Sampling Rate Effects on Resting State fMRI Metrics

Multi-Metric Approach for the Comparison of Denoising Techniques for Resting-State fMRI

Despite the increasing use of resting-state functional magnetic resonance imaging (rs-fMRI) data for studying the spontaneous functional interactions within the brain, the achievement of robust results is often hampered by insufficient data quality and by poor knowledge of the most effective denoising methods. The present study aims to define an appropriate denoising strategy for rs-fMRI data by proposing a robust framework for the quantitative and comprehensive comparison of the performance of multiple pipelines made available by the newly proposed HALFpipe software. This will ultimately contribute to standardizing rs-fMRI preprocessing and denoising steps. Fifty-three participants took part in the study by undergoing a rs-fMRI session. Synthetic rs-fMRI data from one subject were also generated. Nine different denoising pipelines were applied in parallel to the minimally preprocessed fMRI data. The comparison was conducted by computing previously proposed and novel metrics that quantify the degree of artifact removal, signal enhancement, and resting-state network identifiability. A summary performance index, accounting for both noise removal and information preservation, was proposed. The results confirm the performance heterogeneity of different denoising pipelines across the different quality metrics. In both real and synthetic data, the summary performance index favored the denoising strategy including the regression of mean signals from white matter and cerebrospinal fluid brain areas and global signal. This pipeline resulted in the best compromise between artifact removal and preservation of the information on resting-state networks. Our study provided useful methodological tools and key information on the effectiveness of multiple denoising strategies for rs-fMRI data. Besides providing a robust comparison approach that could be adapted to other fMRI studies, a suitable denoising pipeline for rs-fMRI data was identified, which could be used to improve the reproducibility of rs-fMRI findings.

Applications and advances of combined fMRI-fNIRs techniques in brain functional research

Understanding the intricate functions of the human brain requires multimodal approaches that integrate complementary neuroimaging techniques. This review systematically examines the integration of functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRs) in brain functional research, addressing their synergistic potential, methodological advancements, clinical and neuroscientific applications, and persistent challenges. We conducted a comprehensive literature review of 63 studies (from PubMed and Web of Science up to September 2024) using keyword combinations such as fMRI, fNIRs, and multimodal imaging. Our analysis reveals three key findings: (1) Methodological Synergy: Combining fMRI's high spatial resolution with fNIRs's superior temporal resolution and portability enables robust spatiotemporal mapping of neural activity, validated across motor, cognitive, and clinical tasks. Additionally, this study examines experimental paradigms and data processing techniques essential for effective multimodal neuroimaging. (2) Applications: The review categorizes integration methodologies into synchronous and asynchronous detection modes, highlighting their respective applications in spatial localization, validation of efficacy, and mechanism discovery. Synchronous and asynchronous integration modes have advanced research in neurological disorders (e.g., stroke, Alzheimer's), social cognition, and neuroplasticity, while novel hyperscanning paradigms extend applications to naturalistic, interactive settings. (3) Challenges: Hardware incompatibilities (e.g., electromagnetic interference in MRI environments), experimental limitations (e.g., restricted motion paradigms), and data fusion complexities hinder widespread adoption. The future direction emphasizes hardware innovation (such as fNIR probe compatible with MRI), standardized protocol and data integration driven by machine learning, etc. to solve the depth limitation of fNIR and infer subcortical activities. This synthesis underscores the transformative potential of fMRI-fNIRs integration in bridging spatial and temporal gaps in neuroimaging, while enhancing diagnostic and therapeutic strategies and paving the way for future innovations in brain research.

TR(Acking) Individuals Down: Exploring the Effect of Temporal Resolution in Resting-State Functional MRI Fingerprinting

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisition's temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s (TR(s)/identifiability(%): 0.5/64; 0.7/47; 1/44; 2/44; 3/56). We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make an higher contribution to subject identifiability (functional connections with highest mean ICC: within subcortical network [SUB; ICC = 0.0387], within default mode network [DMN; ICC = 0.0058]; between DMN and somato-motor [SM] network [ICC = 0.0013]; between ventral attention network [VA] and DMN [ICC = 0.0008]; between VA and SM [ICC = 0.0007]), whereas sensory-motor regions become more influential when integrating data from different TRs (functional connections with highest mean ICC: within fronto-parietal network [ICC = 0.382], within dorsal attention network [DA; ICC = 0.373]; within SUB [ICC = 0.367]; between visual network [VIS] and DA [ICC = 0.362]; within VIS [ICC = 0.358]). We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signal's sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks' results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

Cardiovascular and vasomotor pulsations in the brain and periphery during awake and NREM sleep in a multimodal fMRI study

Introduction

The cerebrospinal fluid dynamics in the human brain are driven by physiological pulsations, including cardiovascular pulses and very low-frequency (< 0.1 Hz) vasomotor waves. Ultrafast functional magnetic resonance imaging (fMRI) facilitates the simultaneous measurement of these signals from venous and arterial compartments independently with both classical venous blood oxygenation level dependent (BOLD) and faster arterial spin-phase contrast.

Methods

In this study, we compared the interaction of these two pulsations in awake and sleep using fMRI and peripheral fingertip photoplethysmography in both arterial and venous signals in 10 healthy subjects (5 female).

Results

Sleep increased the power of brain cardiovascular pulsations, decreased peripheral pulsation, and desynchronized them. However, vasomotor waves increase power and synchronicity in both brain and peripheral signals during sleep. Peculiarly, lag between brain and peripheral vasomotor signals reversed in sleep within the default mode network. Finally, sleep synchronized cerebral arterial vasomotor waves with venous BOLD waves within distinct parasagittal brain tissue.

Discussion

These changes in power and pulsation synchrony may reflect systemic sleep-related changes in vascular control between the periphery and brain vasculature, while the increased synchrony of arterial and venous compartments may reflect increased convection of regional neurofluids in parasagittal areas in sleep.

Non-Cartesian 3D-SPARKLING vs Cartesian 3D-EPI encoding schemes for functional Magnetic Resonance Imaging at 7 Tesla

The quest for higher spatial and/or temporal resolution in functional MRI (fMRI) while preserving a sufficient temporal signal-to-noise ratio (tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs. 3D acquisition strategies, parallel imaging and/or compressed sensing (CS) accelerations and simultaneous multi-slice acquisitions to cite a few. In this paper, we investigate the use of a finely tuned version of 3D-SPARKLING. This is a non-Cartesian CS-based acquisition technique for high spatial resolution whole-brain fMRI. We compare it to state-of-the-art Cartesian 3D-EPI during both a retinotopic mapping paradigm and resting-state acquisitions at 1mm3 (isotropic spatial resolution). This study involves six healthy volunteers and both acquisition sequences were run on each individual in a randomly-balanced order across subjects. The performances of both acquisition techniques are compared to each other in regards to tSNR, sensitivity to the BOLD effect and spatial specificity. Our findings reveal that 3D-SPARKLING has a higher tSNR than 3D-EPI, an improved sensitivity to detect the BOLD contrast in the gray matter, and an improved spatial specificity. Compared to 3D-EPI, 3D-SPARKLING yields, on average, 7% more activated voxels in the gray matter relative to the total number of activated voxels.

I know why the caged bird sings: Distress tolerant individuals show greater resting state connectivity between ventromedial prefrontal cortex and right amygdala as a function of higher vagal tone

BACKGROUND

Intolerance to psychological distress is associated with various forms of psychopathology, ranging from addiction to mood disturbance. The capacity to withstand aversive affective states is often explained by individual differences in cardiovagal tone as well as resting state connectivity of the ventromedial prefrontal cortex (vmPFC), a region involved in the regulation of emotions and cardio-autonomic tone. However, it is unclear which brain regions involved in distress tolerance show greater resting state functional connectivity (rsFC) as a function of resting heart rate variability (HRV).

