BOLD Signal Research Articles

A preliminary study of fMRI and the relationship with depression and anxiety in Meniere's patients

PurposeTo examine alterations in Blood Oxygen Level-Dependent (BOLD) resting-state functional magnetic resonance imaging (rs-fMRI) signals, utilizing regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuation (fALFF) metrics, within activated brain regions. Additionally, this study aims to explore the relationship between these neural changes and clinical characteristics, as well as emotional states, in patients diagnosed with unilateral Meniere's disease (MD). MethodThe study included 24 patients diagnosed with left Meniere's disease (L-MD), 25 patients diagnosed with right Meniere's disease (R-MD), and 23 healthy control subjects. Resting-state blood‑oxygen-level-dependent functional magnetic resonance imaging (rest-BOLD-fMRI) data were preprocessed. A two-sample t-test was employed to compare the regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuation (fALFF) between the patient groups and the control group. Brain regions exhibiting significant differences were further analyzed for correlations with disease duration, vertigo severity, vertigo duration, hearing loss grade, and levels of anxiety and depression. ResultsIn patients with L-MD, fALFF values were significantly decreased in the right cerebellar hemisphere, middle occipital gyrus, among other regions. In patients with right-sided Ménière's disease (R-MD), fractional amplitude of low-frequency fluctuation (fALFF) values were elevated in the right middle inferior temporal gyrus and fusiform gyrus. Regional homogeneity (ReHo) values exhibited both increases and decreases in the temporal gyrus, parahippocampal gyrus, occipital gyrus, superior marginal gyrus, anterior central gyrus, and fusiform gyrus. In studies examining relational aspects, the parahippocampal gyrus, inferior temporal gyrus, middle occipital gyrus, superior middle occipital gyrus, superior marginal gyrus, and occipital gyrus demonstrated positive or negative correlations with clinical characteristics and emotional states. ConclusionsPatients with unilateral Meniere's disease (MD) exhibited both increased and decreased activation in various brain regions when compared to control subjects. A correlation was identified between these neural activation patterns and clinical characteristics, as well as emotional state, which holds significant implications for clinical treatment, prognosis, and rehabilitation strategies for MD patients.

Palmitoylethanolamide causes dose-dependent changes in brain function and the lipidome

The present studies were undertaken to understand the effects of the commonly used nutraceutical PEA on brain function and lipid chemistry. These studies using MRI and broad-scale lipidomics are without precedent in animal or human research. During the MRI scanning session awake rats were given one of three doses of PEA (3, 10, or 30 mg/kg) or vehicle and imaged for changes in BOLD signal and functional connectivity. There was an inverse dose–response for negative BOLD suggesting a decrease in brain activity affecting the prefrontal ctx, sensorimotor cortices, basal ganglia and thalamus. However, there was a dose-dependent increase in functional connectivity in these same brain areas. Plasma and CNS levels of PEA and over 80 endogenous lipids (endolipids) were determined post treatment. While levels of PEA in the CNS were significantly higher after 30 mg/kg treatment, levels of the endocannabinoid, Anandamide, and at least 20 additional endolipids, were significantly lower across the CNS. Of the 78 endolipids that were detected in all CNS regions evaluated, 51 of them were modulated in at least one of the regions. Taken together, the functional connectivity and lipidomics changes provide evidence that PEA treatment drives substantial changes in CNS activity.

Altered Blood Oxygen Level-Dependent Signal Stability in the Brain of Patients with Major Depressive Disorder Undergoing Resting-State Functional Magnetic Resonance Imaging

Introduction: Major depressive disorder (MDD) is a common, relapse-prone psychiatric disorder with unknown pathogenesis. Previous studies on resting-state functional magnetic resonance imaging of MDD have mostly focused on the spontaneous activity of blood oxygen level-dependent (BOLD) signals; however, a few studies have investigated BOLD signal stability. Methods: We conducted a resting-state functional study in 42 patients with MDD and 42 healthy controls (HC) matched for age and sex. This included the BOLD signal stability, resting-state functional connectivity (RSFC) analysis, correlation analysis, and support vector machine (SVM) analysis. Results: The BOLD signal stability of the left fusiform gyrus, right inferior temporal gyrus, right temporal pole superior temporal gyrus, and left thalamus was significantly lower in the MDD group compared to the HC group. Further RSFC analysis revealed that the connectivity between right inferior temporal gyrus and both left inferior temporal gyrus and left supramarginal gyrus was significantly reduced in the MDD group. Additionally, the RSFC levels of left thalamus and right thalamus were decreased. Combining BOLD signal stability and RSFC, the SVM-based classification model achieved an accuracy of 80.95% (sensitivity: 78.57%; specificity: 83.33%; receiver-operating characteristic area under the curve: 0.8793). Conclusion: The integration of the BOLD signal stability index and RSFC index demonstrates a robust capability to differentiate between individuals with MDD and HC subjects. We tentatively believe that a combination of the BOLD signal stability index and RSFC can be used to diagnose MDD.

