Blood Oxygen Level-dependent fMRI Signal Research Articles

Altered Resting‐state fMRI BOLD Signal Fluctuations in the Spectrum of Alzheimer’s Dementia and in Patients with White Matter Hyperintensities

AbstractBackgroundAltered fluctuations in blood oxygen level dependent (BOLD) signal during resting‐state functional magnetic resonance imaging (rs‐fMRI) have been considered indicative of decreased cerebrovascular health. Previous studies reported changes in BOLD fluctuations associated with Alzheimer’s dementia (AD) and white matter hyperintensities (WMH). However, to date, there has been no large‐scale study involving patients with AD, mild cognitive impairment (MCI), subjective cognitive decline (SCD), and control subjects (CON) that has explored local AD‐ and WMH‐related effects in BOLD fluctuations.MethodWe calculated the voxel‐based coefficient of variation (CV: standard deviation divided by the mean) and the relative CV (rCV: CV divided by the mean CV of the whole‐brain) of the rs‐fMRI BOLD signal in the large multicentric DZNE DELCODE cohort. After spatial preprocessing, regression of movement parameters, movement spikes, and scanner drift, low‐pass filtering and exclusion of subjects with high motion, CV and rCV maps were obtained and compared with respect to (A) clinical diagnosis (AD: n = 79; MCI: n = 113; SCD: n = 314; CON: n = 192) and cerebrospinal fluid Aß42/40 (AD: n = 41; MCI: n = 64; SCD: n = 139; CON: n = 77); and (B) effects of vascular‐related lesions, here WMH.ResultThe CV and rCV of the precuneus and middle cingulum indicated increasingly reduced BOLD fluctuation across the clinical groups towards AD. Interestingly, we observed increases in rCV in the basal ganglia and frontal white matter in AD compared with CON. Of note, lower CSF Aß42/40 (more pathological) was associated to reduced CV and rCV in the precuneus and with lower rCV in the right angular gyrus. Subjects with higher WMH volume showed widespread reductions of CV in white matter near the left angular gyrus and reduced rCV in large portions of white matter and subcortical gray matter.ConclusionThe AD spectrum exhibits altered BOLD fluctuations that are at least partially tied to actual amyloid differences. Larger WMH are associated with reduced BOLD signal fluctuations consistent with widespread white matter pathology ‐ beyond the effects of diagnosis. We argue that rCV is a useful complement to CV to detect these changes that might reflect cerebrovascular impairment.

Altered Resting‐state fMRI BOLD Signal Fluctuations in the Spectrum of Alzheimer’s Dementia and in Patients with White Matter Hyperintensities

AbstractBackgroundAltered fluctuations in blood oxygen level dependent (BOLD) signal during resting‐state functional magnetic resonance imaging (rs‐fMRI) have been considered indicative of decreased cerebrovascular health. Previous studies reported changes in BOLD fluctuations associated with Alzheimer’s dementia (AD) and white matter hyperintensities (WMH). However, to date, there has been no large‐scale study involving patients with AD, mild cognitive impairment (MCI), subjective cognitive decline (SCD), and control subjects (CON) that has explored local AD‐ and WMH‐related effects in BOLD fluctuations.MethodWe calculated the voxel‐based coefficient of variation (CV: standard deviation divided by the mean) and the relative CV (rCV: CV divided by the mean CV of the whole‐brain) of the rs‐fMRI BOLD signal in the large multicentric DZNE DELCODE cohort. After spatial preprocessing, regression of movement parameters, movement spikes, and scanner drift, low‐pass filtering and exclusion of subjects with high motion, CV and rCV maps were obtained and compared with respect to (A) clinical diagnosis (AD: n = 79; MCI: n = 113; SCD: n = 314; CON: n = 192) and cerebrospinal fluid Aβ42/40 (AD: n = 41; MCI: n = 64; SCD: n = 139; CON: n = 77); and (B) effects of vascular‐related lesions, here WMH.ResultThe CV and rCV of the precuneus and middle cingulum indicated increasingly reduced BOLD fluctuation across the clinical groups towards AD. Interestingly, we observed increases in rCV in the basal ganglia and frontal white matter in AD compared with CON. Of note, lower CSF Aβ42/40 (more pathological) was associated to reduced CV and rCV in the precuneus and with lower rCV in the right angular gyrus. Subjects with higher WMH volume showed widespread reductions of CV in white matter near the left angular gyrus and reduced rCV in large portions of white matter and subcortical gray matter.ConclusionThe AD spectrum exhibits altered BOLD fluctuations that are at least partially tied to actual amyloid differences. Larger WMH are associated with reduced BOLD signal fluctuations consistent with widespread white matter pathology ‐ beyond the effects of diagnosis. We argue that rCV is a useful complement to CV to detect these changes that might reflect cerebrovascular impairment.