METHODS

One-hundred and twenty-six adults, aged 20 to 83.5 years, were selected from a lifespan cohort at the Nathan Kline Institute-Rockland Sample. Participants' distress tolerance levels were assessed based upon performance on the Behavioral Indicator of Resiliency to Distress (BIRD) task. Artifact-free resting-state functional brain scans collected during separate sessions were used. While inside the scanner, a pulse oximeter was used to record beat-to-beat intervals to derive high-frequency heart rate variability (HF-HRV). The relationship between HF-HRV and vmPFC to whole brain functional connectivity was compared between distress tolerant (BIRD completers) and distress intolerant (BIRD non-completers).

RESULTS

Groups did not differ in their history of psychiatric diagnosis. Higher resting HF-HRV was associated with longer total time spent on the BIRD task for the entire sample (r = 0.255, p = 0.004). After controlling for age, gender, body mass index, head motion, and gray matter volume. Distress tolerant individuals showed greater rsFC (p < 0.005 (uncorrected), k = 20) between the vmPFC and default-mode network (DMN) hubs including posterior cingulate cortex/precuneus, medial temporal lobes, and the parahippocampal cortex. As a function of higher resting HF-HRV greater vmPFC connectivity was observed with sub-threshold regions in the right amygdala and left anterior prefrontal cortex, with the former passing small volume correction, in distress tolerant versus distress intolerant individuals.

CONCLUSION

In a lifespan sample of community-dwelling adults, distress tolerant individuals showed greater vmPFC connectivity with anterior and posterior hubs of the DMN compared to distress intolerant individuals. As a function of greater HF-HRV, distress tolerant individuals evidenced greater vmPFC with salience and executive control network hubs. These findings are consistent with deficits in neural resource allocation within a triple network resting amongst persons exhibiting behavioral intolerance to psychological distress.

Synchronous functional magnetic resonance eye imaging, video ophthalmoscopy, and eye surface imaging reveal the human brain and eye pulsation mechanisms

The eye possesses a paravascular solute transport pathway that is driven by physiological pulsations, resembling the brain glymphatic pathway. We developed synchronous multimodal imaging tools aimed at measuring the driving pulsations of the human eye, using an eye-tracking functional eye camera (FEC) compatible with magnetic resonance imaging (MRI) for measuring eye surface pulsations. Special optics enabled integration of the FEC with MRI-compatible video ophthalmoscopy (MRcVO) for simultaneous retinal imaging along with functional eye MRI imaging (fMREye) of the BOLD (blood oxygen level dependent) contrast. Upon optimizing the fMREye parameters, we measured the power of the physiological (vasomotor, respiratory, and cardiac) eye and brain pulsations by fast Fourier transform (FFT) power analysis. The human eye pulsated in all three physiological pulse bands, most prominently in the respiratory band. The FFT power means of physiological pulsation for two adjacent slices was significantly higher than in one-slice scans (RESP1 vs. RESP2; df = 5, p = 0.045). FEC and MRcVO confirmed the respiratory pulsations at the eye surface and retina. We conclude that in addition to the known cardiovascular pulsation, the human eye also has respiratory and vasomotor pulsation mechanisms, which are now amenable to study using non-invasive multimodal imaging of eye fluidics.

Electome network factors: Capturing emotional brain networks related to health and disease

Therapeutic development for mental disorders has been slow despite the high worldwide prevalence of illness. Unfortunately, cellular and circuit insights into disease etiology have largely failed to generalize across individuals that carry the same diagnosis, reflecting an unmet need to identify convergent mechanisms that would facilitate optimal treatment. Here, we discuss how mesoscale networks can encode affect and other cognitive processes. These networks can be discovered through electrical functional connectome (electome) analysis, a method built upon explainable machine learning models for analyzing and interpreting mesoscale brain-wide signals in a behavioral context. We also outline best practices for identifying these generalizable, interpretable, and biologically relevant networks. Looking forward, translational electome analysis can span species and various moods, cognitive processes, or other brain states, supporting translational medicine. Thus, we argue that electome analysis provides potential translational biomarkers for developing next-generation therapeutics that exhibit high efficacy across heterogeneous disorders.

Resting-state alterations in behavioral variant frontotemporal dementia are related to the distribution of monoamine and GABA neurotransmitter systems

Background

Aside to clinical changes, behavioral variant frontotemporal dementia (bvFTD) is characterized by progressive structural and functional alterations in frontal and temporal regions. We examined if there is a selective vulnerability of specific neurotransmitter systems in bvFTD by evaluating the link between disease-related functional alterations and the spatial distribution of specific neurotransmitter systems and their underlying gene expression levels.

Methods

Maps of fractional amplitude of low-frequency fluctuations (fALFF) were derived as a measure of local activity from resting-state functional magnetic resonance imaging for 52 bvFTD patients (mean age = 61.5 ± 10.0 years; 14 females) and 22 healthy controls (HC) (mean age = 63.6 ± 11.9 years; 13 females). We tested if alterations of fALFF in patients co-localize with the non-pathological distribution of specific neurotransmitter systems and their coding mRNA gene expression. Furthermore, we evaluated if the strength of co-localization is associated with the observed clinical symptoms.

Results

Patients displayed significantly reduced fALFF in frontotemporal and frontoparietal regions. These alterations co-localized with the distribution of serotonin (5-HT1b and 5-HT2a) and γ-aminobutyric acid type A (GABAa) receptors, the norepinephrine transporter (NET), and their encoding mRNA gene expression. The strength of co-localization with NET was associated with cognitive symptoms and disease severity of bvFTD.

Conclusions

Local brain functional activity reductions in bvFTD followed the distribution of specific neurotransmitter systems indicating a selective vulnerability. These findings provide novel insight into the disease mechanisms underlying functional alterations. Our data-driven method opens the road to generate new hypotheses for pharmacological interventions in neurodegenerative diseases even beyond bvFTD.

Funding

This study has been supported by the German Consortium for Frontotemporal Lobar Degeneration, funded by the German Federal Ministry of Education and Research (BMBF; grant no. FKZ01GI1007A).

Investigating cognitive reserve, symptom resolution and brain connectivity in mild traumatic brain injury

BACKGROUND

A proportion of patients with mild traumatic brain injury (mTBI) suffer long-term consequences, and the reasons behind this are still poorly understood. One factor that may affect outcomes is cognitive reserve, which is the brain's ability to maintain cognitive function despite injury. It is often assessed through educational level or premorbid IQ tests. This study aimed to explore whether there were differences in post-concussion symptoms and symptom resolution between patients with mTBI and minor orthopedic injuries one week and three months after injury. Additional aims were to explore the relationship between cognitive reserve and outcome, as well as functional connectivity according to resting state functional magnetic resonance imaging (rs-fMRI).

METHOD

Fifteen patients with mTBI and 15 controls with minor orthopedic injuries were recruited from the emergency department. Assessments, including Rivermead Post-Concussion Questionnaire (RPQ), neuropsychological testing, and rs-fMRI scans, were conducted on average 7 days (SD = 2) and 122 days (SD = 51) after injury.

RESULTS

At the first time point, significantly higher rates of post-concussion symptoms (U = 40.0, p = 0.003), state fatigue (U = 56.5, p = 0.014), and fatigability (U = 58.5, p = 0.025) were observed among the mTBI group than among the controls. However, after three months, only the difference in post-concussion symptoms remained significant (U = 27.0, p = 0.003). Improvement in post-concussion symptoms was found to be significantly correlated with cognitive reserve, but only in the mTBI group (Spearman's rho = -0.579, p = .038). Differences in the trajectory of recovery were also observed for fatigability between the two groups (U = 36.5, p = 0.015). Moreover, functional connectivity differences in the frontoparietal network were observed between the groups, and for mTBI patients, functional connectivity differences in an executive control network were observed over time.