Multi-parametric MRI: can we assess renal function differently?

Abstract We are lacking tools to evaluate renal performance. In this review, we presented the current knowledge and potential future applications in nephrology of new magnetic resonance imaging (MRI) techniques, focusing on Diffusion-weighted (DWI) MRI, Blood oxygen Level Dependent (BOLD) MRI, and magnetic resonance relaxometry (T1 and T2 mapping). These sequences are sensitive to early changes in biological processes such as perfusion, oxygenation, oedema or fibrosis without requiring contrast medium injection and avoids irradiation and nephrotoxicity. Combining these different sequences into the so-called ‘multiparametric MRI’ enables non-invasive, repeated exploration of renal performance on each kidney separately. DWI MRI, which evaluates the movement of water molecules, is a promising tool for non-invasive assessment of interstitial fibrosis and the cortical restricted diffusion has a prognostic value for the deterioration of renal function in diabetic nephropathy. BOLD MRI is sensitive to changes in renal tissue oxygenation based on the paramagnetic properties of deoxyhemoglobin and is of particular interest in the setting of renal artery stenosis to assess tissue oxygenation in the post-stenotic kidney. This sequence can be used for predicting degradation of renal function in CKD and might be useful in preclinical studies to assess nephroprotective and nephrotoxic effects of drugs in development. T1 and T2 relaxation times change with tissue water content and might help assessing renal fibrosis. A corticomedullary dedifferentiation in T1 has been observed in CKD and negatively correlates with glomerular filtration rate. Data on the significance of T2 values in renal imaging is more limited. Multiparametric MRI has the potential to provide a better understanding of renal physiology and pathophysiology, a better characterization of renal lesions, an earlier and more sensitive detection of renal disease, and an aid to personalized patient-centered therapeutic decision-making. Further data and clinical trials are needed to allow its routine application in clinical practice.

The intrinsic propagation directionality of fMRI infra-slow activity during visual tasks

The temporal order of propagation in the blood-oxygen-level-dependent (BOLD) infra-slow activity (ISA, 0.01–0.1 Hz) of functional magnetic resonance imaging (fMRI) can indicate the functional organization of the brain. While prior studies have revealed the temporal order of propagation of BOLD ISA during rest, how it emerges during cognitive tasks remains unclear. Furthermore, its differences between the gray and white matters at the whole-brain scale are unexplored. In this study, we probed the propagation of BOLD ISA using a publicly available fMRI dataset from participants performing visual detection and discrimination tasks (N = 46, 29 females). We examined the temporal order of propagation based on ISA oscillatory phase differences among brain parcels. During visual task performance, ISA in both the gray and white matters propagated in a direction from the visual cortex to the association cortex, including the default mode network (DMN). This result differs from the previously reported propagation direction during rest that traveled from the visual and somatosensory cortices to the DMN, suggesting that the functional organization may change when performing cognitive tasks. In addition, the propagation in the white matter represented more complex patterns than that in the gray matter, exhibiting that the cingulum preceded DMN. Our results may help the understanding of how task performance alters the sensory-DMN propagation according of ISA.

