How do tasks impact the reliability of fMRI functional connectivity?

While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.

High-temporal resolution functional PET/MRI reveals coupling between human metabolic and hemodynamic brain response.

PURPOSE: Positron emission tomography (PET) provides precise molecular information on physiological processes, but its low temporal resolution is a major obstacle. Consequently, we characterized the metabolic response of the human brain to working memory performance using an optimized functional PET (fPET) framework at a temporal resolution of 3 s. METHODS: Thirty-five healthy volunteers underwent fPET with [18F]FDG bolus plus constant infusion, 19 of those at a hybrid PET/MRI scanner. During the scan, an n-back working memory paradigm was completed. fPET data were reconstructed to 3 s temporal resolution and processed with a novel sliding window filter to increase signal to noise ratio. BOLD fMRI signals were acquired at 2 s. RESULTS: Consistent with simulated kinetic modeling, we observed a constant increase in the [18F]FDG signal during task execution, followed by a rapid return to baseline after stimulation ceased. These task-specific changes were robustly observed in brain regions involved in working memory processing. The simultaneous acquisition of BOLD fMRI revealed that the temporal coupling between hemodynamic and metabolic signals in the primary motor cortex was related to individual behavioral performance during working memory. Furthermore, task-induced BOLD deactivations in the posteromedial default mode network were accompanied by distinct temporal patterns in glucose metabolism, which were dependent on the metabolic demands of the corresponding task-positive networks. CONCLUSIONS: In sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing. This allows to capture unique information in the temporal domain, which is not accessible to conventional PET imaging.

Alteration in early resting­state functional MRI activity in comatose survivors of cardiac arrest: a prospective cohort study.

BACKGROUND: This study aimed to explore the characteristics of abnormal regional resting-state functional magnetic resonance imaging (rs-fMRI) activity in comatose patients in the early period after cardiac arrest (CA), and to investigate their relationships with neurological outcomes. We also explored the correlations between jugular venous oxygen saturation (SjvO2) and rs-fMRI activity in resuscitated comatose patients. We also examined the relationship between the amplitude of the N20-baseline and the rs-fMRI activity within the intracranial conduction pathway of somatosensory evoked potentials (SSEPs). METHODS: Between January 2021 and January 2024, eligible post-resuscitated patients were screened to undergo fMRI examination. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) of rs-fMRI blood oxygenation level-dependent (BOLD) signals were used to characterize regional neural activity. Neurological outcomes were evaluated using the Glasgow-Pittsburgh cerebral performance category (CPC) scale at 3 months after CA. RESULTS: In total, 20 healthy controls and 31 post-resuscitated patients were enrolled in this study. The rs-fMRI activity of resuscitated patients revealed complex changes, characterized by increased activity in some local brain regions and reduced activity in others compared to healthy controls (P < 0.05). However, the mean ALFF values of the whole brain were significantly greater in CA patients (P = 0.011). Among the clusters of abnormal rs-fMRI activity, the cluster values of ALFF in the left middle temporal gyrus and inferior temporal gyrus and the cluster values of ReHo in the right precentral gyrus, superior frontal gyrus and middle frontal gyrus were strongly correlated with the CPC score (P < 0.001). There was a strong correlation between the mean ALFF and SjvO2 in CA patients (r = 0.910, P < 0.001). The SSEP N20-baseline amplitudes in CA patients were negatively correlated with thalamic rs-fMRI activity (all P < 0.001). CONCLUSIONS: This study revealed that abnormal rs-fMRI BOLD signals in resuscitated patients showed complex changes, characterized by increased activity in some local brain regions and reduced activity in others. Abnormal BOLD signals were associated with neurological outcomes in resuscitated patients. The mean ALFF values of the whole brain were closely related to SjvO2 levels, and changes in the thalamic BOLD signals correlated with the N20-baseline amplitudes of SSEP responses. TRIAL REGISTRATION: NCT05966389 (Registered July 27, 2023).

Imbalanced temporal states of cortical blood-oxygen-level-dependent signal variability during rest in episodic migraine.

