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.

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 (MREG_BOLD ) 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 MREG_BOLD signal during 2018-2021. Motion effects were removed. Voxel-by-voxel Coefficient of Variation (CV) maps of MREG_BOLD 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).

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.

Prenatal exposure to maternal cigarette smoking and structural properties of the human corpus callosum

Alterations induced by prenatal exposure to nicotine have been observed in experimental (rodent) studies. While numerous developmental outcomes have been associated with prenatal exposure to maternal cigarette smoking (PEMCS) in humans, the possible relation with brain structure is less clear. Here we sought to elucidate the relation between PEMCS and structural properties of human corpus callosum in adolescence and early adulthood in a total of 1,747 youth. We deployed three community-based cohorts of 446 (age 25-27 years, 46% exposed), 934 (age 12-18 years, 47% exposed) and 367 individuals (age 18-21 years, 9% exposed). A mega-analysis revealed lower mean diffusivity in the callosal segments of exposed males. We speculate that prenatal exposure to maternal cigarette smoking disrupts the early programming of callosal structure and increases the relative portion of small-diameter fibres.

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.

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.

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.

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.

Cerebellar activity in young people with familial risk for psychosis--The Oulu Brain and Mind Study

OBJECTIVE

The cerebellum plays a critical role in cognition and behavior. Altered function of the cerebellum has been related to schizophrenia and psychosis but it is not known how this applies to spontaneous resting state activity in young people with familial risk for psychosis.

METHODS

We conducted resting-state functional MRI (R-fMRI) in 72 (29 male) young adults with a history of psychosis in one or both parents (FR) but without their own psychosis, and 72 (29 male) similarly healthy control subjects without parental psychosis. Both groups in the Oulu Brain and Mind Study were drawn from the Northern Finland Birth Cohort 1986. Participants were 20-25 years old. Parental psychosis was established using the Care Register for Health Care. R-fMRI data pre-processing was conducted using independent component analysis with 30 and 70 components. A dual regression technique was used to detect between-group differences in the cerebellum with p<0.05 threshold corrected for multiple comparisons.

RESULTS

FR participants demonstrated statistically significantly increased activity compared to control subjects in the anterior lobe of the right cerebellum in the analysis with 70 components. The volume of the increased activity was 73 mm(3). There was no difference between the groups in the analysis with 30 components.

CONCLUSION

The finding suggests that increased activity of the anterior lobe of the right cerebellum may be associated with increased vulnerability to psychosis. The finding is novel, and needs replication to be confirmed.

Longitudinal regional brain volume loss in schizophrenia: Relationship to antipsychotic medication and change in social function

BACKGROUND

Progressive brain volume loss in schizophrenia has been reported in previous studies but its cause and regional distribution remains unclear. We investigated progressive regional brain reductions in schizophrenia and correlations with potential mediators.

METHOD

Participants were drawn from the Northern Finland Birth Cohort 1966. A total of 33 schizophrenia individuals and 71 controls were MRI scanned at baseline (mean age=34.7, SD=0.77) and at follow-up (mean age=43.4, SD=0.44). Regional brain change differences and associations with clinical mediators were examined using FSL voxelwise SIENA.

RESULTS

Schizophrenia cases exhibited greater progressive brain reductions than controls, mainly in the frontal and temporal lobes. The degree of periventricular brain volume reductions were predicted by antipsychotic medication exposure at the fourth ventricular edge and by the number of days in hospital between the scans (a proxy measure of relapse duration) at the thalamic ventricular border. Decline in social and occupational functioning was associated with right supramarginal gyrus reduction.

CONCLUSION

Our findings are consistent with the possibility that antipsychotic medication exposure and time spent in relapse partially explain progressive brain reductions in schizophrenia. However, residual confounding could also account for the findings and caution must be applied before drawing causal inferences from associations demonstrated in observational studies of modest size. Less progressive brain volume loss in schizophrenia may indicate better preserved social and occupational functions.

Reading, listening and memory-related brain activity in children with early-stage temporal lobe epilepsy of unknown cause-an fMRI study

BACKGROUND AND AIMS

The changes in functional brain organization associated with paediatric epilepsy are largely unknown. Since children with epilepsy are at risk of developing learning difficulties even before or shortly after the onset of epilepsy, we assessed the functional organization of memory and language in paediatric patients with temporal lobe epilepsy (TLE) at an early stage in epilepsy.