CONCLUSION

The findings of this pilot study suggest that mTBI, compared to minor orthopedic trauma, is associated to both functional connectivity changes in the brain and concussion-related symptoms. While there is improvement in these symptoms over time, a small subgroup with lower cognitive reserve appears to experience more persistent and possibly worsening symptoms over time. This, however, needs to be validated in larger studies.

TRIAL REGISTRATION

NCT05593172. Retrospectively registered.

Effect of sleep deprivation and NREM sleep stage on physiological brain pulsations

Introduction

Sleep increases brain fluid transport and the power of pulsations driving the fluids. We investigated how sleep deprivation or electrophysiologically different stages of non-rapid-eye-movement (NREM) sleep affect the human brain pulsations.

Methods

Fast functional magnetic resonance imaging (fMRI) was performed in healthy subjects (n = 23) with synchronous electroencephalography (EEG), that was used to verify arousal states (awake, N1 and N2 sleep). Cardiorespiratory rates were verified with physiological monitoring. Spectral power analysis assessed the strength, and spectral entropy assessed the stability of the pulsations.

Results

In N1 sleep, the power of vasomotor (VLF < 0.1 Hz), but not cardiorespiratory pulsations, intensified after sleep deprived vs. non-sleep deprived subjects. The power of all three pulsations increased as a function of arousal state (N2 > N1 > awake) encompassing brain tissue in both sleep stages, but extra-axial CSF spaces only in N2 sleep. Spectral entropy of full band and respiratory pulsations decreased most in N2 sleep stage, while cardiac spectral entropy increased in ventricles.

Discussion

In summary, the sleep deprivation and sleep depth, both increase the power and harmonize the spectral content of human brain pulsations.

Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset

Cortical depth-dependent functional magnetic resonance image (fMRI), also known as layer-fMRI, has the potential to capture directional neural information flow of brain computations within and across large-scale cortical brain networks. E.g., layer-fMRI can differentiate feedforward and feedback cortical input in hierarchically organized brain networks. Recent advancements in 3D-EPI sampling approaches and MR contrast generation strategies have allowed proof-of-principle studies showing that layer-fMRI can provide sufficient data quality for capturing laminar changes in functional connectivity. These studies have however not shown how reliable the signal is and how repeatable the respective results are. It is especially unclear whether whole-brain layer-fMRI functional connectivity protocols are widely applicable across common neuroscience-driven analysis approaches. Moreover, there are no established preprocessing fMRI methods that are optimized to work for whole-brain layer-fMRI datasets. In this work, we aimed to serve the field of layer-fMRI and build tools for future routine whole-brain layer-fMRI in application-based neuroscience research. We have developed publicly available sequences, acquisition protocols, and processing pipelines for whole-brain layer-fMRI. These protocols are validated across 60 hours of scanning in nine participants. Specifically, we identified and exploited methodological advancements for maximizing tSNR efficiency and test-retest reliability. We are sharing an extensive multi-modal whole-brain layer-fMRI dataset (20 scan hours of movie-watching in a single participant) for the purpose of benchmarking future method developments: The Kenshu dataset. With this dataset, we are also exemplifying the usefulness of whole brain layer-fMRI for commonly applied analysis approaches in modern cognitive neuroscience fMRI studies. This includes connectivity analyses, representational similarity matrix estimations, general linear model analyses, principal component analysis clustering, etc. We believe that this work paves the road for future routine measurements of directional functional connectivity across the entire brain.

Low-frequency oscillations in the brain show differential regional associations with severity of cerebral small vessel disease: a systematic review

Background

Cerebral small vessel disease (cSVD) is associated with endothelial dysfunction but the pathophysiology is poorly understood. Low-frequency oscillations (LFOs) in the BOLD signal partly reflect cerebrovascular function and have the potential to identify endothelial dysfunction in cSVD. A systematic review was performed to assess the reported relationships between imaging markers of cSVD and LFOs.

Methods

Medline and EMBASE were searched for original studies reporting an association between LFOs and STRIVE-defined imaging markers of cSVD, including: white matter hyperintensities (WMH), enlarged perivascular spaces, lacunes, CADASIL, and cerebral microbleeds, from inception to September 1, 2022. Variations in LFOs were extracted, where available, on a global, tissue-specific, or regional level, in addition to participant demographics, data acquisition, methods of analysis, and study quality. Where a formal meta-analysis was not possible, differences in the number of studies reporting LFO magnitude by presence or severity of cSVD were determined by sign test.

Results

15 studies were included from 841 titles. Studies varied in quality, acquisition parameters, and in method of analysis. Amplitude of low-frequency fluctuation (ALFF) in resting state fMRI was most commonly assessed (12 studies). Across 15 studies with differing markers of cSVD (9 with WMH; 1 with cerebral microbleeds; 1 with lacunar infarcts; 1 with CADASIL; 3 with multiple markers), LFOs in patients with cSVD were decreased in the posterior cortex (22 of 32 occurrences across all studies, p = 0.05), increased in the deep grey nuclei (7 of 7 occurrences across all studies, p = 0.016), and potentially increased in the temporal lobes (9 of 11 occurrences across all studies, p = 0.065).

Conclusion

Despite limited consensus on the optimal acquisition and analysis methods, there was reasonably consistent regional variation in LFO magnitude by severity of cSVD markers, supporting its potential as a novel index of endothelial dysfunction. We propose a consistent approach to measuring LFOs to characterise targetable mechanisms underlying cSVD.

Cortical depth-dependent human fMRI of resting-state networks using EPIK

Introduction

Recent laminar-fMRI studies have substantially improved understanding of the evoked cortical responses in multiple sub-systems; in contrast, the laminar component of resting-state networks spread over the whole brain has been less studied due to technical limitations. Animal research strongly suggests that the supragranular layers of the cortex play a critical role in maintaining communication within the default mode network (DMN); however, whether this is true in this and other human cortical networks remains unclear.

Methods

Here, we used EPIK, which offers unprecedented coverage at sub-millimeter resolution, to investigate cortical broad resting-state dynamics with depth specificity in healthy volunteers.

Results

Our results suggest that human DMN connectivity is primarily supported by intermediate and superficial layers of the cortex, and furthermore, the preferred cortical depth used for communication can vary from one network to another. In addition, the laminar connectivity profile of some networks showed a tendency to change upon engagement in a motor task. In line with these connectivity changes, we observed that the amplitude of the low-frequency-fluctuations (ALFF), as well as the regional homogeneity (ReHo), exhibited a different laminar slope when subjects were either performing a task or were in a resting state (less variation among laminae, i.e., lower slope, during task performance compared to rest).

Discussion

The identification of varied laminar profiles concerning network connectivity, ALFF, and ReHo, observed across two brain states (task vs. rest) has major implications for the characterization of network-related diseases and suggests the potential diagnostic value of laminar fMRI in psychiatric disorders, e.g., to differentiate the cortical dynamics associated with disease stages linked, or not linked, to behavioral changes. The evaluation of laminar-fMRI across the brain encompasses computational challenges; nonetheless, it enables the investigation of a new dimension of the human neocortex, which may be key to understanding neurological disorders from a novel perspective.

Impact of amplitude and phase of fMRI time series for functional connectivity analysis

PURPOSE

Several studies in the field of fMRI have reported the synchrony between the brain regions using instantaneous phase (IP) representation (derived from analytic representation of BOLD time series). We hypothesized that instantaneous amplitude (IA) representation from different brain regions might give additional information to the functional brain networks. To validate this, we explored this representation of resting state BOLD fMRI signal for deriving resting state networks (RSNs) and compared it with the IP representation based RSNs.

METHOD

Resting state fMRI data of 100 healthy adults (age = 20-35 years, 54 females) from the population of 500 Subjects of HCP dataset were studied. Data was acquired using a 3 T scanner in four runs (15-min each) with the phase encoding directions: Left to Right (LR), Right to Left (RL). These four runs were acquired in two sessions, and subjects were asked to keep their eyes open with a fixation on a white cross. The IA and IP representations were derived from a narrow-band filtered BOLD time series using Hilbert transforms and a seed-based approach is used to compute the RSNs in the brain.