BACKGROUND: Migraine has been associated with functional brain changes including altered connectivity and activity both during and between headache attacks. Recent studies established that the variability of the blood-oxygen-level-dependent (BOLD) signal is an important attribute of brain activity, which has so far been understudied in migraine. In this study, we investigate how time-varying measures of BOLD variability change interictally in episodic migraine patients. METHODS: Two independent resting state functional MRI datasets acquired on 3T (discovery cohort) and 1.5T MRI scanners (replication cohort) including 99 episodic migraine patients (n3T = 42, n1.5T=57) and 78 healthy controls (n3T = 46, n1.5T=32) were analyzed in this cross-sectional study. A framework using time-varying measures of BOLD variability was applied to derive BOLD variability states. Descriptors of BOLD variability states such as dwell time and fractional occupancy were calculated, then compared between migraine patients and healthy controls using Mann-Whitney U-tests. Spearman's rank correlation was calculated to test associations with clinical parameters. RESULTS: Resting-state activity was characterized by states of high and low BOLD signal variability. Migraine patients in the discovery cohort spent more time in the low variability state (mean dwell time: p = 0.014, median dwell time: p = 0.022, maximum dwell time: p = 0.013, fractional occupancy: p = 0.013) and less time in the high variability state (mean dwell time: p = 0.021, median dwell time: p = 0.021, maximum dwell time: p = 0.025, fractional occupancy: p = 0.013). Higher uptime of the low variability state was associated with greater disability as measured by MIDAS scores (maximum dwell time: R = 0.45, p = 0.007; fractional occupancy: R = 0.36, p = 0.035). Similar results were observed in the replication cohort. CONCLUSION: Episodic migraine patients spend more time in a state of low BOLD variability during rest in headache-free periods, which is associated with greater disability. BOLD variability states show potential as a replicable functional imaging marker in episodic migraine.

Functional imaging and neurochemistry identify in vivo neuroprotection mechanisms counteracting excitotoxicity and neurovascular changes in the hippocampus and visual cortex of obese and type 2 diabetic animal models.

Functional MRI (fMRI) with 1 H-MRS was combined on the hippocampus and visual cortex of animal models of obesity (high-fat diet, HFD) and type 2 diabetes (T2D) to identify the involved mechanisms and temporal evolution of neurometabolic changes in these disorders that could serve as potentially reliable clinical biomarkers. HFD rats presented elevated levels of N-acetylaspartylglutamate (NAAG) (p = 0.0365 vs. standard diet, SD) and glutathione (GSH) (p = 0.0494 vs. SD) in the hippocampus. NAAG and GSH levels in this structure proved to be correlated (r = 0.4652, p = 0.0336). This mechanism was not observed in diabetic rats. Combining MRS and fMRI-evaluated blood-oxygen-level-dependent (BOLD) response, elevated taurine (p = 0.0326 vs. HFD) and GABA type A receptor (GABAA R) (p = 0.0211 vs. SD and p = 0.0153 vs. HFD) were observed in the visual cortex of only diabetic rats, counteracting the elevated BOLD response and suggesting an adaptative mechanism against hyperexcitability observed in the primary visual cortex (V1) (p = 0.0226 vs. SD). BOLD amplitude was correlated with the glutamate levels (r = 0.4491; p = 0.0316). Therefore, here we found evidence for several biological dichotomies regarding excitotoxicity and neuroprotection in different brain regions, identifying putative markers of their different susceptibility and response to the metabolic and vascular insults of obesity and diabetes.

From local properties to brain-wide organization: A review of intraregional temporal features in functional magnetic resonance imaging data.