METHODS

Functional magnetic resonance imaging was used to measure the blood oxygenation level-dependent (BOLD) response to four cognitive tasks measuring reading, story listening, memory encoding and retrieval in a population-based group of children with TLE of unknown cause (n = 21) and of normal intelligence and a healthy age and gender-matched control group (n = 21).

RESULTS

Significant BOLD response differences were found only in one of the four tasks. In the story listening task, significant differences were found in the right hemispheric temporal structures, thalamus and basal ganglia. Both activation and deactivation differed significantly between the groups, activation being increased and deactivation decreased in the TLE group. Furthermore, the patients with abnormal electroencephalograms (EEGs) showed significantly increased activation bilaterally in the temporal structures, basal ganglia and thalamus relative to those with normal EEGs. The patients with normal interictal EEGs had a significantly stronger deactivation than those with abnormal EEGs or the controls, the differences being located outside the temporal structures.

CONCLUSIONS

Our results suggest that TLE entails a widespread disruption of brain networks. This needs to be taken into consideration when evaluating learning abilities in patients with TLE. The thalamus seems to play an active role in TLE. The changes in deactivation may reflect neuronal inhibition.

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.

Functional mapping of dynamic happy and fearful facial expressions in young adults with familial risk for psychosis - Oulu Brain and Mind Study

BACKGROUND

Social interaction requires mirroring to other people's mental state. Psychotic disorders have been connected to social interaction and emotion recognition impairment. We compared the brain activity between young adults with familial risk for psychosis (FR) and matched controls during visual exposure to emotional facial expression. We also investigated the role of the amygdala, the key region for social interaction and emotion recognition.

METHODS

51 FR and 52 control subjects were drawn from the Northern Finland 1986 Birth Cohort (Oulu Brain and Mind Study). None of the included participants had developed psychosis. The FR group was defined as having a parent with psychotic disorder according to the Finnish Hospital Discharge Register. Participants underwent functional MRI (fMRI) using visual presentation of dynamic happy and fearful facial expressions. FMRI data were processed to produce maps of activation for happy and fearful facial expression, which were then compared between groups. Two spherical regions of interest (ROIs) in the amygdala were set to extract BOLD responses during happy and fearful facial expression. BOLD responses were then compared with subjects' emotion recognition, which was assessed after fMRI. Psychophysiological interaction (PPI) for the left and right amygdala during happy and fearful facial expression was conducted using the amygdala as seed regions.

RESULTS

FR subjects had increased activity in the left premotor cortex and reduced deactivation of medial prefrontal cortex structures during happy facial expression. There were no between-group differences during fearful facial expression. The FR group also showed a statistically significant linear correlation between mean amygdala BOLD response and facial expression recognition. PPI showed that there was a significant negative interaction between the amygdala and the dorsolateral prefrontal cortex (dlPFC) and superior temporal gyrus in FR subjects.

CONCLUSIONS

Increased activations by positive valence in FR were in brain regions crucial to emotion recognition and social interaction. Increased activation of the premotor cortex may serve as a compensatory mechanism as FR subjects may have to exert more effort on processing the stimuli, as has been found earlier in schizophrenia. Failure to deactivate PFC structures may imply error in the default mode network. Abnormal PFC function in FR was also suggested by PPI, as the dlPFC showed decreased functional connectivity with the amygdala in the FR group. This may indicate that in FR subjects the amygdala have to take a greater role in emotion recognition and social functioning. This inference was supported by our discovery of statistically significant correlations between the amygdala BOLD response and emotion recognition in the FR group but not in controls.

Validation of a MOSFET dosemeter system for determining the absorbed and effective radiation doses in diagnostic radiology

This study aimed to validate a MOSFET dosemeter system for determining absorbed and effective doses (EDs) in the dose and energy range used in diagnostic radiology. Energy dependence, dose linearity and repeatability of the dosemeter were examined. The absorbed doses (ADs) were compared at anterior-posterior projection and the EDs were determined at posterior-anterior, anterior-posterior and lateral projections of thoracic imaging using an anthropomorphic phantom. The radiation exposures were made using digital radiography systems. This study revealed that the MOSFET system with high sensitivity bias supply set-up is sufficiently accurate for AD and ED determination. The dosemeter is recommended to be calibrated for energies <60 and >80 kVp. The entrance skin dose level should be at least 5 mGy to minimise the deviation of the individual dosemeter dose. For ED determination, dosemeters should be implanted perpendicular to the surface of the phantom to prevent the angular dependence error.