A Novel Finding in Cerebral Blood Flow in Patients with Sickle Cell Disease Using Bold Functional MRI

Background: Sickle cell disease (SCD) is an inherited blood disorder which is associated with acute and chronic pain, as well as other vascular complications such as arterial steno-occlusion, stroke, and silent cerebral infarction. Alterations of cerebral blood flow have been found to be associated with different painful stages in SCD, thus cerebral blood flow might be correlated with the disease severity determined by varied pain conditions in SCD. Blood oxygen level dependent (BOLD) imaging is a blood-related contrast technology in functional magnetic resonance imaging (fMRI). We have previously demonstrated the feasibility of using low frequency oscillation (LFO) in BOLD fMRI to track the hemodynamic features of the blood flow with maximum cross-correlation coefficient (MCCC) and the Delay Time (i.e., blood arrival time at different brain regions) (Tong et al., JCBFM. 2017). Positive MCCC value was found between the LFO BOLD signals extracted from the global mean (GMean, averaged signal across the whole brain) and the superior sagittal sinus (SSS) in our previous findings (Tong et al., JCBFM. 2018). In this study, we aim to use LFO BOLD signals to explore the cerebral hemodynamic features with MCCCs and their associations with hematological and disease severity related outcomes in patients with SCD. Methods: Ten subjects with SCD aged 14-43 years old (5 HbSS, 3 HbSC, 1 HbSB+, 1 HbB0) and 10 age- and sex-matched controls from a publicly available dataset Human Connectome Project (HCP) (Essen et al., Neuroimage, 2013) were included for data analysis. T1-weighted and resting-state BOLD fMRI (repetition time (TR)=1.05 sec) were collected using a 3T SIEMENS (Prisma) scanner. A BOLD fMRI dataset with TR of 0.72 sec from healthy subjects was used as control. All the BOLD fMRI data were preprocessed using FSL to correct the motion artifact. The SSS region was identified in the T1-weighted image using FSL. The corresponding SSS mask was then applied in the BOLD fMRI image to extract the BOLD fMRI signals in the SSS region. The GMean BOLD signal was averaged across the whole brain. BOLD fMRI Signals were demeaned and band-pass filtered (0.01-0.1 Hz) to acquire the LFO signals. Cross-correlation between the SSS-LFO signal and GMean-LFO signal was performed to calculate the MCCC and their corresponding Delay Time. Correlations between the MCCCs of the two LFO signals (from SSS and GMean) and each hematological index were further performed. Results: Consistent with the healthy control in our previous findings, half of the patients (3 HbSC, 1 HbSB+, and 1 HbSS) presented positive MCCC values. However, interestingly, negative MCCC values were found from another half of the patients (4 HbSS and 1 HbB0, Figure 1, p < 0.05 for all subjects). Notably, all patients with HbSC presented positive MCCC values that are similar to controls whereas 4 out 5 patients with HbSS (80%) were found to have negative MCCC values. More importantly, higher percentages of HbS and lower total Hgb (HbS(%): r=-0.73, p=0.03; Hgb(GM/dL): r=0.69, p=0.03) were found to be significantly associated with the negative MCCC values. Conclusions: We found a never-before-seen negative MCCC signature between the BOLD fMRI signals in the SSS and the GMean in a subset of patients with SCD compared with healthy controls. This might indicate a significant change in cerebral blood flow in a subset of patients with SS disease compared with SC and controls. Importantly, we further observed that this never-before-seen negative MCCC signature is highly associated with the percentage of the HbS and total Hgb that are known to be correlated with disease severity. This might be due to the microvascular shunting (Juttukonda et al., JCBFM, 2019) that exists in patients who have high percentage of HbS. This study is the first to associate BOLD fMRI derived hemodynamic metrics with disease severity related outcomes in SCD. This never-before-seen anti-correlation MCCC signature may have tremendous promise as a novel biomarker for SCD severity and differentiate pain characteristics in different sickle cell phenotypes, and further investigation in larger number of subjects is needed to understand variations between SCD phenotypes and its relationship to clinical outcomes. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The many layers of BOLD. The effect of hypercapnic and hyperoxic stimuli on macro- and micro-vascular compartments quantified by CVR, M, and CBV across cortical depth