Based on the fluctuations ensembled over neighbouring neurons, blood oxygen level-dependent (BOLD) signal is a mesoscale measurement of brain signals. Intraregional temporal features (IRTFs) of BOLD signal, extracted from regional neural activities, are utilized to investigate how the brain functions in local brain areas. This literature highlights four types of IRTFs and their representative calculations including variability in the temporal domain, variability in the frequency domain, entropy, and intrinsic neural timescales, which are tightly related to cognitions. In the brain-wide spatial organization, these brain features generally organized into two spatial hierarchies, reflecting structural constraints of regional dynamics and hierarchical functional processing workflow in brain. Meanwhile, the spatial organization gives rise to the link between neuronal properties and cognitive performance. Disrupted or unbalanced spatial conditions of IRTFs emerge with suboptimal cognitive states, which improved our understanding of the aging process and/or neuropathology of brain disease. This review concludes that IRTFs are important properties of the brain functional system and IRTFs should be considered in a brain-wide manner.

HALO: A software tool for real-time head alignment in the MR scanner.

PURPOSE: MRI studies in human subjects often require multiple scanning sessions/visits. Changes in a subject's head position across sessions result in different alignment between brain tissues and the magnetic field which leads to changes in magnetic susceptibility. These changes can have considerable impacts on acquired signals. Head ALignment Optimization (HALO), a software tool was developed by the authors for active head alignment between sessions. METHODS: HALO provides real-time visual feedback of a subject's current head position relative to the position in a previous session. The tool was evaluated in a pilot sample of seven healthy human subjects. RESULTS: HALO was shown to enable subjects to actively align their head positions to the desired position of their initial sessions. The subjects were able to improve their head alignment significantly using HALO and achieved good alignment with their first session meeting stringent criteria similar to that used for within-run head motion (less than 2 mm translation or 2 degrees rotation in any direction from the desired position). Moreover, we found a negative correlation between the post-alignment rotation and similarity in inter-session BOLD patterns around the air-tissue interface near sinus which further highlighted the impact of tissue-field alignment on BOLD data quality. CONCLUSION: Utilization of HALO in longitudinal studies may help to improve data quality by ensuring the consistency of susceptibility gradients in brain tissues across sessions. HALO has been made publicly available.

A mechanistic computational framework to investigate the hemodynamic fingerprint of the blood oxygenation level-dependent signal.

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is one of the most used imaging techniques to map brain activity or to obtain clinical information about human cortical vasculature, in both healthy and disease conditions. Nevertheless, BOLD fMRI is an indirect measurement of brain functioning triggered by neurovascular coupling. The origin of the BOLD signal is quite complex, and the signal formation thus depends, among other factors, on the topology of the cortical vasculature and the associated hemodynamic changes. To understand the hemodynamic evolution of the BOLD signal response in humans, it is beneficial to have a computational framework available that virtually resembles the human cortical vasculature, and simulates hemodynamic changes and corresponding MRI signal changes via interactions of intrinsic biophysical and magnetic properties of the tissues. To this end, we have developed a mechanistic computational framework that simulates the hemodynamic fingerprint of the BOLD signal based on a statistically defined, three-dimensional, vascular model that approaches the human cortical vascular architecture. The microvasculature is approximated through a Voronoi tessellation method and the macrovasculature is adapted from two-photon microscopy mice data. Using this computational framework, we simulated hemodynamic changes-cerebral blood flow, cerebral blood volume, and blood oxygen saturation-induced by virtual arterial dilation. Then we computed local magnetic field disturbances generated by the vascular topology and the corresponding blood oxygen saturation changes. This mechanistic computational framework also considers the intrinsic biophysical and magnetic properties of nearby tissue, such as water diffusion and relaxation properties, resulting in a dynamic BOLD signal response. The proposed mechanistic computational framework provides an integrated biophysical model that can offer better insights regarding the spatial and temporal properties of the BOLD signal changes.

Within-Individual BOLD Signal Variability and its Implications for Task-Based Cognition: A Systematic Review.