Functional localization of the human auditory and visual thalamus using a thalamic localizer functional magnetic resonance imaging task

Abstract Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due, in part, to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmarked their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

NIMG-26. GLIOBLASTOMA TISSUE INVASION MAPPING WITH NOVEL HYPOXIA-TARGETED BLOOD OXYGENATION-LEVEL DEPENDENT MRI

Abstract BACKGROUND Glioblastoma is a devastating incurable malignant primary brain tumor exhibiting an erratic growth pattern and brain tissue invasion exceeding the typically depicted contrast-enhancing lesion on MRI. This behaviour is strongly associated with the presence of aberrant vasculature. Advanced clinical neuroimaging techniques, however, have not provided convincing imaging markers to determine glioblastoma tissue infiltration and monitor treatment effects. We investigated a new imaging technique based on blood oxygenation-level dependent (BOLD) MRI contrast with precise hypoxic respiratory targeting in order to better determine tumor tissue invasion not restricted to the typically depicted focal glioblastoma lesion on contrast-enhanced MRI. METHODS A computer-controlled gas blender was used to induce transient and standardized hypoxic stimulus in isocapnic conditions during echo-planar-imaging acquisition in patients with brain tumors.8,9 Data were preprocessed using SPM and in-house written MATLAB scripts. The induced BOLD signal changes were used to calculate %BOLD signal change during hypoxic stimulus with respect to the baseline, contrast to noise (CNR) ratio and goodness-of-fit (Rsquared) for each voxel in the whole-brain and automatically segmented region of interest (contrast-enhancement, necrosis, edema) and overlayed using color-maps onto anatomical high resolution T1. RESULTS Hypoxic targeting during gas modulation could be performed with high reproducibility, caused no discomfort in patients with brain tumors and was able to induce strong signal change during hypoxic stimulus with high contrast-noise ratio and Rsquared. In particular, when compared with other tumors, BOLD hypoxia glioblastoma tissue invasion maps show very distinct hypoxic signal responses in some cases extending beyond the focal -contrast-enhancing- glioblastoma lesion. CONCLUSION The ability to map at voxel level BOLD hypoxia signal patterns may determine a novel therapeutic imaging marker based on structural vascular alterations in the tumour tissue infiltration zone before these become appreciable with standard neuroimaging. This may support a refinement and tailoring of tumor resection and improve follow-up after resection and during treatment i.e. chemoradiotherapy.

NIMG-23. A NOVEL APPROACH TO PERFUSION MRI STUDIES IN BRAIN TUMOR PATIENTS USING ENDOGENOUS DEOXYHEMOGLOBIN AS A CONTRAST AGENT: A PROOF-OF-CONCEPT STUDY

Abstract BACKGROUND Assessment of resting perfusion in brain tumors is clinically relevant and relies on dynamic susceptibility contrast magnetic resonance imaging using gadolinium contrast. Preliminary studies have introduced the use of controlled hemoglobin desaturation by means of a transient hypoxic stimulus during echo-planar imaging(EPI) as a surrogate for gadolinium for resting perfusion assessment in healthy subjects. In this proof of concept study, we investigated whether this approach is feasible in brain tumors and warrant further validation. METHODS A computer controlled gas blender was used to induce transient and standardized hypoxic stimulus in isocapnic conditions during EPI acquisition in a heterogenous cohort of tumor patients. The induced blood-oxygen-level-dependent(BOLD) signal changes determined by transit of a bolus of deoxygenated blood in the brain vasculature were used as a surrogate of gadolinium to calculate cerebral blood volume(CBV), cerebral blood flow(CBF) and mean transit time(MTT) using a global arterial input functions obtained through the BOLD signal measured over the middle cerebral artery as previously described by Poublanc et al. Perfusion parameters were calculated in the whole-brain voxel-by-voxel and in automatically segmented region of interest(ROI) using SPM, AFNI, Verbena softwares and MATLAB in-house written scripts. RESULTS Nine patients were included. MTT, rCBV, rCBF were calculated voxel-per-voxel. Obtained maps were overlayed on the co-registered T1 MRI scans and compared to T1CE and FLAIR to identify perfusion patterns in tumor ROIs (contrast enhancement, edema, necrosis). The deoxy-hemoglobin BOLD MRI perfusion were compared to clinical DSC-MRI when available and showed high qualitative agreement. CONCLUSION Transient and precise hemoglobin desaturation by controlled hypoxic gas modulation is safe and repeatable in brain tumor patients. The induced BOLD signal change allows measurement of resting perfusion, which qualitatively closely mimic gadolinium perfusion in different brain tumors. Deoxyhemoglobin-based perfusion warrant further quantitative validation against gadolinium in a larger cohort of heterogenous tumor patients.