Ultra-high field functional magnetic resonance imaging (fMRI) offers the spatial resolution to measure neuronal activity at the scale of cortical layers. However, cortical depth dependent vascularization differences, such as a higher prevalence of macro-vascular compartments near the pial surface, have a confounding effect on depth-resolved blood-oxygen-level dependent (BOLD) fMRI signals. In the current study, we use hypercapnic and hyperoxic breathing conditions to quantify the influence of all venous vascular and micro-vascular compartments on laminar BOLD fMRI, as measured with gradient-echo (GE) and spin-echo (SE) scan sequences, respectively. We find that all venous vascular and micro-vascular compartments are capable of comparable theoretical maximum signal intensities, as represented by the M-value parameter. However, the capacity for vessel dilation, as reflected by the cerebrovascular reactivity (CVR), is approximately two and a half times larger for all venous vascular compartments combined compared to the micro-vasculature at superficial layers. Finally, there is roughly a 35% difference in estimates of CBV changes between all venous vascular and micro-vascular compartments, although this relative difference was approximately uniform across cortical depth. Thus, our results suggest that fMRI BOLD signal differences across cortical depth are likely caused by differences in dilation properties between macro- and micro-vascular compartments.

0739 Cerebrovascular Response to Intermittent Hypoxia During Sleep in OSA Patients

Abstract Introduction Obstructive sleep apnea (OSA) is associated with increased risks of cerebrovascular accidents, but it remains unclear how OSA impacts the cerebral vasculature. Intermittent hypoxia is a hallmark feature of OSA and recurs throughout sleep. In awake humans, the cerebrovascular response to intermittent hypoxia has been well characterized, as an increase of blood perfusion that begins at least a few seconds after the start of hypoxia. Functional magnetic resonance imaging (fMRI) that measures the blood oxygen level dependent (BOLD) signal has revealed significant differences between the cerebrovascular responses in awake humans with and without OSA. However, intermittent hypoxia occurs primarily during sleep in OSA, yet the cerebrovascular response to intermittent hypoxia has not been studied during sleep. Methods Eight adult patients with severe OSA were recruited to this study. Each subject first underwent an acclimatization session in which they tried to sleep in the MRI scanner while listening to sound recordings of the fMRI. Each acclimatized subject then underwent an overnight study in which T2*-weighed BOLD fMRI of the whole brain was conducted for 1.5-3 hours in total (TE: 35 ms, TR: 2.0 s, 35 sagittal slices, 3.5 mm isotropic). Oxygen saturation (SaO2), chest movement, end-tidal carbon dioxide and scalp encephalography (EEG) were simultaneously recorded with the fMRI. After rigid-body motion correction and removal of artifacts, the temporal correlation between BOLD signal and the SaO2 signal was analyzed on a voxel-by-voxel basis. Results Four subjects (50%) were acclimatized to sleep in the MRI scanner and completed this study. In all subjects, the BOLD fMRI signal showed an initial decrease corresponding to the decrease of SaO2, followed by a delayed increase corresponding to the hyperperfusion, throughout the gray matter of cerebral cortex. The time course of BOLD fMRI signal was significantly advanced in time, by 2-4 seconds, in the visual cortex compared to the rest of cerebral cortex in all subjects. This phenomenon was also observed in some other brain regions, but not consistently across subjects. Conclusion This study is, to our knowledge, the first study of the cerebrovascular response to intermittent hypoxia during sleep in humans. In patients with OSA, we observed spatiotemporal heterogeneity of the cerebrovascular response, such that the response in the visual cortex was significantly advanced in time than other brain regions. This phenomenon has not been reported before, and future studies are needed to understand how this heterogeneity is associated with OSA. Support (If Any) AASM Foundation

Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models.

Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography–functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O2 to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models.

Assessment of single-vessel cerebral blood velocity by phase contrast fMRI.

Current approaches to high-field functional MRI (fMRI) provide 2 means to map hemodynamics at the level of single vessels in the brain. One is through changes in deoxyhemoglobin in venules, i.e., blood oxygenation level-dependent (BOLD) fMRI, while the second is through changes in arteriole diameter, i.e., cerebral blood volume (CBV) fMRI. Here, we introduce cerebral blood flow-related velocity-based fMRI, denoted CBFv-fMRI, which uses high-resolution phase contrast (PC) MRI to form velocity measurements of flow. We use CBFv-fMRI in measure changes in blood velocity in single penetrating microvessels across rat parietal cortex. In contrast to the venule-dominated BOLD and arteriole-dominated CBV fMRI signals, CBFv-fMRI is comparable from both arterioles and venules. A single fMRI platform is used to map changes in blood pO2 (BOLD), volume (CBV), and velocity (CBFv). This combined high-resolution single-vessel fMRI mapping scheme enables vessel-specific hemodynamic mapping in animal models of normal and diseased states and further has translational potential to map vascular dementia in diseased or injured human brains with ultra-high-field fMRI.

Decoding Activity in Broca's Area Predicts the Occurrence of Auditory Hallucinations Across Subjects

BackgroundFunctional magnetic resonance imaging (fMRI) capture aims at detecting auditory-verbal hallucinations (AVHs) from continuously recorded brain activity. Establishing efficient capture methods with low computational cost that easily generalize between patients remains a key objective in precision psychiatry. To address this issue, we developed a novel automatized fMRI-capture procedure for AVHs in patients with schizophrenia (SCZ). MethodsWe used a previously validated but labor-intensive personalized fMRI-capture method to train a linear classifier using machine learning techniques. We benchmarked the performances of this classifier on 2320 AVH periods versus resting-state periods obtained from SCZ patients with frequent symptoms (n = 23). We characterized patterns of blood oxygen level–dependent activity that were predictive of AVH both within and between subjects. Generalizability was assessed with a second independent sample gathering 2000 AVH labels (n = 34 patients with SCZ), while specificity was tested with a nonclinical control sample performing an auditory imagery task (840 labels, n = 20). ResultsOur between-subject classifier achieved high decoding accuracy (area under the curve = 0.85) and discriminated AVH from rest and verbal imagery. Optimizing the parameters on the first schizophrenia dataset and testing its performance on the second dataset led to an out-of-sample area under the curve of 0.85 (0.88 for the converse test). We showed that AVH detection critically depends on local blood oxygen level–dependent activity patterns within Broca's area. ConclusionsOur results demonstrate that it is possible to reliably detect AVH states from fMRI blood oxygen level–dependent signals in patients with SCZ using a multivariate decoder without performing complex preprocessing steps. These findings constitute a crucial step toward brain-based treatments for severe drug-resistant hallucinations.

Sensory Recruitment Revisited: Ipsilateral V1 Involved in Visual Working Memory.