RESULTS

The experimental results demonstrate that within the frequency range 0.01-0.1 Hz, IA representation based RSNs have the highest similarity score between the two sessions for the motor network. Whereas for fronto-parietal network, IP based activation maps have the highest similarity score for all the frequency bands. For higher frequency band (0.198-0.25 Hz) consistency of the obtained RSNs across two sessions reduced for both IA and IP representations. Fusion of IA and IP representations based RSNs in comparison to those of IP based representation, leads to 3-10% improvement in the similarity scores between the default mode network obtained for the two sessions. In addition, the same comparison demonstrates 15-20% improvement for the motor network in the frequency bands: 0.01-0.04 Hz, 0.04-0.07 Hz, slow5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz). It is also observed that the similarity score between two sessions using instantaneous frequency (IF) (derivative of unwrapped IP) representation in exploring functional connectivity (FC) networks is comparable with those obtained using IP representation.

CONCLUSION

Our findings suggest that IA-representation based measures can estimate RSNs with the reproducibility between the sessions comparable to that of the IP representation-based methods. This study demonstrates that IA and IP representations contain the complementary information of BOLD signal, and their fusion improves the results of FC.

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction

The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.

Network organization of resting-state cerebral hemodynamics and their aliasing contributions measured by functional near-infrared spectroscopy

Objective. Spontaneous fluctuations of cerebral hemodynamics measured by functional magnetic resonance imaging (fMRI) are widely used to study the network organization of the brain. The temporal correlations among the ultra-slow, <0.1 Hz fluctuations across the brain regions are interpreted as functional connectivity maps and used for diagnostics of neurological disorders. However, despite the interest narrowed in the ultra-slow fluctuations, hemodynamic activity that exists beyond the ultra-slow frequency range could contribute to the functional connectivity, which remains unclear.Approach. In the present study, we have measured the brain-wide hemodynamics in the human participants with functional near-infrared spectroscopy (fNIRS) in a whole-head, cap-based and high-density montage at a sampling rate of 6.25 Hz. In addition, we have acquired resting state fMRI scans in the same group of participants for cross-modal evaluation of the connectivity maps. Then fNIRS data were deliberately down-sampled to a typical fMRI sampling rate of ∼0.5 Hz and the resulted differential connectivity maps were subject to a k-means clustering.Main results. Our diffuse optical topographical analysis of fNIRS data have revealed a default mode network (DMN) in the spontaneous deoxygenated and oxygenated hemoglobin changes, which remarkably resemble the same fMRI network derived from participants. Moreover, we have shown that the aliased activities in the down-sampled optical signals have altered the connectivity patterns, resulting in a network organization of aliased functional connectivity in the cerebral hemodynamics.Significance.The results have for the first time demonstrated that fNIRS as a broadly accessible modality can image the resting-state functional connectivity in the posterior midline, prefrontal and parietal structures of the DMN in the human brain, in a consistent pattern with fMRI. Further empowered by the fast sampling rate of fNIRS, our findings suggest the presence of aliased connectivity in the current understanding of the human brain organization.

Altered spontaneous brain activity during dobutamine challenge in healthy young adults: A resting-state functional magnetic resonance imaging study

Introduction

There is a growing interest in exploring brain-heart interactions. However, few studies have investigated the brain-heart interactions in healthy populations, especially in healthy young adults. The aim of this study was to explore the association between cardiovascular and spontaneous brain activities during dobutamine infusion in healthy young adults.

Methods

Forty-eight right-handed healthy participants (43 males and 5 females, range: 22-34 years) underwent vital signs monitoring, cognitive function assessment and brain MRI scans. Cardiovascular function was evaluated using blood pressure and heart rate, while two resting-state functional magnetic resonance imaging (rs-fMRI) methods-regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF)-were used together to reflect the local neural activity of the brain. Logistic regression was used to model the association between brain and heart.

Results

Results showed that blood pressure and heart rate significantly increased after dobutamine infusion, and the performance in brain functional activity was the decrease in ReHo in the left gyrus rectus and in ALFF in the left frontal superior orbital. The results of logistic regression showed that the difference of diastolic blood pressure (DBP) had significant positive relationship with the degree of change of ReHo, while the difference of systolic blood pressure (SBP) had significant negative impact on the degree of change in ALFF.

Discussion

These findings suggest that the brain-heart interactions exist in healthy young adults under acute cardiovascular alterations, and more attention should be paid to blood pressure changes in young adults and assessment of frontal lobe function to provide them with more effective health protection management.

Measuring brain beats: Cardiac-aligned fast functional magnetic resonance imaging signals

Blood and cerebrospinal fluid (CSF) pulse and flow throughout the brain, driven by the cardiac cycle. These fluid dynamics, which are essential to healthy brain function, are characterized by several noninvasive magnetic resonance imaging (MRI) methods. Recent developments in fast MRI, specifically simultaneous multislice acquisition methods, provide a new opportunity to rapidly and broadly assess cardiac-driven flow, including CSF spaces, surface vessels and parenchymal vessels. We use these techniques to assess blood and CSF flow dynamics in brief (3.5 min) scans on a conventional 3 T MRI scanner in five subjects. Cardiac pulses are measured with a photoplethysmography (PPG) on the index finger, along with functional MRI (fMRI) signals in the brain. We, retrospectively, align the fMRI signals to the heartbeat. Highly reliable cardiac-gated fMRI temporal signals are observed in CSF and blood on the timescale of one heartbeat (test-retest reliability within subjects R²  > 50%). In blood vessels, a local minimum is observed following systole. In CSF spaces, the ventricles and subarachnoid spaces have a local maximum following systole instead. Slower resting-state scans with slice timing, retrospectively, aligned to the cardiac pulse, reveal similar cardiac-gated responses. The cardiac-gated measurements estimate the amplitude and phase of fMRI pulsations in the CSF relative to those in the arteries, an estimate of the local intracranial impedance. Cardiac aligned fMRI signals can provide new insights about fluid dynamics or diagnostics for diseases where these dynamics are important.

Increased interictal synchronicity of respiratory related brain pulsations in epilepsy

Respiratory brain pulsations have recently been shown to drive electrophysiological brain activity in patients with epilepsy. Furthermore, functional neuroimaging indicates that respiratory brain pulsations have increased variability and amplitude in patients with epilepsy compared to healthy individuals. To determine whether the respiratory drive is altered in epilepsy, we compared respiratory brain pulsation synchronicity between healthy controls and patients. Whole brain fast functional magnetic resonance imaging was performed on 40 medicated patients with focal epilepsy, 20 drug-naïve patients and 102 healthy controls. Cerebrospinal fluid associated respiratory pulsations were used to generate individual whole brain respiratory synchronization maps, which were compared between groups. Finally, we analyzed the seizure frequency effect and diagnostic accuracy of the respiratory synchronization defect in epilepsy. Respiratory brain pulsations related to the verified fourth ventricle pulsations were significantly more synchronous in patients in frontal, periventricular and mid-temporal regions, while the seizure frequency correlated positively with synchronicity. The respiratory brain synchronicity had a good diagnostic accuracy (ROC_AUC = 0.75) in discriminating controls from medicated patients. The elevated respiratory brain synchronicity in focal epilepsy suggests altered physiological effect of cerebrospinal fluid pulsations possibly linked to regional brain water dynamics involved with interictal brain physiology.

Increased very low frequency pulsations and decreased cardiorespiratory pulsations suggest altered brain clearance in narcolepsy

Background

Narcolepsy is a chronic neurological disease characterized by daytime sleep attacks, cataplexy, and fragmented sleep. The disease is hypothesized to arise from destruction or dysfunction of hypothalamic hypocretin-producing cells that innervate wake-promoting systems including the ascending arousal network (AAN), which regulates arousal via release of neurotransmitters like noradrenalin. Brain pulsations are thought to drive intracranial cerebrospinal fluid flow linked to brain metabolite transfer that sustains homeostasis. This flow increases in sleep and is suppressed by noradrenalin in the awake state. Here we tested the hypothesis that narcolepsy is associated with altered brain pulsations, and if these pulsations can differentiate narcolepsy type 1 from healthy controls.