Central executive network in young people with familial risk for psychosis--the Oulu Brain and Mind Study

OBJECTIVE

The central executive network controls and manages high-level cognitive functions. Abnormal activation in the central executive network has been related to psychosis and schizophrenia but it is not established how this applies to people with familial risk for psychosis (FR).

METHODS

We conducted a resting-state functional MRI (R-fMRI) in 72 (29 males) young adults with a history of psychosis in one or both parents (FR) but without psychosis themselves, and 72 (29 males) similarly healthy control subjects without parental psychosis. Both groups in the Oulu Brain and Mind Study were drawn from the Northern Finland Birth Cohort 1986. Participants were 20-25years old. Parental psychosis was established using the Care Register for Health Care. R-fMRI data pre-processing was conducted using independent component analysis with 30 and 70 components. A dual regression technique was used to detect between-group differences in the central executive network with p<0.05 threshold corrected for multiple comparisons.

RESULTS

FR participants demonstrated statistically significantly lower activity compared to control subjects in the right inferior frontal gyrus, a key area of central executive network corresponding to Brodmann areas 44 and 45, known as Broca's area. The volume of the lower activation area with 30 components was 896mm(3) and with 70 components was 1151mm(3).

CONCLUSION

The activity of the central executive network differed in the right inferior frontal gyrus between FR and control groups. This suggests that abnormality of the right inferior frontal gyrus may be a central part of vulnerability for psychosis.

Aberrant Functional Connectivity in the Default Mode and Central Executive Networks in Subjects with Schizophrenia - A Whole-Brain Resting-State ICA Study

Neurophysiological changes of schizophrenia are currently linked to disturbances in connectivity between functional brain networks. Functional magnetic resonance imaging studies on schizophrenia have focused on a few selected networks. Also previously, it has not been possible to discern whether the functional alterations in schizophrenia originate from spatial shifting or amplitude alterations of functional connectivity. In this study, we aim to discern the differences in schizophrenia patients with respect to spatial shifting vs. signal amplitude changes in functional connectivity in the whole-brain connectome. We used high model order-independent component analysis to study some 40 resting-state networks (RSN) covering the whole cortex. Group differences were analyzed with dual regression coupled with y-concat correction for multiple comparisons. We investigated the RSNs with and without variance normalization in order to discern spatial shifting from signal amplitude changes in 43 schizophrenia patients and matched controls from the Northern Finland 1966 Birth Cohort. Voxel-level correction for multiple comparisons revealed 18 RSNs with altered functional connectivity, 6 of which had both spatial and signal amplitude changes. After adding the multiple comparison, y-concat correction to the analysis for including the 40 RSNs as well, we found that four RSNs showed still changes. These robust changes actually seem encompass parcellations of the default mode network and central executive networks. These networks both have spatially shifted connectivity and abnormal signal amplitudes. Interestingly the networks seem to mix their functional representations in areas like left caudate nucleus and dorsolateral prefrontal cortex. These changes overlapped with areas that have been related to dopaminergic alterations in patients with schizophrenia compared to controls.

Synchronous multiscale neuroimaging environment for critically sampled physiological analysis of brain function: hepta-scan concept