The influence of alterations in the composition of intestinal microbiota on neurovascular coupling and cognitive dysfunction in individuals afflicted with CSVD

IntroductionAn expanding body of research has explored the crucial role of gut microbiota in cerebral small vessel disease (CSVD). The objective of this study is to investigate alterations in the gut microbiota structure among CSVD patients, to explore the correlation between differential taxonomic levels and the neurovascular coupling index as well as cognitive function and to elucidate the imaging and biomarkers of mild cognitive impairment (MCI) in CSVD. MethodsWe enrolled 104 patients with CSVD and 40 healthy controls (HC). Based on cognitive test scores, CSVD patients were categorized into a cognitively normal group (CSVD-NCI, n=61) and a mild cognitive impairment group (CSVD-MCI, n=43). Performing magnetic resonance imaging (MRI) scans, gut microbiota analysis, as well as clinical and neuropsychological assessments for all participants. Based on arterial spin labeling (ASL) and blood oxygen level-dependent (BOLD) imaging data, cerebral blood flow (CBF) and neural activity indices are computed. The coupling indices of CBF/mReHo, CBF/mfALFF, CBF/mALFF, and CBF/mDC are calculated to assess the whole-brain neurovascular coupling changes in patients with CSVD. ResultsSpecies annotation revealed differences in the composition at the phylum and genus levels among the HC, CSVD-NCI, and CSVD-MCI groups. Additionally, differential analysis using the Kruskal-Wallis test demonstrated specific dominant microbial communities in all three groups. The relative abundance of certain dominant microbial communities in CSVD patients exhibited correlations with neurovascular coupling and cognitive function. The combined assessment of Bacteroides genus and CBF/mDC proved effective in distinguishing between CSVD-NCI and CSVD-MCI, providing a novel non-invasive approach for the diagnosis of MCI in CSVD.

Efficient fully Bayesian approach to brain activity mapping with complex-valued fMRI data

Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) signal provides a more holistic approach that can increase power to detect neuronal activation. We propose a fully Bayesian model for brain activity mapping with cv-fMRI data. Our model accommodates temporal and spatial dynamics. Additionally, we propose a computationally efficient sampling algorithm, which enhances processing speed through image partitioning. Our approach is shown to be computationally efficient via image partitioning and parallel computation while being competitive with state-of-the-art methods. We support these claims with both simulated numerical studies and an application to real cv-fMRI data obtained from a finger-tapping experiment.

Sensitivity assessment of QSM+qBOLD (or QQ) in detecting elevated oxygen extraction fraction (OEF) in physiological change.

The study investigated the sensitivity of a novel MRI-based OEF mapping, quantitative susceptibility mapping plus quantitative blood oxygen level-dependent imaging (QSM+qBOLD or QQ), to physiological changes, particularly increased oxygen extraction fraction (OEF) by using hyperventilation as a vasoconstrictive stimulus. While QQ's sensitivity to decreased OEF during hypercapnia has been demonstrated, its sensitivity to increased OEF levels, crucial for cerebrovascular disorders like vascular dementia and Parkinson's disease, remains unexplored. In comparison with a previous QSM-based OEF, we evaluated QQ's sensitivity to high OEF values. MRI data were obtained from 11 healthy subjects during resting state (RS) and hyperventilation state (HV) using a 3 T MRI with a three-dimensional multi-echo gradient echo sequence (mGRE) and arterial spin labeling (ASL). Region of interest (ROI) analysis and paired t-tests were used to compare OEF, CMRO2 and CBF between QQ and QSM. Similar to QSM, QQ showed higher OEF during HV compared to RS: in cortical gray matter, QQ-OEF and QSM-OEF was 36.44.7% and 35.312.5% at RS and 45.011.6% and 45.014.8% in HV, respectively. These findings demonstrate QQ's ability to detect physiological changes and suggest its potential in studying brain metabolism in neurological disorders.