Within-individual blood oxygen level-dependent (BOLD) signal variability, intrinsic moment-to-moment signal fluctuations within a single individual in specific voxels across a given time course, is a relatively new metric recognized in the neuroimaging literature. Within-individual BOLD signal variability has been postulated to provide information beyond that provided by mean-based analysis. Synthesis of the literature using within-individual BOLD signal variability methodology to examine various cognitive domains is needed to understand how intrinsic signal fluctuations contribute to optimal performance. This systematic review summarizes and integrates this literature to assess task-based cognitive performance in healthy groups and few clinical groups. Included papers were published through October 17, 2022. Searches were conducted on PubMed and APA PsycInfo. Studies eligible for inclusion used within-individual BOLD signal variability methodology to examine BOLD signal fluctuations during task-based functional magnetic resonance imaging (fMRI) and/or examined relationships between task-based BOLD signal variability and out-of-scanner behavioral measure performance, were in English, and were empirical research studies. Data from each of the included 19 studies were extracted and study quality was systematically assessed. Results suggest that variability patterns for different cognitive domains across the lifespan (ages 7-85) may depend on task demands, measures, variability quantification method used, and age. As neuroimaging methods explore individual-level contributions to cognition, within-individual BOLD signal variability may be a meaningful metric that can inform understanding of neurocognitive performance. Further research in understudied domains/populations, and with consistent quantification methods/cognitive measures, will help conceptualize how intrinsic BOLD variability impacts cognitive abilities in healthy and clinical groups.

The initial decrease in 7T-BOLD signals detected by hyperalignment contains information to decode facial expressions.

The initial decrease in the blood oxygenation level-dependent (BOLD) signal reflects primary neuronal activity more than the later hemodynamic positive peak responses. Moreover, ultra-high field BOLD has high sensitivity for the initial de-oxygenation signal. However, it is not fully understood how much information about task events and cognitive processes the initial decrease in the BOLD signal contains. Multivoxel pattern analysis (MVPA) of the BOLD signal has enabled the quantification of information contained in the activity patterns, but it has mainly relied on the positive peak responses. Here, we applied a signal-based functional inter-individual alignment algorithm (i.e., hyper-alignment) to a 7T-BOLD timeseries scanned while participants conducted a facial expression discrimination task. We found that the MVPA decoding accuracy in the bilateral amygdala 2 s after the face onset was significantly beyond chance. Furthermore, we confirmed that the voxels contributing to the decoding accuracy at 2 s displayed a decreasing hemodynamics response. These results demonstrated that the initial decrease in 7T-BOLD signals contains finer information about task events and cognitive processes than thought previously.

Resting-state BOLD functional connectivity depends on the heterogeneity of capillary transit times in the human brain A combined lesion and simulation study about the influence of blood flow response timing.

Functional connectivity (FC) derived from blood oxygenation level dependent (BOLD) functional magnetic resonance imaging at rest (rs-fMRI), is commonly interpreted as indicator of neuronal connectivity. In a number of brain disorders, however, metabolic, vascular, and hemodynamic impairments can be expected to alter BOLD-FC independently from neuronal activity. By means of a neurovascular coupling (NVC) model of BOLD-FC, we recently demonstrated that aberrant timing of cerebral blood flow (CBF) responses may influence BOLD-FC. In the current work, we support and extend this finding by empirically linking BOLD-FC with capillary transit time heterogeneity (CTH), which we consider as an indicator of delayed and broadened CBF responses. We assessed 28 asymptomatic patients with unilateral high-grade internal carotid artery stenosis (ICAS) as a hemodynamic lesion model with largely preserved neurocognitive functioning and 27 age-matched healthy controls. For each participant, we obtained rs-fMRI, arterial spin labeling, and dynamic susceptibility contrast MRI to study the dependence of left-right homotopic BOLD-FC on local perfusion parameters. Additionally, we investigated the dependency of BOLD-FC on CBF response timing by detailed simulations. Homotopic BOLD-FC was negatively associated with increasing CTH differences between homotopic brain areas. This relation was more pronounced in asymptomatic ICAS patients even after controlling for baseline CBF and relative cerebral blood volume influences. These findings match simulation results that predict an influence of delayed and broadened CBF responses on BOLD-FC. Results demonstrate that increasing CTH differences between homotopic brain areas lead to BOLD-FC reductions. Simulations suggest that CTH increases correspond to broadened and delayed CBF responses to fluctuations in ongoing neuronal activity.

Associations between Maternal Risk Factors and Intrinsic Placental and Fetal Brain Functional Properties in Congenital Heart Disease.