The "sensory recruitment hypothesis" posits an essential role of sensory cortices in working memory, beyond the well-accepted frontoparietal areas. Yet, this hypothesis has recently been challenged. In the present study, participants performed a delayed orientation recall task while high-spatial-resolution 3T functional magnetic resonance imaging (fMRI) signals were measured in posterior cortices. A multivariate inverted encoding model approach was used to decode remembered orientations based on blood oxygen level-dependent fMRI signals from visual cortices during the delay period. We found that not only did activity in the contralateral primary visual cortex (V1) retain high-fidelity representations of the visual stimuli, but activity in the ipsilateral V1 also contained such orientation tuning. Moreover, although the encoded tuning was faded in the contralateral V1 during the late delay period, tuning information in the ipsilateral V1 remained sustained. Furthermore, the ipsilateral representation was presented in secondary visual cortex (V2) as well, but not in other higher-level visual areas. These results thus supported the sensory recruitment hypothesis and extended it to the ipsilateral sensory areas, which indicated the distributed involvement of visual areas in visual working memory.

Size of the spatial correlation between ECoG and fMRI activity

Electrocorticography (ECoG) is typically employed to accurately identify the seizure focus as well as the location of brain functions to be spared during surgical resection in participants with drug-resistant epilepsy. Increasingly, this technique has become a powerful tool to map cognitive functions onto brain regions. Cortical mapping is more commonly investigated with functional MRI (fMRI), which measures blood-oxygen level dependent (BOLD) changes induced by neuronal activity. The multimodal integration between typical 3T fMRI activity maps and ECoG measurements can provide unique insight into the spatiotemporal aspects of cognition. However, the optimal integration of fMRI and ECoG requires fundamental insight into the spatial smoothness of the BOLD signal under each electrode.Here we use ECoG as ground truth for the extent of activity, as each electrode is thought to record from the cortical tissue directly underneath the contact, to estimate the spatial smoothness of the associated BOLD response at 3T fMRI. We compared the high-frequency broadband (HFB) activity recorded with ECoG while participants performed a motor task. Activity maps were obtained with fMRI at 3T for the same task in the same participant prior to surgery. We then correlated HFB power with the fMRI BOLD signal change in the area around each electrode. This latter measure was quantified by applying a 3D Gaussian kernel of varying width (sigma between 1 mm and 20 mm) to the fMRI maps including only gray-matter.We found that the correlation between HFB and BOLD activity increased sharply up to the point when the kernel width was set to 4 mm, which we defined as the kernel width of maximal spatial specificity. After this point, as the kernel width increased, the highest level of explained variance was reached at a kernel width of 9 mm for most participants. Intriguingly, maximal specificity was also limited to 4 mm for low-frequency bands, such as alpha and beta, but the kernel width with the highest explained variance was less spatially limited than the HFB.In summary, spatial specificity is limited to a kernel width of 4 mm but explained variance keeps on increasing as you average over more and more voxels containing the relatively noisy BOLD signal. Future multimodal studies should choose the kernel width based on their research goal. For maximal spatial specificity, ECoG electrodes are best compared to 3T fMRI with a kernel width of 4 mm. When optimizing the correlation between modalities, highest explained variance can be obtained at larger kernel widths of 9 mm, at the expense of spatial specificity. Finally, we release the complete pipeline so that researchers can estimate the most appropriate kernel width from their multimodal datasets.

Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network.

The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.

Autism Spectrum Disorder Detection Based on Wavelet Transform of BOLD fMRI Signals Using Pre-trained Convolution Neural Network

Autism spectrum disorder (ASD) is a mental disorder and the main problem in ASD treatment has no definite cure, and one possible option is to control its symptoms. Conventional ASD assessment using questionnaires may not be accurate and required evaluation of trained experts. Several attempts to use resting-state functional magnetic resonance imaging (fMRI) as an assisting tool combined with a classifier have been reported for ASD detection. Still, researchers barely reach an accuracy of 70% for replicated models with independent datasets. Most of the ASD studies have used functional connectivity and structural measurements and ignored the temporal dynamics features of fMRI data analysis. This study aims to present several convolutional neural networks as tools for ASD detection based on temporal dynamic features classification and improve the ASD prediction results. The sample size is 82 subjects (41 ASD and 41 normal cases) collected from three different sites of Autism Brain Imaging Data Exchange (ABIDE). The default mode network (DMN) regions are selected for blood-oxygen-level-dependent (BOLD) signals extraction. The extracted BOLD signals' time-frequency components are converted to scalogram images and used as input for pre-trained convolutional neural networks for feature extraction such as GoogLenet, DenseNet201, ResNet18, and ResNet101. The extracted features are trained using two classifiers: support vector machine (SVM) and K-nearest neighbours (KNN). The best prediction results are 85.9% accuracy achieved by extracted the features from DenseNet201 network and classified these features by KNN classifier. Comparison with previous studies, has indicated the good potential of the proposed model for diagnosis of ASD cases. From another perspective, the presented method can be applied for analysis of rs-fMRI data on other type of brain disorders.

