for rectum, 0.69 for femoral heads and 0.51 for penile bulb. This study proves that using a general DL-based AST for CT images saves time and improves consistency.

Functional segmentation of the brain cortex using high model order group PICA.

Baseline activity of resting state brain networks (RSN) in a resting subject has become one of the fastest growing research topics in neuroimaging. It has been shown that up to 12 RSNs can be differentiated using an independent component analysis (ICA) of the blood oxygen level dependent (BOLD) resting state data. In this study, we investigate how many RSN signal sources can be separated from the entire brain cortex using high dimension ICA analysis from a group dataset. Group data from 55 subjects was analyzed using temporal concatenation and a probabilistic independent component analysis algorithm. ICA repeatability testing verified that 60 of the 70 computed components were robustly detectable. Forty-two independent signal sources were identifiable as RSN, and 28 were related to artifacts or other noninterest sources (non-RSN). The depicted RSNs bore a closer match to functional neuroanatomy than the previously reported RSN components. The non-RSN sources have significantly lower temporal intersource connectivity than the RSN (P < 0.0003). We conclude that the high model order ICA of the group BOLD data enables functional segmentation of the brain cortex. The method enables new approaches to causality and connectivity analysis with more specific anatomical details.

COMPARISON OF ORGAN-BASED TUBE CURRENT MODULATION AND BISMUTH SHIELDING IN CHEST CT: EFFECT ON THE IMAGE QUALITY AND THE PATIENT DOSE.

The aim of the study was to compare the absorbed doses and image quality of organ-based tube current modulation (OBTCM) and bismuth shielding of breasts and thyroid against regular tube current modulation in chest CT scan. An anthropomorphic phantom and MOSFET dosemeters were used to evaluate absorbed doses. Image quality was assessed from HU and noise. Relative to the reference scan, the average absorbed dose reduction with OBTCM was 5.2% and with bismuth shields 24.2%. Difference in HU values compared to the reference varied between -4.1 and 4.2 HU in OBTCM scan and between -22.2 and 118.6 HU with bismuth shields. Image noise levels varied between 10.0 to 26.3 HU in the reference scan, from 9.6 to 27.7 HU for the OBTCM scan and from 11.9 to 43.9 HU in the bismuth scan. The use of bismuth shields provided greatest dose reduction compared to the investigated OBTCM.

Brain response to facial expressions in adults with adolescent ADHD.

The symptoms of ADHD tend to have continuity to adulthood even though the diagnostic criteria were no longer fulfilled. The aim of our study was to find out possible differences in BOLD signal in the face-processing network between adults with previous ADHD (pADHD, n = 23) and controls (n = 29) from the same birth cohort when viewing dynamic facial expressions. The brain imaging was performed using a General Electric Signa 1.5 Tesla HDX. Dynamic facial expression stimuli included happy and fearful expressions. The pADHD group demonstrated elevated activity in the left parietal area during fearful facial expression. The Network Based Statistics including multiple areas demonstrated higher functional connectivity in attention related network during visual exposure to happy faces in the pADHD group. Conclusions: We found differences in brain responses to facial emotional expressions in individuals with previous ADHD compared to control group in a number of brain regions including areas linked to processing of facial emotional expressions and attention. This might indicate that although these individuals no longer fulfill the ADHD diagnosis, they exhibit overactive network properties affecting facial processing.

Real-time monitoring of human blood-brain barrier disruption.

Chemotherapy aided by opening of the blood-brain barrier with intra-arterial infusion of hyperosmolar mannitol improves the outcome in primary central nervous system lymphoma. Proper opening of the blood-brain barrier is crucial for the treatment, yet there are no means available for its real-time monitoring. The intact blood-brain barrier maintains a mV-level electrical potential difference between blood and brain tissue, giving rise to a measurable electrical signal at the scalp. Therefore, we used direct-current electroencephalography (DC-EEG) to characterize the spatiotemporal behavior of scalp-recorded slow electrical signals during blood-brain barrier opening. Nine anesthetized patients receiving chemotherapy were monitored continuously during 47 blood-brain barrier openings induced by carotid or vertebral artery mannitol infusion. Left or right carotid artery mannitol infusion generated a strongly lateralized DC-EEG response that began with a 2 min negative shift of up to 2000 µV followed by a positive shift lasting up to 20 min above the infused carotid artery territory, whereas contralateral responses were of opposite polarity. Vertebral artery mannitol infusion gave rise to a minimally lateralized and more uniformly distributed slow negative response with a posterior-frontal gradient. Simultaneously performed near-infrared spectroscopy detected a multiphasic response beginning with mannitol-bolus induced dilution of blood and ending in a prolonged increase in the oxy/deoxyhemoglobin ratio. The pronounced DC-EEG shifts are readily accounted for by opening and sealing of the blood-brain barrier. These data show that DC-EEG is a promising real-time monitoring tool for blood-brain barrier disruption augmented drug delivery.

Body mass index and brain white matter structure in young adults at risk for psychosis - The Oulu Brain and Mind Study.

Antipsychotic medications and psychotic illness related factors may affect both weight and brain structure in people with psychosis. Genetically high-risk individuals offer an opportunity to study the relationship between body mass index (BMI) and brain structure free from these potential confounds. We examined the effect of BMI on white matter (WM) microstructure in subjects with familial risk for psychosis (FR). We used diffusion tensor imaging and tract-based spatial statistics to explore the effect of BMI on whole brain FA in 42 (13 males) participants with FR and 46 (16 males) control participants aged 20-25 years drawn from general population-based Northern Finland Birth Cohort 1986. We also measured axial, radial and mean diffusivities. Most of the participants were normal weight rather than obese. In the FR group, decrease in fractional anisotropy and increase in radial diffusivity were associated with an increase in BMI in several brain areas. In controls the opposite pattern was seen in participants with higher BMI. There was a statistically significant interaction between group and BMI on FA and radial and mean diffusivities. Our results suggest that the effect of BMI on WM differs between individuals with FR for psychosis and controls.