The relationship between maternal risk factors (MRFs) (particularly pre-gravid obesity, diabetes, and hypertension) and congenital heart disease (CHD) to placental and fetal brain outcomes is poorly understood. Here, we tested the hypothesis that MRF and CHD would be associated with reduced intrinsic placental and fetal brain function using a novel non-invasive technique. Pregnant participants with and without MRF and fetal CHD were prospectively recruited and underwent feto-placental MRI. Using intrinsic properties of blood oxygen level dependent imaging (BOLD) we quantified spatiotemporal variance of placenta and fetal brain. MRFs and CHD were correlated with functional characteristics of the placenta and fetal brain. Co-morbid MRF (hypertension, diabetes, and obesity) reduced spatiotemporal functional variance of placenta and fetal brain (p < 0.05). CHD predicted reduced fetal brain temporal variance compared to non-CHD (p < 0.05). The presence of both MRF and CHD was associated with reduced intrinsic pBOLD temporal variance (p = 0.047). There were no significant interactions of MRFs and CHD status on either temporal or spatial variance of intrinsic brain BOLD. MRF and CHD reduced functional characteristic of placenta and brain in fetuses. MRF modification and management during pregnancy may have the potential to not only provide additional risk stratification but may also improve neurodevelopmental outcomes.

Multivariate semi-blind deconvolution of fMRI time series.

Whole brain estimation of the haemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to get insight on the global status of the neurovascular coupling of an individual in healthy or pathological condition. Most of existing approaches in the literature works on task-fMRI data and relies on the experimental paradigm as a surrogate of neural activity, hence remaining inoperative on resting-stage fMRI (rs-fMRI) data. To cope with this issue, recent works have performed either a two-step analysis to detect large neural events and then characterize the HRF shape or a joint estimation of both the neural and haemodynamic components in an univariate fashion. In this work, we express the neural activity signals as a combination of piece-wise constant temporal atoms associated with sparse spatial maps and introduce an haemodynamic parcellation of the brain featuring a temporally dilated version of a given HRF model in each parcel with unknown dilation parameters. We formulate the joint estimation of the HRF shapes and spatio-temporal neural representations as a multivariate semi-blind deconvolution problem in a paradigm-free setting and introduce constraints inspired from the dictionary learning literature to ease its identifiability. A fast alternating minimization algorithm, along with its efficient implementation, is proposed and validated on both synthetic and real rs-fMRI data at the subject level. To demonstrate its significance at the population level, we apply this new framework to the UK Biobank data set, first for the discrimination of haemodynamic territories between balanced groups (n=24 individuals in each) patients with an history of stroke and healthy controls and second, for the analysis of normal aging on the neurovascular coupling. Overall, we statistically demonstrate that a pathology like stroke or a condition like normal brain aging induce longer haemodynamic delays in certain brain areas (e.g. Willis polygon, occipital, temporal and frontal cortices) and that this haemodynamic feature may be predictive with an accuracy of 74 % of the individual's age in a supervised classification task performed on n=459 subjects.

The longitudinal relationship between BOLD signal variability changes and white matter maturation during early childhood.

Intra-individual transient temporal fluctuations in brain signal, as measured by fMRI blood oxygenation level dependent (BOLD) variability, is increasingly considered an important signal rather than measurement noise. Evidence from computational and cognitive neuroscience suggests that signal variability is a good proxy-measure of brain functional integrity and information processing capacity. Here, we sought to explore across-participant and longitudinal relationships between BOLD variability, age, and white matter structure in early childhood. We measured standard deviation of BOLD signal, total white matter volume, global fractional anisotropy (FA) and mean diffusivity (MD) during passive movie viewing in a sample of healthy children (aged 2-8 years; N = 83). We investigated how age and white matter development related to changes in BOLD variability both across- and within-participants. Our across-participant analyses using behavioural partial least squares (bPLS) revealed that the influence of age and white matter maturation on BOLD variability was highly interrelated. BOLD variability increased in widespread frontal, temporal and parietal regions, and decreased in the hippocampus and parahippocampal gyrus with age and white matter development. Our longitudinal analyses using linear mixed effects modelling revealed significant associations between BOLD variability, age and white matter microstructure. Analyses using artificial neural networks demonstrated that BOLD variability and white matter micro and macro-structure at earlier ages were strong predictors of BOLD variability at later ages. By characterizing the across-participant and longitudinal features of the association between BOLD variability and white matter micro- and macrostructure in early childhood, our results provide a novel perspective to understand structure-function relationships in the developing brain.