Cerebrovascular reactivity and response times describe recent ischemic symptomatology in patients with moyamoya

Abstract Moyamoya is a non-atherosclerotic intracranial steno-occlusive condition that places patients at high risk for ischemic stroke. Randomized trials of surgical revascularization demonstrating efficacy in ischemic moyamoya have not been performed, and as such, biomarkers of parenchymal hemodynamic impairment are needed to assist with triage and evaluate post-surgical response. In this prospective study, we test the hypothesis that parenchymal cerebrovascular reactivity (CVR) metrics in response to a fixed-inspired 5% carbon dioxide challenge correlate with recent focal ischemic symptoms. Hypercapnic reactivity blood oxygenation level-dependent (BOLD) MRI (echo time=35ms; spatial resolution=3.5x3.5x3.5mm) and catheter angiography assessments of cortical reserve capacity and vascular patency, respectively, in moyamoya disease and syndromic participants (n=73) were performed in sequence. Cerebrovascular reactivity uncorrected for response time (CVRRAW) was quantified, and time regression analyses were applied to quantify maximum cerebrovascular reactivity (CVRMAX) and cerebrovascular reactivity response time (CVRDELAY). Symptomatology was categorized by a stroke neurologist by hemisphere: symptomatic (lateralizing ischemic symptoms < 6 months) or asymptomatic (no ischemic symptom history). Values are presented as median [interquartile range]; logistic regression assessed the association of cerebrovascular reactivity metrics with symptoms, controlling for age and sex. A total of 109 hemispheres, including 39 symptomatic and 70 asymptomatic hemispheres, met inclusion criteria. Symptomatic hemispheres displayed reduced CVRRAW (p<0.01) (symptomatic=0.45 [0.28-0.70] z-statistic/ΔEtCO2 vs. asymptomatic=0.67 [0.44-0.98] z-statistic/ΔEtCO2), lengthened CVRDELAY (p<0.001) (symptomatic=47.6 [37.7-57.0] seconds vs. asymptomatic=37.7 [30.4-46.4] seconds), and reduced CVRMAX (p=0.037) (symptomatic=1.31 [0.99-1.94] z-statistic/ΔEtCO2 vs. asymptomatic=1.64 [1.29-2.12] z-statistic/ΔEtCO2). CVRDELAY (p<0.001) was found to be significantly related to age in asymptomatic hemispheres (0.33-unit increase/year). Of assessed measures, the receiver operating characteristic curves suggest that CVRDELAY is associated most closely with recent ischemic symptoms (p<0.001). Findings support that cerebrovascular reactivity metrics are uniquely altered in hemispheres with recent ischemic symptoms, further motivating their utilization as biomarkers of ischemic symptomatology and potential treatment efficacy in moyamoya disease and syndrome.

Neonatal neural responses to novelty related to behavioral inhibition at 1 year.

Behavioral inhibition (BI), an early-life temperament characterized by vigilant responses to novelty, is a risk factor for anxiety disorders. In this study, we investigated whether differences in neonatal brain responses to infrequent auditory stimuli relate to children's BI at 1 year of age. Using functional magnetic resonance imaging (fMRI), we collected blood-oxygen-level-dependent (BOLD) data from N = 45 full-term, sleeping neonates during an adapted auditory oddball paradigm and measured BI from n = 27 of these children 1 year later using an observational assessment. Whole-brain analyses corrected for multiple comparisons identified 46 neonatal brain regions producing novelty-evoked BOLD responses associated with children's BI scores at 1 year of age. More than half of these regions (n = 24, 52%) were in prefrontal cortex, falling primarily within regions of the default mode or frontoparietal networks or in ventromedial/orbitofrontal regions without network assignments. Hierarchical clustering of the regions based on their patterns of association with BI resulted in two groups with distinct anatomical, network, and response-timing profiles. The first group, located primarily in subcortical and temporal regions, tended to produce larger early oddball responses among infants with lower subsequent BI. The second group, located primarily in prefrontal cortex, produced larger early oddball responses among infants with higher subsequent BI. These results provide preliminary insights into brain regions engaged by novelty in infants that may relate to later BI. The findings may inform understanding of anxiety disorders and guide future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies. In this study, we aim to bridge this gap. In particular, 1) we curated a comprehensive dataset by collating images from 30 datasets, which comprises 70,781 samples of 46,686 participants. Moreover, we introduce pseudo-functional connectivity (pFC) to further generates millions of augmented brain networks by randomly dropping certain timepoints of the BOLD signal; 2) we propose the BrainMass framework for brain network self-supervised learning via mask modeling and feature alignment. BrainMass employs Mask-ROI Modeling (MRM) to bolster intra-network dependencies and regional specificity. Furthermore, Latent Representation Alignment (LRA) module is utilized to regularize augmented brain networks of the same participant with similar topological properties to yield similar latent representations by aligning their latent embeddings. Extensive experiments on eight internal tasks and seven external brain disorder diagnosis tasks show BrainMass's superior performance, highlighting its significant generalizability and adaptability. Nonetheless, BrainMass demonstrates powerful few/zero-shot learning abilities and exhibits meaningful interpretation to various diseases, showcasing its potential use for clinical applications.

Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always possible, so recent research has proposed using machine learning to directly estimate respiratory variation (RV), potentially obviating the need for external monitoring. In this study, we propose an extended method for reconstructing RV waveforms directly from resting state BOLD-fMRI data in healthy adult participants with the inclusion of both BOLD signals and derived head motion parameters. In the proposed method, 1D convolutional neural networks (1D-CNNs) used BOLD signals and head motion parameters to reconstruct the RV waveform for the whole fMRI scan time. Resting-state fMRI data and associated respiratory records from the Human Connectome Project in Young Adults (HCP-YA) dataset are used to train and test the proposed method. Compared to using only BOLD-fMRI data for a CNN input, this approach yielded improvements of 14% in mean absolute error, 24% in mean square error, 14% in correlation, and 12% in dynamic time warping. When tested on independent datasets, the method demonstrated generalizability, even in data with different TRs and physiological conditions. This study shows that the respiratory variations could be reconstructed from BOLD-fMRI data in the young adult population, and its accuracy could be improved using supportive data such as head motion parameters. The method also performed well on independent datasets with different experimental conditions.

BOLD Amplitude Correlates of Preclinical Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by a long preclinical stage during which molecular markers of amyloid beta and tau pathology rise, but there is minimal neurodegeneration or cognitive decline. Previous literature suggests that measures of brain function might be more sensitive to neuropathologic burden during the preclinical stage of AD than conventional measures of macrostructure, such as cortical thickness. However, among studies that used resting-state functional Magnetic Resonance Imaging (fMRI) acquisitions with Blood Oxygenation Level Dependent (BOLD) contrast, most employed connectivity-based analytic approaches, which discard information about the amplitude of spontaneous brain activity. Consequently, little is known about the effects of amyloid and tau pathology on BOLD amplitude. To address this knowledge gap, we characterized the effects of preclinical and prodromal AD on the amplitude of low-frequency fluctuations (ALFF) of the BOLD signal both at the whole-brain level and, at a more granular level, focused on subregions of the medial temporal lobe. We observed reduced ALFF in both preclinical and prodromal AD. In preclinical AD, amyloid positivity was associated with a spatially diffuse ALFF reduction in the frontal, medial parietal, and lateral temporal association cortices, while tau pathology was negatively associated with ALFF in the entorhinal cortex. These ALFF effects were observed in the absence of observable macrostructural changes in preclinical AD and remained after adjusting for structural atrophy in prodromal AD, indicating that ALFF offers additional sensitivity to early disease processes beyond what is provided by traditional structural imaging biomarkers of neurodegeneration. We conclude that ALFF may be a promising imaging-based biomarker for assessing the effects of amyloid-clearing immunotherapies in preclinical AD.

Pharmacological manipulation of neurotransmitter activity induces disparate effects on cerebral blood flow and resting-state fluctuations

Abstract Functional MRI methods can assess aspects of drug-induced brain response. Resting blood oxygenation level dependent (BOLD) fMRI and arterial spin labeling (ASL) perfusion MRI indirectly measure brain function through the coupling of activity to cerebral blood flow (CBF) and oxygenation but their relative sensitivity has not been directly compared. We assessed changes in resting measures of BOLD and ASL MRI in response to two neurotransmitter modulators: citalopram, a selective serotonin reuptake inhibitor, and alprazolam, a positive allosteric modulator of GABA type A receptor. Thirty healthy subjects were imaged in a placebo-controlled study, with N=20 subjects receiving each treatment as part of an incomplete block design. Time-averaged CBF images from ASL and measures of resting-state fluctuations of BOLD and ASL images were assessed for significant effects. Following acute citalopram administration, analysis of the ASL data showed a reduction in time-averaged regional CBF in regions associated with high levels of 5-HT1A receptor density. In contrast, following alprazolam administration, BOLD amplitude of low frequency fluctuations showed a highly significant and cortically widespread increase, consistent with the distribution of GABA-A receptors. Only a marginal decrease in ASL CBF was detected after alprazolam intake. BOLD and ASL are each sensitive to drugs targeting neurotransmitter systems, but appear to reflect different aspects of neural metabolism and the balance between excitatory and inhibitory activity. Accordingly, their combination may best capture the effects of neurotransmitter modulations, and thus be advantageous for pharmacological MRI studies.