Distinct Neurophysiological Correlates of the fMRI BOLD Signal in the Hippocampus and Neocortex.

Functional magnetic resonance imaging (fMRI) is among the foremost methods for mapping human brain function but provides only an indirect measure of underlying neural activity. Recent findings suggest that the neurophysiological correlates of the fMRI blood oxygenation level-dependent (BOLD) signal might be regionally specific. We examined the neurophysiological correlates of the fMRI BOLD signal in the hippocampus and neocortex, where differences in neural architecture might result in a different relationship between the respective signals. Fifteen human neurosurgical patients (10 female, 5 male) implanted with depth electrodes performed a verbal free recall task while electrophysiological activity was recorded simultaneously from hippocampal and neocortical sites. The same patients subsequently performed a similar version of the task during a later fMRI session. Subsequent memory effects (SMEs) were computed for both imaging modalities as patterns of encoding-related brain activity predictive of later free recall. Linear mixed-effects modeling revealed that the relationship between BOLD and gamma-band SMEs was moderated by the lobar location of the recording site. BOLD and high gamma (70-150 Hz) SMEs positively covaried across much of the neocortex. This relationship was reversed in the hippocampus, where a negative correlation between BOLD and high gamma SMEs was evident. We also observed a negative relationship between BOLD and low gamma (30-70 Hz) SMEs in the medial temporal lobe more broadly. These results suggest that the neurophysiological correlates of the BOLD signal in the hippocampus differ from those observed in the neocortex.SIGNIFICANCE STATEMENT The BOLD signal forms the basis of fMRI but provides only an indirect measure of neural activity. Task-related modulation of BOLD signals are typically equated with changes in gamma-band activity; however, relevant empirical evidence comes largely from the neocortex. We examined neurophysiological correlates of the BOLD signal in the hippocampus, where the differing neural architecture might result in a different relationship between the respective signals. We identified a positive relationship between encoding-related changes in BOLD and gamma-band activity in the frontal and parietal cortices. This effect was reversed in the hippocampus, where BOLD and gamma-band effects negatively covaried. These results suggest regional variability in the transfer function between neural activity and the BOLD signal in the hippocampus and neocortex.

Asynchrony in Peritumoral Resting-State Blood Oxygen Level-Dependent fMRI Predicts Meningioma Grade and Invasion.

Meningioma grade is determined by histologic analysis, with detectable brain invasion resulting in a diagnosis of grade II or III tumor. However, tissue undersampling is a common problem, and invasive parts of the tumor can be missed, resulting in the incorrect assignment of a lower grade. Radiographic biomarkers may be able to improve the diagnosis of grade and identify targets for biopsy. Prior work in patients with gliomas has shown that the resting-state blood oxygen level-dependent fMRI signal within these tumors is not synchronous with normal brain. We hypothesized that blood oxygen level-dependent asynchrony, a functional marker of vascular dysregulation, could predict meningioma grade. We identified 25 patients with grade I and 11 patients with grade II or III meningiomas. Blood oxygen level-dependent time-series were extracted from the tumor and the radiographically normal control hemisphere and were included as predictors in a multiple linear regression to generate a blood oxygen level-dependent asynchrony map, in which negative values signify synchronous and positive values signify asynchronous activity relative to healthy brain. Masks of blood oxygen level-dependent asynchrony were created for each patient, and the fraction of the mask that extended beyond the contrast-enhancing tumor was computed. The spatial extent of blood oxygen level-dependent asynchrony was greater in high (grades II and III) than in low (I) grade tumors (P < 0.001) and could discriminate grade with high accuracy (area under the curve = 0.88). Blood oxygen level-dependent asynchrony radiographically discriminates meningioma grade and may provide targets for biopsy collection to aid in histologic diagnosis.