The coupling of BOLD signal variability and degree centrality underlies cognitive functions and psychiatric diseases.

Brain signal variability has been consistently linked to functional integration; however, whether this coupling is associated with cognitive functions and/or psychiatric diseases has not been clarified. Using multiple multimodality datasets, including resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP: N = 927) and a Beijing sample (N = 416) and cerebral blood flow (CBF) and rsfMRI data from a Hangzhou sample (N = 29), we found that, compared with the existing variability measure (i.e., SDBOLD), the mean-scaled (standardized) fractional standard deviation of the BOLD signal (mfSDBOLD) maintained very high test-retest reliability, showed greater cross-site reliability and was less affected by head motion. We also found strong reproducible couplings between the mfSDBOLD and functional integration measured by the degree centrality (DC), both cross-voxel and cross-subject, which were robust to scanning and preprocessing parameters. Moreover, both mfSDBOLD and DC were correlated with CBF, suggesting a common physiological basis for both measures. Critically, the degree of coupling between mfSDBOLD and long-range DC was positively correlated with individuals' cognitive total composite scores. Brain regions with greater mismatches between mfSDBOLD and long-range DC were more vulnerable to brain diseases. Our results suggest that BOLD signal variability could serve as a meaningful index of local function that underlies functional integration in the human brain and that a strong coupling between BOLD signal variability and functional integration may serve as a hallmark of balanced brain networks that are associated with optimal brain functions.

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.

Comparison of Neurochemical and BOLD Signal Contrast Response Functions in the Human Visual Cortex.

We investigated the relationship between neurochemical and hemodynamic responses as a function of image contrast in the human primary visual cortex (V1). Simultaneously acquired BOLD-fMRI and single voxel proton MR spectroscopy signals were measured in V1 of 24 healthy human participants of either sex at 7 tesla field strength, in response to presentations (64 s blocks) of different levels of image contrast (3%, 12.5%, 50%, 100%). Our results suggest that complementary measures of neurotransmission and energy metabolism are in partial agreement: BOLD and glutamate signals were linear with image contrast; however, a significant increase in glutamate concentration was evident only at the highest intensity level. In contrast, GABA signals were steady across all intensity levels. These results suggest that neurochemical concentrations are maintained at lower ranges of contrast levels, which match the statistics of natural vision, and that high stimulus intensity may be critical to increase sensitivity to visually modulated glutamate signals in the early visual cortex using MR spectroscopy.SIGNIFICANCE STATEMENT Glutamate and GABA are the major excitatory and inhibitory neurotransmitters of the brain. To better understand the relationship between MRS-visible neurochemicals, the BOLD signal change, and stimulus intensity, we measured combined neurochemical and BOLD signals (combined fMRI-MRS) to different image contrasts in human V1 at 7 tesla. While a linear change to contrast was present for both signals, the increase in glutamate was significant only at the highest stimulus intensity. These results suggest that hemodynamic and neurochemical signals reflect common metabolic markers of neural activity, whereas the mismatch at lower contrast levels may indicate a sensitivity threshold for detecting neurochemical changes during visual processing. Our results highlight the challenge and importance of reconciling cellular and metabolic measures of neural activity in the human brain.

Hybrid modeling of alpha rhythm and the amplitude of low-frequency fluctuations abnormalities in the thalamocortical region and basal ganglia in Alzheimer's disease.