Functional connectivity of the resting-state networks of the brain is thought to be mediated by very-low-frequency fluctuations (VLFFs <0.1 Hz) in neuronal activity. However, vasomotor waves and cardiorespiratory pulsations influence indirect measures of brain function, such as the functional magnetic resonance imaging blood-oxygen-level-dependent (BOLD) signal. How strongly physiological oscillations correlate with spontaneous BOLD signals is not known, partially due to differences in the data-sampling rates of different methods. Recent ultrafast inverse imaging sequences, including magnetic resonance encephalography (MREG), enable critical sampling of these signals. In this study, we describe a multimodal concept, referred to as Hepta-scan, which incorporates synchronous MREG with scalp electroencephalography, near-infrared spectroscopy, noninvasive blood pressure, and anesthesia monitoring. Our preliminary results support the idea that, in the absence of aliased cardiorespiratory signals, VLFFs in the BOLD signal are affected by vasomotor and electrophysiological sources. Further, MREG signals showed a high correlation coefficient between the ventromedial default mode network (DMNvmpf) and electrophysiological signals, especially in the VLF range. Also, oxy- and deoxyhemoglobin and vasomotor waves were found to correlate with DMNvmpf. Intriguingly, usage of shorter time windows in these correlation measurements produced significantly (p<0.05) higher positive and negative correlation coefficients, suggesting temporal nonstationary behavior between the measurements. Focus on the VLF range strongly increased correlation strength.

Longitudinal changes in total brain volume in schizophrenia: relation to symptom severity, cognition and antipsychotic medication

Studies show evidence of longitudinal brain volume decreases in schizophrenia. We studied brain volume changes and their relation to symptom severity, level of function, cognition, and antipsychotic medication in participants with schizophrenia and control participants from a general population based birth cohort sample in a relatively long follow-up period of almost a decade. All members of the Northern Finland Birth Cohort 1966 with any psychotic disorder and a random sample not having psychosis were invited for a MRI brain scan, and clinical and cognitive assessment during 1999-2001 at the age of 33-35 years. A follow-up was conducted 9 years later during 2008-2010. Brain scans at both time points were obtained from 33 participants with schizophrenia and 71 control participants. Regression models were used to examine whether brain volume changes predicted clinical and cognitive changes over time, and whether antipsychotic medication predicted brain volume changes. The mean annual whole brain volume reduction was 0.69% in schizophrenia, and 0.49% in controls (p = 0.003, adjusted for gender, educational level, alcohol use and weight gain). The brain volume reduction in schizophrenia patients was found especially in the temporal lobe and periventricular area. Symptom severity, functioning level, and decline in cognition were not associated with brain volume reduction in schizophrenia. The amount of antipsychotic medication (dose years of equivalent to 100 mg daily chlorpromazine) over the follow-up period predicted brain volume loss (p = 0.003 adjusted for symptom level, alcohol use and weight gain). In this population based sample, brain volume reduction continues in schizophrenia patients after the onset of illness, and antipsychotic medications may contribute to these reductions.

Brain structure in different psychosis risk groups in the Northern Finland 1986 birth cohort

We tested the hypothesis that family risk for psychosis (FR) and clinical risk for psychosis (CR) are associated with structural brain abnormalities, with increased deficits in those at both family risk and clinical risk for psychosis (FRCR). The study setting was the Oulu Brain and Mind Study, with subjects drawn from the Northern Finland 1986 Birth Cohort (n=9479) using register and questionnaire based screening, and interviews using the Structured Interview for Prodromal Symptoms. After this procedure, 172 subjects were included in the study, classified as controls (n=73) and three risk groups: FR excluding CR (FR, n=60), CR without FR (CR, n=26), and individuals at both FR and CR (FRCR, n=13). T1-weighted brain scans were acquired and processed in a voxel-based analysis using permutation-based statistics. In the comparison between FRCR versus controls, we found lower grey matter volume (GMV) in a cluster (1689 voxels at -4.00, -72.00, -18.00mm) covering both cerebellar hemispheres and the vermis. This cluster was subsequently used as a mask to extract mean GMV in all four groups: FR had a volume intermediate between controls and FRCR. Within FRCR there was an association between cerebellar cluster brain volume and motor function. These findings are consistent with an evolving pattern of cerebellar deficits in psychosis risk with the most pronounced deficits in those at highest risk of psychosis.

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.

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.

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.

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.

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.

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.

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.

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.

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.