Methods

In this case-control study, 23 patients with narcolepsy type 1 (NT1) were imaged with ultrafast fMRI (MREG) along with 23 age- and sex-matched healthy controls (HC). The physiological brain pulsations were quantified as the frequency-wise signal variance. Clinical relevance of the pulsations was investigated with correlation and receiving operating characteristic analysis.

Results

We find that variance and fractional variance in the very low frequency (MREG_vlf) band are greater in NT1 compared to HC, while cardiac (MREG_card) and respiratory band variances are lower. Interestingly, these pulsations differences are prominent in the AAN region. We further find that fractional variance in MREG_vlf shows promise as an effective bi-classification metric (AUC = 81.4%/78.5%), and that disease severity measured with narcolepsy severity score correlates with MREG_card variance (R = −0.48, p = 0.0249).

Conclusions

We suggest that our novel results reflect impaired CSF dynamics that may be linked to altered glymphatic circulation in narcolepsy type 1.

The flow of fluid surrounding and inside the human brain is thought to be caused by the movement of brain vessels, breathing and heart rate. These so called brain pulsations are linked to clearing waste from the brain. This process is increased during sleep and suppressed while we are awake. Narcolepsy is a neurological disease where the brain areas regulating being awake and asleep are affected. The diagnosis requires time-consuming hospital tests and is often delayed which has a prolonged negative impact on the patients. Here, we use brain imaging to investigate whether brain pulsations are altered in patients with narcolepsy, and if they can be utilized to differentiate patients with narcolepsy from healthy individuals. We find that narcolepsy affects all brain pulsations, and these findings show promise as an additional diagnostic tool that could help detect the disease earlier.

Järvelä et al. investigate if narcolepsy is associated with altered brain pulsations using ultrafast fMRI. They find differences in the brain pulsations between narcolepsy type 1 patients and healthy controls that may link to altered brain clearance in narcolepsy, have diagnostic potential and correlate with the severity of narcolepsy.

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

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

Combination of Group Singular Value Decomposition and eLORETA Identifies Human EEG Networks and Responses to Transcranial Photobiomodulation

Transcranial Photobiomodulation (tPBM) has demonstrated its ability to alter electrophysiological activity in the human brain. However, it is unclear how tPBM modulates brain electroencephalogram (EEG) networks and is related to human cognition. In this study, we recorded 64-channel EEG from 44 healthy humans before, during, and after 8-min, right-forehead, 1,064-nm tPBM or sham stimulation with an irradiance of 257 mW/cm². In data processing, a novel methodology by combining group singular value decomposition (gSVD) with the exact low-resolution brain electromagnetic tomography (eLORETA) was implemented and performed on the 64-channel noise-free EEG time series. The gSVD+eLORETA algorithm produced 11 gSVD-derived principal components (PCs) projected in the 2D sensor and 3D source domain/space. These 11 PCs took more than 70% weight of the entire EEG signals and were justified as 11 EEG brain networks. Finally, baseline-normalized power changes of each EEG brain network in each EEG frequency band (delta, theta, alpha, beta and gamma) were quantified during the first 4-min, second 4-min, and post tPBM/sham periods, followed by comparisons of frequency-specific power changes between tPBM and sham conditions. Our results showed that tPBM-induced increases in alpha powers occurred at default mode network, executive control network, frontal parietal network and lateral visual network. Moreover, the ability to decompose EEG signals into individual, independent brain networks facilitated to better visualize significant decreases in gamma power by tPBM. Many similarities were found between the cortical locations of SVD-revealed EEG networks and fMRI-identified resting-state networks. This consistency may shed light on mechanistic associations between tPBM-modulated brain networks and improved cognition outcomes.

Cardiovascular Pulsatility Increases in Visual Cortex Before Blood Oxygen Level Dependent Response During Stimulus

The physiological pulsations that drive tissue fluid homeostasis are not well characterized during brain activation. Therefore, we used fast magnetic resonance encephalography (MREG) fMRI to measure full band (0–5 Hz) blood oxygen level-dependent (BOLD_FB) signals during a dynamic visual task in 23 subjects. This revealed brain activity in the very low frequency (BOLD_VLF) as well as in cardiac and respiratory bands. The cardiovascular hemodynamic envelope (CHe) signal correlated significantly with the visual BOLD_VLF response, considered as an independent signal source in the V1-V2 visual cortices. The CHe preceded the canonical BOLD_VLF response by an average of 1.3 (± 2.2) s. Physiologically, the observed CHe signal could mark increased regional cardiovascular pulsatility following vasodilation.

Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI

Despite remarkable advances in mapping the functional connectivity of the cortex, the functional connectivity of subcortical regions is understudied in living humans. This is the case for brainstem nuclei that control vital processes, such as autonomic, limbic, nociceptive and sensory functions. This is because of the lack of precise brainstem nuclei localization, of adequate sensitivity and resolution in the deepest brain regions, as well as of optimized processing for the brainstem. To close the gap between the cortex and the brainstem, on 20 healthy subjects, we computed a correlation-based functional connectome of 15 brainstem nuclei involved in autonomic, limbic, nociceptive, and sensory function (superior and inferior colliculi, ventral tegmental area-parabrachial pigmented nucleus complex, microcellular tegmental nucleus-prabigeminal nucleus complex, lateral and medial parabrachial nuclei, vestibular and superior olivary complex, superior and inferior medullary reticular formation, viscerosensory motor nucleus, raphe magnus, pallidus, and obscurus, and parvicellular reticular nucleus - alpha part) with the rest of the brain. Specifically, we exploited 1.1mm isotropic resolution 7 Tesla resting-state fMRI, ad-hoc coregistration and physiological noise correction strategies, and a recently developed probabilistic template of brainstem nuclei. Further, we used 2.5mm isotropic resolution resting-state fMRI data acquired on a 3 Tesla scanner to assess the translatability of our results to conventional datasets. We report highly consistent correlation coefficients across subjects, confirming available literature on autonomic, limbic, nociceptive and sensory pathways, as well as high interconnectivity within the central autonomic network and the vestibular network. Interestingly, our results showed evidence of vestibulo-autonomic interactions in line with previous work. Comparison of 7 Tesla and 3 Tesla findings showed high translatability of results to conventional settings for brainstem-cortical connectivity and good yet weaker translatability for brainstem-brainstem connectivity. The brainstem functional connectome might bring new insight in the understanding of autonomic, limbic, nociceptive and sensory function in health and disease.

Functional connectome of arousal and motor brainstem nuclei in living humans by 7 Tesla resting-state fMRI

Brainstem nuclei play a pivotal role in many functions, such as arousal and motor control. Nevertheless, the connectivity of arousal and motor brainstem nuclei is understudied in living humans due to the limited sensitivity and spatial resolution of conventional imaging, and to the lack of atlases of these deep tiny regions of the brain. For a holistic comprehension of sleep, arousal and associated motor processes, we investigated in 20 healthy subjects the resting-state functional connectivity of 18 arousal and motor brainstem nuclei in living humans. To do so, we used high spatial-resolution 7 Tesla resting-state fMRI, as well as a recently developed in-vivo probabilistic atlas of these nuclei in stereotactic space. Further, we verified the translatability of our brainstem connectome approach to conventional (e.g. 3 Tesla) fMRI. Arousal brainstem nuclei displayed high interconnectivity, as well as connectivity to the thalamus, hypothalamus, basal forebrain and frontal cortex, in line with animal studies and as expected for arousal regions. Motor brainstem nuclei showed expected connectivity to the cerebellum, basal ganglia and motor cortex, as well as high interconnectivity. Comparison of 3 Tesla to 7 Tesla connectivity results indicated good translatability of our brainstem connectome approach to conventional fMRI, especially for cortical and subcortical (non-brainstem) targets and to a lesser extent for brainstem targets. The functional connectome of 18 arousal and motor brainstem nuclei with the rest of the brain might provide a better understanding of arousal, sleep and accompanying motor function in living humans in health and disease.