Prediction of response to Certolizumab-Pegol in rheumatoid arthritis (PreCePRA) by functional MRI of the brain – Study protocol for a randomized double-blind controlled study

BackgroundTumor necrosis factor inhibitors (TNFi) signify a major advance in the treatment of rheumatoid arthritis (RA). However, treatment success initially remains uncertain as approximately half of the patients do not respond adequately to TNFi. Thus, an unmet need exists to better predict therapeutic outcome of biologicals. ObjectivesWe investigated whether brain activity associated with arthritis measured by functional magnetic resonance imaging (fMRI) of the brain can serve as a predictor of response to TNFi in RA patients. MethodsPreCePRA is a multi-center, randomized, double-blind, placebo-controlled fMRI trial on patients with RA [1] [2]. Active RA patients failing csDMARDs therapy with a DAS28 > 3.2 and at least three tender and/or swollen joints underwent a brain BOLD (blood-oxygen-level dependent) fMRI scan upon joint compression at screening. Patients were then randomized into a 12-week double-blinded treatment phase with 200 mg Certolizumab Pegol (CZP) every two weeks (arm 1: fMRI BOLD signal activated volume > 2000 voxel, i.e. 2 cm3; arm 2: fMRI BOLD signal activated volume <2000 voxel) or placebo (arm 3). DAS28 low disease activity at 12 weeks was assigned as primary endpoint. A 12-week follow-up phase in which patients were switched from the placebo to the treatment arm followed the blinded phase. fMRI was carried out at screening as well as after 12 and 24 weeks of receiving CZP or placebo. ConclusionWe hypothesize that high-level central nervous representation of pain in patients with rheumatoid arthritis predicts response to the TNFi CZP which we further investigate in the PreCePRA trial.

Rearrangement of the Activity of Neural Networks in the Human Brain on Reaching the Recognition Threshold for Fragmented Images

Common patterns of rearrangements of neural networks in the whole human brain on reaching the threshold of recognition of images in conditions of uncertainly were identifi ed. This result was obtained using a combination of digital synthesis of dynamic images and a brain activity mapping method using fMRI BOLD (blood oxygen level-dependent) signals. Reaching the threshold of recognition of images of objects was found to be associated with increases in the BOLD signal in some parts of the brain and decreases in the activity of neural networks in other areas. Brain areas whose activity was maximal at the time corresponding to the recognition threshold and task solution were described, and this activity was shown to be lesser in subthreshold and even suprathreshold conditions for observation of dynamic images. Areas in which this activity was minimal were identified. The data were interpreted in the framework of multichannel and multilevel model of systems taking decisions relating to visual signals.

Perinidal Angiogenesis Is a Predictor for Neurovascular Uncoupling in the Periphery of Brain Arteriovenous Malformations: A Task‐Based and Resting‐State fMRI Study

Potential neurovascular uncoupling (NVU) related to perinidal angiogenesis (PA) of brain arteriovenous malformations (AVMs) may cause inappropriate presurgical mapping using functional magnetic resonance imaging (fMRI), resulting in overconfident resection and postoperative morbidity. To evaluate the potential impact of PA upon fMRI blood oxygen level-dependent signal in the periphery of AVMs. Prospective. Twenty-one patients with AVMs located in the primary sensorimotor cortex (SM1) undergoing task-based fMRI (hand motor), and 19 patients with supratentorial AVMs undergoing resting-state fMRI. 3.0T, echo-planar, time-of-flight, and magnetization-prepared rapid gradient-echo. The presence of PA was determined by three observers (Y.C., J.W., and X.C.) according to digital subtraction angiography and MR angiography. Interhemispheric asymmetry of fMRI activations contralateral to hand movements was evaluated with the interhemispheric ratio of the average t-value within ipsilesional SM1 to contralesional SM1. Regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF) were extracted from ring-shaped perinidal regions and contralateral homologous regions, and the corresponding interhemispheric ratios were calculated. The effect of PA on the interhemispheric asymmetry of motor activations, ReHo, and fALFF was estimated. Pearson analysis, paired and independent t-test, multiple linear regression, Friedman test, and factorial analysis of variance were used. Motor activations were significantly reduced in ipsilesional SM1 compared to contralesional SM1 (P < 0.05). The presence of PA was the independent predictor of activation loss in ipsilateral SM1(P < 0.05). Furthermore, perinidal regions exhibited reduced ReHo compared to the homologous regions (P < 0.05). PA was significantly associated with the decline of ReHo and fALFF in perinidal regions (P < 0.05, for both). The presence of PA can predict perinidal NVU that may confound the interpretation of both task-based and resting-state fMRI, highlighting the importance of alternative approaches of brain functional localization in improving treatment of AVMs. 2 TECHNICAL EFFICACY STAGE: 2.