A hybrid computational model of thalamocortical circuitry and basal ganglia is proposed to investigate the relation between the fractional amplitude of low-frequency fluctuations (fALFF) in the resting-state functional magnetic resonance imaging (rs-fMRI) in the striatum and electroencephalogram (EEG) changes within the alpha frequency bands in thalamic region in the case of Alzheimer's disease (AD). For that purpose, an Izikhevich neuron model-based network of the basal ganglia region is constructed and connected with the thalamic region which is modeled as neural mass. By considering the neurodegenerative changes in AD, the network dynamics are analyzed. The relation between the neural activity of basal ganglia and AD is investigated by modeling the blood oxygenation level-dependent (BOLD) signal. Decrease in fALFF of slow-4 band in the simulated BOLD signal of the striatum is observed. As the thalamic region receives inhibitory connections from basal ganglia over globus pallidus internal segment (GPi), the parameter changes emulating AD degenerations in the striatum increased the inhibitory effect on the thalamic network, and as a result, slowing in alpha rhythms is observed. It is observed that the decrease in the synaptic strength between the neurons in the striatum has a dominant effect on the slowing in alpha rhythm and also causes a decrease in fALFF of slow-4 band in striatum. This demonstrates a close and causal relation between the decrease in fALFF in the striatum and the slowing in alpha rhythms in the thalamic region in AD.

The association between resting-state functional magnetic resonance imaging and aortic pulse-wave velocity in healthy adults.

Resting-state functional magnetic resonance imaging (rs-fMRI) is frequently used to study brain function; but, it is unclear whether BOLD-signal fluctuation amplitude and functional connectivity are associated with vascular factors, and how vascular-health factors are reflected in rs-fMRI metrics in the healthy population. As arterial stiffening is a known age-related cardiovascular risk factor, we investigated the associations between aortic stiffening (as measured using pulse-wave velocity [PWV]) and rs-fMRI metrics. We used cardiac MRI to measure aortic PWV (an established indicator of whole-body vascular stiffness), as well as dual-echo pseudo-continuous arterial-spin labeling to measure BOLD and CBF dynamics simultaneously in a group of generally healthy adults. We found that: (1) higher aortic PWV is associated with lower variance in the resting-state BOLD signal; (2) higher PWV is also associated with lower BOLD-based resting-state functional connectivity; (3) regions showing lower connectivity do not fully overlap with those showing lower BOLD variance with higher PWV; (4) CBF signal variance is a significant mediator of the above findings, only when averaged across regions-of-interest. Furthermore, we found no significant association between BOLD signal variance and systolic blood pressure, which is also a known predictor of vascular stiffness. Age-related vascular stiffness, as measured by PWV, provides a unique scenario to demonstrate the extent of vascular bias in rs-fMRI signal fluctuations and functional connectivity. These findings suggest that a substantial portion of age-related rs-fMRI differences may be driven by vascular effects rather than directly by brain function.

Dynamic B0 shimming of the motor cortex and cerebellum with a multicoil shim setup for BOLD fMRI at 9.4T.

PURPOSE: We assessed how improved static magnetic field (B0 ) homogeneity with a dynamic multicoil shimming can influence the blood oxygen level dependent (BOLD) contrast to noise when echo planar imaging (EPI) sequence is used for a motor task functional MRI study. We showed that a multicoil shim setup can be a proper choice for dynamic shimming of 2 spatially distant areas with different inhomogeneity distributions. METHODS: A 16-channel multicoil shim setup is used to provide improved B0 homogeneity by dynamic slice-wise shimming. The performance of dynamic B0 shimming was investigated in 2 distinct brain regions, the motor cortex and the cerebellum, in the same experiment during a finger-tapping task. Temporal SNR (tSNR), geometric distortion of the EPIs, and results of an analysis with a general linear model before and after shimming with the multicoil were compared. RESULTS: Reduced B0 deviation by 30% and 52% in the cerebellum and motor cortex, respectively, resulted in higher tSNR and a reduction of distortions in the EPI. Statistical analysis applied to the EPIs showed higher t values and increased number of voxels above significance threshold when shimming with the multicoil setup. CONCLUSIONS: Improved B0 homogeneity leads to higher tSNR and enhances the detection of BOLD signal.