An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI

Resting-state functional magnetic resonance imaging (fMRI) has drastically expanded the scope of brain research by advancing our knowledge about the topologies, dynamics, and interspecies translatability of functional brain networks. Several databases have been developed and shared in accordance with recent key initiatives in the rodent fMRI community to enhance the transparency, reproducibility, and interpretability of data acquired at various sites. Despite these pioneering efforts, one notable challenge preventing efficient standardization in the field is the customary choice of anisotropic echo planar imaging (EPI) schemes with limited spatial coverage. Imaging with anisotropic resolution and/or reduced brain coverage has significant shortcomings including reduced registration accuracy and increased deviation in brain feature detection. Here we proposed a high-spatial-resolution (0.4 mm), isotropic, whole-brain EPI protocol for the rat brain using a horizontal slicing scheme that can maintain a functionally relevant repetition time (TR), avoid high gradient duty cycles, and offer unequivocal whole-brain coverage. Using this protocol, we acquired resting-state EPI fMRI data from 87 healthy rats under the widely used dexmedetomidine sedation supplemented with low-dose isoflurane on a 9.4 T MRI system. We developed an EPI template that closely approximates the Paxinos and Watson's rat brain coordinate system and demonstrated its ability to improve the accuracy of group-level approaches and streamline fMRI data pre-processing. Using this database, we employed a multi-scale dictionary-learning approach to identify reliable spatiotemporal features representing rat brain intrinsic activity. Subsequently, we performed k-means clustering on those features to obtain spatially discrete, functional regions of interest (ROIs). Using Euclidean-based hierarchical clustering and modularity-based partitioning, we identified the topological organizations of the rat brain. Additionally, the identified group-level FC network appeared robust across strains and sexes. The "triple-network" commonly adapted in human fMRI were resembled in the rat brain. Through this work, we disseminate raw and pre-processed isotropic EPI data, a rat brain EPI template, as well as identified functional ROIs and networks in standardized rat brain coordinates. We also make our analytical pipelines and scripts publicly available, with the hope of facilitating rat brain resting-state fMRI study standardization.

Advances in resting state fMRI acquisitions for functional connectomics

Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.

The Relationship Between Functional Connectivity and Interoceptive Sensibility

Background: Interoceptive signals related to changes in heartbeat, respiration, and gastric functioning continuously feedback to the brain. The interpretation of these signals influences several cognitive, affective, and motoric functions. Previous research has highlighted the distinction between the ability to accurately detect interoceptive information (i.e., interoceptive accuracy) and an individual's beliefs about his or her interoceptive abilities (i.e., interoceptive sensibility). Although numerous studies have delineated the neural substrates of interoceptive accuracy, less is known about the brain areas involved with interoceptive sensibility. Materials and Methods: In the current study, 28 healthy participants completed the Multidimensional Assessment of Interoceptive Awareness (MAIA), a self-report measure of interoceptive sensibility, before undergoing a 7-min resting-state functional magnetic resonance imaging scan. IRB ethics approval was obtained prior to data collection. Results: Overall MAIA scores, as well as scores on its eight subscales, were entered as covariates in subsequent region-of-interest and independent-component analyses. These analyses yielded three key results. First, interoceptive sensibility was negatively correlated with the functional connectivity of visual regions. Second, the cerebellar resting-state network showed positive correlations with two MAIA subscales, suggesting that this structure plays a role in interoceptive functions. Finally, the functional connectivity of the insula, a structure critical for interoceptive accuracy, was not correlated with any of the MAIA scores. Conclusion: These results demonstrate that the brain areas associated with individual differences in interoceptive sensibility show relatively little overlap with those involved with the accurate detection of interoceptive information.

Resting-State Functional Connectivity of the Ageing Female Brain-Differences Between Young and Elderly Female Adults on Multislice Short TR rs-fMRI

Introduction: Age-related brain changes are one of the most important world health problems due to the rising lifespan and size of the elderly populations. The aim of the study was to assess the effect of ageing in women on coordinated brain activity between eight resting-state networks.

Material and Methods: The study group comprised 60 healthy female volunteers who were divided into two age groups: younger women (aged 20–30 n = 30) and older women (aged 55–80 n = 30). Resting-state data were collected during a 15 min scan in the eyes-closed condition using a 3T MR scanner. Data were preprocessed and analysed using the CONN toolbox version 19.c. The large-scale network analysis included a priori selected regions of interest of the default mode, the sensorimotor, the visual, the salience, the dorsal attention, the fronto-parietal, the language, and the cerebellar network.

Results: Within the visual, the default mode, the salience, and the sensorimotor network, the intra-network resting-state functional connectivity (RSFC) was significantly higher with increasing age. There was also a significant increase in the inter-network RSFC in older females compared to young females found in the following networks: sensorimotor lateral and salience, salience and language, salience and fronto-parietal, cerebellar anterior and default mode, cerebellar posterior and default mode, visual and sensorimotor lateral, visual and sensorimotor, visual lateral and default mode, language and cerebellar anterior, language and cerebellar posterior, fronto-parietal and cerebellar anterior, dorsal attention and sensorimotor, dorsal attention and default mode, sensorimotor superior, and salience. Compared to young females, elderly women presented bilaterally significantly lower inter-network RSFC of the salience supramarginal gyrus and cerebellar posterior, sensorimotor lateral, and cerebellar anterior network, and sensorimotor lateral and cerebellar posterior as well as sensorimotor superior and cerebellar posterior network.

Conclusion: Increased RSFC between some brain networks including the visual, the default mode, the salience, the sensorimotor, the language, the fronto-parietal, the dorsal attention, and the cerebellar networks in elderly females may function as a compensation mechanism during the ageing process of the brain. To the best of our knowledge, this study is the first to report the importance of increase of cerebellar networks RSFC during healthy female ageing.

Co-activation pattern alterations in autism spectrum disorder-A volume-wise hierarchical clustering fMRI study

INTRODUCTION

There has been a growing effort to characterize the time-varying functional connectivity of resting state (RS) fMRI brain networks (RSNs). Although voxel-wise connectivity studies have examined different sliding window lengths, nonsequential volume-wise approaches have been less common.

METHODS

Inspired by earlier co-activation pattern (CAP) studies, we applied hierarchical clustering (HC) to classify the image volumes of the RS-fMRI data on 28 adolescents with autism spectrum disorder (ASD) and their 27 typically developing (TD) controls. We compared the distribution of the ASD and TD groups' volumes in CAPs as well as their voxel-wise means. For simplification purposes, we conducted a group independent component analysis to extract 14 major RSNs. The RSNs' average z-scores enabled us to meaningfully regroup the RSNs and estimate the percentage of voxels within each RSN for which there was a significant group difference. These results were jointly interpreted to find global group-specific patterns.

RESULTS

We found similar brain state proportions in 58 CAPs (clustering interval from 2 to 30). However, in many CAPs, the voxel-wise means differed significantly within a matrix of 14 RSNs. The rest-activated default mode-positive and default mode-negative brain state properties vary considerably in both groups over time. This division was seen clearly when the volumes were partitioned into two CAPs and then further examined along the HC dendrogram of the diversifying brain CAPs. The ASD group network activations followed a more heterogeneous distribution and some networks maintained higher baselines; throughout the brain deactivation state, the ASD participants had reduced deactivation in 12/14 networks. During default mode-negative CAPs, the ASD group showed simultaneous visual network and either dorsal attention or default mode network overactivation.