A comparative analysis of the sensitivity and BOLD contamination of the VASO response at 3 Tesla: ME-DEPICTING vs. ME-EPI readouts

Abstract ‘Non-BOLD fMRI’ data acquired at non-zero echo time (TE) suffer from contamination by the Blood Oxygenation Level Dependent (BOLD) signal due to the unavoidable signal decay caused by transverse relaxation. This contamination further reduces their already low inherent functional sensitivities and makes their correction essential. The Slice-Saturation Slab-Inversion Vascular Space Occupancy (SS-SI–VASO), for instance, cancels out BOLD contributions from VASO data, reflecting cerebral blood volume (CBV) changes, via a dynamic division approach. Alternatively, multi-echo (ME) data provide the possibility of extrapolating to TE=0. Acquisitions at very short TE would minimize the need for such corrections. The center-out EPI variant (‘DEPICTING’) is one such readout which allows for short TE. The ME 2D DEPICTING was compared here against a traditional ME 2D EPI for its sensitivity to functional changes in the VASO signal. The two BOLD-correction schemes were also evaluated. Clear differences in functional sensitivity were observed for the uncorrected VASO data obtained from the first echo, TE1, of the two readouts. VASO data corrected by ME extrapolation were, however, found to be almost identical in their sensitivity for detecting CBV changes for both readouts. An excessively high increase in VASO signal sensitivity observed with the dynamic division correction for both readouts revealed a near-perfect linear dependence on TE of VASO signal changes. This could be attributed to the substantial intravascular BOLD contributions at 3 T. In the present data, extravascular ΔR2* fraction was found to be around ~50–60%. ME extrapolation is, hence, recommended to avoid overestimation of functional CBV changes at commonly used TEs.

Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM.

Age-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a sparse dynamic causal model (sDCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. sDCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range (31 females), providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model predicting the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects (173 females) selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable.Significance statement By inferring region-wise hemodynamic profiles at the individual level, this is the first study providing an exhaustive whole-brain characterization of the hemodynamic sensitivity to healthy aging, reporting further evidence of the vascular changes across the adult lifespan. Using a predictive framework, we analysed the statistical influence of advancing age on individual regional hemodynamic attributes, offering a quantitative evaluation of the diverse hemodynamic bias across different brain regions. We then unveiled a specific set of hemodynamic predictors to discriminate young from elderly people, mainly describing vascular properties of right-hemispheric areas. This suggests the asymmetric nature of vascular degeneration processes affecting the human brain at the latest stage of life, other than a potential biomarker that could be relevant for brain-age prediction.

Online self-evaluation of fMRI-based neurofeedback performance.

This study explores the subjective evaluation of supplementary motor area (SMA) regulation performance in a real-time functional magnetic resonance imaging neurofeedback (fMRI-NF) task. In fMRI-NF, people learn how to self-regulate their brain activity by performing mental actions to achieve a certain target level (TL) of blood-oxygen-level-dependent (BOLD) activation. Here, we studied two types of self-evaluation: performance predictions and perceived confidence in the prediction judgement. Participants completed three sessions of SMA regulation in a 7 T fMRI scanner, performing a mental drawing task. During each trial, they modulated their imagery strategy to achieve one of two different levels of SMA activation and reported a performance prediction and their confidence in the prediction before receiving delayed BOLD-activation feedback. Results show that participants' performance predictions improved with learning throughout the three sessions, and that these improvements were not driven exclusively by their knowledge of previous performance. Confidence reports on the other hand showed no change throughout training and did not correlate with better and worse predictions. In addition to shedding light on mechanisms of internal self-evaluation during neurofeedback training, these results also point to a dissociation between predictions of performance and confidence reports in the presence of feedback. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.