Alterations in regional homogeneity of resting-state brain activity in autism spectrum disorders

Measures assessing resting-state brain activity with blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) can reveal cognitive disorders at an early stage. Analysis of regional homogeneity (ReHo) measures the local synchronization of spontaneous fMRI signals and has been successfully utilized in detecting alterations in subjects with attention-deficit hyperactivity disorder (ADHD), depression, schizophrenia, Parkinson's disease and Alzheimer's dementia. Resting-state brain activity was investigated in 28 adolescents with autism spectrum disorders (ASD) and 27 typically developing controls being imaged with BOLD fMRI and analyzed with the ReHo method. The hypothesis was that ReHo of resting-state brain activity would be different between ASD subjects and controls in brain areas previously shown to display functional alterations in stimulus or task based fMRI studies. Compared with the controls, the subjects with ASD had significantly decreased ReHo in right superior temporal sulcus region, right inferior and middle frontal gyri, bilateral cerebellar crus I, right insula and right postcentral gyrus. Significantly increased ReHo was discovered in right thalamus, left inferior frontal and anterior subcallosal gyrus and bilateral cerebellar lobule VIII. We conclude that subjects with ASD have right dominant ReHo alterations of resting-state brain activity, i.e., areas known to exhibit abnormal stimulus or task related functionality. Our results demonstrate that there is potential in utilizing the ReHo method in fMRI analyses of ASD.

Correction of low-frequency physiological noise from the resting state BOLD fMRI--Effect on ICA default mode analysis at 1.5 T

Confounding low-frequency fluctuation (LFF) physiological noise is a concern for functional connectivity analyses in blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI). Using estimates of LFF physiological noise derived from measured cardiac and respiration signals, noise can be filtered from the time series thus improving the results of functional connectivity analysis. The ability of spatial independent component analysis (ICA) to separate LFF physiological noise from the default mode network (DMN), which overlap each other spatially and occur at similar frequencies, has remained an open question. We aimed to define the net effect of physiological correction for spatial ICA DMN detection at 1.5 T by statistically testing obtained ICASSO centrotype DMN maps before and after physiological correction. Comparisons with 21 subjects were performed for ICA model orders 20, 30 and 40 and no statistically significant spatial difference was found after physiological correction, although slight DMN reduction in precuneus or sagittal sinus was detected in all dimensionalities. A confounding factor in the analysis is the susceptibility of the ICA decomposition for data changes yielding different DMN splitting between and after physiological correction conditions without comparable true change in the data. This issue is mitigated at higher ICA model orders. The results suggest that subject-level DMN can for some subjects be optimized by physiological correction, but on the group-level this contribution is minor.

Mapping Transient Hyperventilation Induced Alterations with Estimates of the Multi-Scale Dynamics of BOLD Signal

Temporal blood oxygen level dependent (BOLD) contrast signals in functional MRI during rest may be characterized by power spectral distribution (PSD) trends of the form 1/f(alpha). Trends with 1/f characteristics comprise fractal properties with repeating oscillation patterns in multiple time scales. Estimates of the fractal properties enable the quantification of phenomena that may otherwise be difficult to measure, such as transient, non-linear changes. In this study it was hypothesized that the fractal metrics of 1/f BOLD signal trends can map changes related to dynamic, multi-scale alterations in cerebral blood flow (CBF) after a transient hyperventilation challenge. Twenty-three normal adults were imaged in a resting-state before and after hyperventilation. Different variables (1/f trend constant alpha, fractal dimension D(f), and, Hurst exponent H) characterizing the trends were measured from BOLD signals. The results show that fractal metrics of the BOLD signal follow the fractional Gaussian noise model, even during the dynamic CBF change that follows hyperventilation. The most dominant effect on the fractal metrics was detected in grey matter, in line with previous hyperventilation vaso-reactivity studies. The alpha was able to differentiate also blood vessels from grey matter changes. D(f) was most sensitive to grey matter. H correlated with default mode network areas before hyperventilation but this pattern vanished after hyperventilation due to a global increase in H. In the future, resting-state fMRI combined with fractal metrics of the BOLD signal may be used for analyzing multi-scale alterations of cerebral blood flow.

Normal Auger spectra of Br in alkali bromide molecules

Molecular Auger electron spectra following the bromine 3d ionization in gas-phase alkali bromides and in HBr were studied both experimentally and theoretically. The AES for HBr and CsBr were measured using photoexcitation, and for LiBr, NaBr, and KBr by using electron impact. These results are compared with the theoretical spectra from nonrelativistic ab initio calculations and one-center approximation and with the spectra of Br(-), computed with the multiconfiguration Dirac-Fock method.