CONCLUSION

Nonsequential volume gathering into CAPs and the comparison of voxel-wise signal changes provide a complementary perspective to connectivity and an alternative to sliding window analysis.

Spectral analysis of physiological brain pulsations affecting the BOLD signal

Physiological pulsations have been shown to affect the global blood oxygen level dependent (BOLD) signal in human brain. While these pulsations have previously been regarded as noise, recent studies show their potential as biomarkers of brain pathology. We used the extended 5 Hz spectral range of magnetic resonance encephalography (MREG) data to investigate spatial and frequency distributions of physiological BOLD signal sources. Amplitude spectra of the global image signals revealed cardiorespiratory envelope modulation (CREM) peaks, in addition to the previously known very low frequency (VLF) and cardiorespiratory pulsations. We then proceeded to extend the amplitude of low frequency fluctuations (ALFF) method to each of these pulsations. The respiratory pulsations were spatially dominating over most brain structures. The VLF pulsations overcame the respiratory pulsations in frontal and parietal gray matter, whereas cardiac and CREM pulsations had this effect in central cerebrospinal fluid (CSF) spaces and major blood vessels. A quasi‐periodic pattern (QPP) analysis showed that the CREM pulsations propagated as waves, with a spatiotemporal pattern differing from that of respiratory pulsations, indicating them to be distinct intracranial physiological phenomenon. In conclusion, the respiration has a dominant effect on the global BOLD signal and directly modulates cardiovascular brain pulsations.

The variability of functional MRI brain signal increases in Alzheimer's disease at cardiorespiratory frequencies

Biomarkers sensitive to prodromal or early pathophysiological changes in Alzheimer's disease (AD) symptoms could improve disease detection and enable timely interventions. Changes in brain hemodynamics may be associated with the main clinical AD symptoms. To test this possibility, we measured the variability of blood oxygen level-dependent (BOLD) signal in individuals from three independent datasets (totaling 80 AD patients and 90 controls). We detected a replicable increase in brain BOLD signal variability in the AD populations, which constituted a robust biomarker for clearly differentiating AD cases from controls. Fast BOLD scans showed that the elevated BOLD signal variability in AD arises mainly from cardiovascular brain pulsations. Manifesting in abnormal cerebral perfusion and cerebrospinal fluid convection, present observation presents a mechanism explaining earlier observations of impaired glymphatic clearance associated with AD in humans.

Atypical Inter-Network Deactivation Associated With the Posterior Default-Mode Network in Autism Spectrum Disorder

Previous studies have suggested that atypical deactivation of functional brain networks contributes to the complex cognitive and behavioral profile associated with autism spectrum disorder (ASD). However, these studies have not considered the temporal dynamics of deactivation mechanisms between the networks. In this study, we examined (a) mutual deactivation and (b) mutual activation-deactivation (i.e., anticorrelated) time-lag patterns between resting-state networks (RSNs) in young adults with ASD (n = 20) and controls (n = 20) by applying the recently defined dynamic lag analysis (DLA) method, which measures time-lag variations peak-by-peak between the networks. In order to achieve temporally accurate lag patterns, the brain imaging data was acquired with a fast functional magnetic resonance imaging (fMRI) sequence (TR = 100 ms). Group-level independent component analysis was used to identify 16 RSNs for the DLA. We found altered mutual deactivation timings in ASD in (a) three of the deactivated and (b) two of the transiently anticorrelated (activated-deactivated) RSN pairs, which survived the strict threshold for significance of surrogate data. Of the significant RSN pairs, 80% included the posterior default-mode network (DMN). We propose that temporally altered deactivation mechanisms, including timings and directionality, between the posterior DMN and RSNs mediating processing of socially relevant information may contribute to the ASD phenotype. LAY SUMMARY: To understand autistic traits on a neural level, we examined temporal fluctuations in information flow between brain regions in young adults with autism spectrum disorder (ASD) and controls. We used a fast neuroimaging procedure to investigate deactivation mechanisms between brain regions. We found that timings and directionality of communication between certain brain regions were temporally altered in ASD, suggesting atypical deactivation mechanisms associated with the posterior default-mode network.

Fractional amplitude of low-frequency fluctuation (fALFF) alterations in young depressed patients with suicide attempts after cognitive behavioral therapy and antidepressant medication cotherapy: A resting-state fMRI study

BACKGROUND

Fractional amplitude of low-frequency fluctuation (fALFF) alterations in young depressed patients with suicide attempts after cognitive behavioral therapy (CBT) and antidepressant medication cotherapy were evaluated.

METHODS

Seventy-eight subjects (age: 18-28) were recruited from April 2017 to March 2019. Forty young depressed patients who attempted suicide were divided into CBT (8 weeks of structured CBT sessions and antidepressant medication cotherapy) and monotherapy (MG: antidepressant therapy alone) groups, and 38 healthy volunteers constituted a healthy control (HC) group. Resting-state functional magnetic resonance imaging (rs-fMRI) was conducted before and after treatment.

RESULTS

Before treatment, spontaneous brain activity in the left posterior cerebellar lobe (L-PCL), right anterior cingulate cortex, left caudate nucleus and left superior frontal cortex was higher in untreated patients than in HCs. After treatment, fALFF in the left middle occipital cortex and left precuneus was significantly increased in the CBT compared with the HC group. fALFF in the right middle frontal cortex, right inferior frontal cortex, l-PCL, and left anterior cerebellar lobe (L-ACL) were increased, while fALFF in the l-mPFC and l-SgACC were reduced, in the CBT compared with the MG group. Pearson correlation analyses provided information about clinical scale scores and mean fALFF relationships.

LIMITATIONS

There was insufficient evidence to confirm that these spontaneous brain activity alterations were the result of CBT or spontaneous recovery.

CONCLUSION

CBT and medication cotherapy can significantly change spontaneous activity in the left cerebellum and default-mode network, thereby regulating and reshaping emotional and cognitive processing.

Neurovisceral integration in the executive control network: A resting state analysis

Neurovisceral integration models emphasize the role of frontal lobes in cognitive, behavioral, and emotional regulation. Two candidate hubs for the regulation of cardio-autonomic control, anxiety, and executive attention are the dorsolateral prefrontal cortex (DLPFC) and middle frontal gyrus (MFG). Two-hundred and seventy-one adults (62.9 % female) aged 18-85 years were selected from the NKI-Rockland Sample. Resting state functional imaging data was preprocessed, and seeds extracted from bilateral DLPFC and MFG to test 4 regression models predicting connectivity with high frequency HRV (HF-HRV), trait anxiety (TA), and reaction time on an executive attention task. After controlling for age, sex, body mass index and head motion, the right DLPFC-MFG seed pair provided strongest support for neurovisceral integration indexed by HF-HRV, low TA and shorter reaction time on the attention network task. This hemispheric effect may underlie the inhibitory role of right PFC in the regulation of cardio-autonomic function, emotion, and executive attention.

15 Years MR-encephalography

Objective

This review article gives an account of the development of the MR-encephalography (MREG) method, which started as a mere ‘Gedankenexperiment’ in 2005 and gradually developed into a method for ultrafast measurement of physiological activities in the brain. After going through different approaches covering k-space with radial, rosette, and concentric shell trajectories we have settled on a stack-of-spiral trajectory, which allows full brain coverage with (nominal) 3 mm isotropic resolution in 100 ms. The very high acceleration factor is facilitated by the near-isotropic k-space coverage, which allows high acceleration in all three spatial dimensions.

Methods

The methodological section covers the basic sequence design as well as recent advances in image reconstruction including the targeted reconstruction, which allows real-time feedback applications, and—most recently—the time-domain principal component reconstruction (tPCR), which applies a principal component analysis of the acquired time domain data as a sparsifying transformation to improve reconstruction speed as well as quality.