Separating vascular and neuronal effects of age on fMRI BOLD signals.

Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease.This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.

A glucose-stimulated BOLD fMRI study of hypothalamic dysfunction in mice fed a high-fat and high-sucrose diet

The hypothalamus is the central regulator of energy homeostasis. Hypothalamic neuronal circuits are disrupted upon overfeeding, and play a role in the development of metabolic disorders. While mouse models have been extensively employed for understanding the mechanisms of hypothalamic dysfunction, functional magnetic resonance imaging (fMRI) on hypothalamic nuclei has been challenging. We implemented a robust glucose-induced fMRI paradigm that allows to repeatedly investigate hypothalamic responses to glucose. This approach was used to test the hypothesis that hypothalamic nuclei functioning is impaired in mice exposed to a high-fat and high-sucrose diet (HFHSD) for seven days. The blood oxygen level-dependent (BOLD) fMRI signal was measured from brains of mice under light isoflurane anaesthesia, during which a 2.6 g/kg glucose load was administered. The mouse hypothalamus responded to glucose but not saline administration with a biphasic BOLD fMRI signal reduction. Relative to controls, HFHSD-fed mice showed attenuated or blunted responses in arcuate nucleus, lateral hypothalamus, ventromedial nucleus and dorsomedial nucleus, but not in paraventricular nucleus. In sum, we have developed an fMRI paradigm that is able to determine dysfunction of glucose-sensing neuronal circuits within the mouse hypothalamus in a non-invasive manner.

Brainstem Correlates of a Cold Pressor Test Measured by Ultra-High Field fMRI.

IntroductionModern imaging techniques such as blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) allow the non-invasive and indirect measurement of brain activity. Whether changes in signal intensity can be detected in small brainstem regions during a cold pressor test (CPT) has not been explored thoroughly. The aim of this study was to measure whole brain and brainstem BOLD signal intensity changes in response to a modified CPT.MethodsBOLD fMRI was measured in healthy normotensive participants during a randomized crossover study (modified CPT vs. control test) using ultra-high field 7 Tesla MRI scanner. Data were analyzed using Statistical Parametric Mapping (SPM) in a whole-brain approach, and with a brainstem-specific analysis using the spatially unbiased infra-tentorial template (SUIT) toolbox. Blood pressure (BP) and hormonal responses (norepinephrine and epinephrine levels) were also measured. Paired t-test statistics were used to compare conditions.ResultsEleven participants (six women, mean age 28 ± 8.9 years) were analyzed. Mean arterial BP increased from 83 ± 12 mm Hg to 87 ± 12 mm Hg (p = 0.0009) during the CPT. Whole-brain analysis revealed significant activations linked to the CPT in the right supplementary motor cortex, midcingulate (bilateral) and the right anterior insular cortex. The brainstem-specific analysis showed significant activations in the dorsal medulla.ConclusionChanges in BOLD fMRI signal intensity in brainstem regions during a CPT can be detected, and show an increased response during a cold stress in healthy volunteers. Consequently, BOLD fMRI at 7T is a promising tool to explore and acquire new insights in the comprehension of neurogenic hypertension.