Applications

Although the BOLD-response is rather slow, the high speed acquisition of MREG allows separation of BOLD-effects from cardiac and breathing related pulsatility. The increased sensitivity enables direct detection of the dynamic variability of resting state networks as well as localization of single interictal events in epilepsy patients. A separate and highly intriguing application is aimed at the investigation of the glymphatic system by assessment of the spatiotemporal patterns of cardiac and breathing related pulsatility.

Discussion

MREG has been developed to push the speed limits of fMRI. Compared to multiband-EPI this allows considerably faster acquisition at the cost of reduced image quality and spatial resolution.

Lag Analysis of Fast fMRI Reveals Delayed Information Flow Between the Default Mode and Other Networks in Narcolepsy

Narcolepsy is a chronic neurological disease characterized by dysfunction of the hypocretin system in brain causing disruption in the wake-promoting system. In addition to sleep attacks and cataplexy, patients with narcolepsy commonly report cognitive symptoms while objective deficits in sustained attention and executive function have been observed. Prior resting-state functional magnetic resonance imaging (fMRI) studies in narcolepsy have reported decreased inter/intranetwork connectivity regarding the default mode network (DMN). Recently developed fast fMRI data acquisition allows more precise detection of brain signal propagation with a novel dynamic lag analysis. In this study, we used fast fMRI data to analyze dynamics of inter resting-state network (RSN) information signaling between narcolepsy type 1 patients (NT1, n = 23) and age- and sex-matched healthy controls (HC, n = 23). We investigated dynamic connectivity properties between positive and negative peaks and, furthermore, their anticorrelative (pos-neg) counterparts. The lag distributions were significantly (P < 0.005, familywise error rate corrected) altered in 24 RSN pairs in NT1. The DMN was involved in 83% of the altered RSN pairs. We conclude that narcolepsy type 1 is characterized with delayed and monotonic inter-RSN information flow especially involving anticorrelations, which are known to be characteristic behavior of the DMN regarding neurocognition.

R-fMRI reconstruction from k-t undersampled data using a subject-invariant dictionary model and VB-EM with nested minorization

Higher spatial resolution in resting-state functional magnetic resonance imaging (R-fMRI) can give reliable information about the functional networks in the cerebral cortex. Typical methods can achieve higher spatial or temporal resolution by speeding up scans using either (i) complex pulse-sequence designs or (ii) k-space undersampling coupled with priors on the signal. We propose to undersample the R-fMRI acquisition in k-space and time to speedup scans in order to improve spatial resolution. We propose a novel model-based R-fMRI reconstruction framework using a robust, subject-invariant, spatially regularized dictionary prior on the signal. Furthermore, we propose a novel inference framework based on variational Bayesian expectation maximization with nested minorization (VB-EM-NM). Our inference framework allows us to provide an estimate of uncertainty of the reconstruction, unlike typical reconstruction methods. Empirical evaluation of (i) simulated R-fMRI reconstruction and (ii) functional-network estimates from brain R-fMRI reconstructions demonstrate that our framework improves over the state of the art, and, additionally, enables significantly higher spatial resolution.

Respiratory-related brain pulsations are increased in epilepsy-a two-centre functional MRI study

Resting-state functional MRI has shown potential for detecting changes in cerebral blood oxygen level-dependent signal in patients with epilepsy, even in the absence of epileptiform activity. Furthermore, it has been suggested that coefficient of variation mapping of fast functional MRI signal may provide a powerful tool for the identification of intrinsic brain pulsations in neurological diseases such as dementia, stroke and epilepsy. In this study, we used fast functional MRI sequence (magnetic resonance encephalography) to acquire ten whole-brain images per second. We used the functional MRI data to compare physiological brain pulsations between healthy controls (n = 102) and patients with epilepsy (n = 33) and furthermore to drug-naive seizure patients (n = 9). Analyses were performed by calculating coefficient of variation and spectral power in full band and filtered sub-bands. Brain pulsations in the respiratory-related frequency sub-band (0.11-0.51 Hz) were significantly (P < 0.05) increased in patients with epilepsy, with an increase in both signal variance and power. At the individual level, over 80% of medicated and drug-naive seizure patients exhibited areas of abnormal brain signal power that correlated well with the known clinical diagnosis, while none of the controls showed signs of abnormality with the same threshold. The differences were most apparent in the basal brain structures, respiratory centres of brain stem, midbrain and temporal lobes. Notably, full-band, very low frequency (0.01-0.1 Hz) and cardiovascular (0.8-1.76 Hz) brain pulses showed no differences between groups. This study extends and confirms our previous results of abnormal fast functional MRI signal variance in epilepsy patients. Only respiratory-related brain pulsations were clearly increased with no changes in either physiological cardiorespiratory rates or head motion between the subjects. The regional alterations in brain pulsations suggest that mechanisms driving the cerebrospinal fluid homeostasis may be altered in epilepsy. Magnetic resonance encephalography has both increased sensitivity and high specificity for detecting the increased brain pulsations, particularly in times when other tools for locating epileptogenic areas remain inconclusive.

Resting-state networks of the neonate brain identified using independent component analysis

Resting-state functional magnetic resonance imaging (rs-fMRI) has been successfully used to probe the intrinsic functional organization of the brain and to study brain development. Here, we implemented a combination of individual and group independent component analysis (ICA) of FSL on a 6-min resting-state data set acquired from 21 naturally sleeping term-born (age 26 ± 6.7 d), healthy neonates to investigate the emerging functional resting-state networks (RSNs). In line with the previous literature, we found evidence of sensorimotor, auditory/language, visual, cerebellar, thalmic, parietal, prefrontal, anterior cingulate as well as dorsal and ventral aspects of the default-mode-network. Additionally, we identified RSNs in frontal, parietal, and temporal regions that have not been previously described in this age group and correspond to the canonical RSNs established in adults. Importantly, we found that careful ICA-based denoising of fMRI data increased the number of networks identified with group-ICA, whereas the degree of spatial smoothing did not change the number of identified networks. Our results show that the infant brain has an established set of RSNs soon after birth.

Current Challenges in Translational and Clinical fMRI and Future Directions

Translational neuroscience is an important field that brings together clinical praxis with neuroscience methods. In this review article, the focus will be on functional neuroimaging (fMRI) and its applicability in clinical fMRI studies. In the light of the "replication crisis," three aspects will be critically discussed: First, the fMRI signal itself, second, current fMRI praxis, and, third, the next generation of analysis strategies. Current attempts such as resting-state fMRI, meta-analyses, and machine learning will be discussed with their advantages and potential pitfalls and disadvantages. One major concern is that the fMRI signal shows substantial within- and between-subject variability, which affects the reliability of both task-related, but in particularly resting-state fMRI studies. Furthermore, the lack of standardized acquisition and analysis methods hinders the further development of clinical relevant approaches. However, meta-analyses and machine-learning approaches may help to overcome current shortcomings in the methods by identifying new, and yet hidden relationships, and may help to build new models on disorder mechanisms. Furthermore, better control of parameters that may have an influence on the fMRI signal and that can easily be controlled for, like blood pressure, heart rate, diet, time of day, might improve reliability substantially.

Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder

This study investigated whole‐brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA). The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. Sixteen resting‐state networks (RSNs) with the highest spatial correlation between NT individuals (n = 20) and individuals with ASD (n = 20) were analyzed. The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant (P‐value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding. Alterations in lag patterns were concentrated in salience, executive, visual, and default‐mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large‐scale functional brain networks may contribute to the ASD phenotype. Autism Res 2020, 13: 244–258. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.

Lay Summary

Autism spectrum disorder (ASD) is characterized by atypical neurodevelopment. Using an ultra‐fast neuroimaging procedure, we investigated communication across brain regions in adults with ASD compared with neurotypical (NT) individuals. We found that ASD individuals had altered information flow patterns across brain regions. Atypical patterns were concentrated in salience, executive, visual, and default‐mode network areas of the brain that have previously been implicated in the pathophysiology of the disorder.