Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response

Delta Wave MRI: fMRI Imaging of Electrophysiologic Activity

We propose a novel application of MR encephalography (MREG) to detect the frequency spectrum of endogenous slow oscillatory brain activity (delta, <4 Hz). MREG offers faster image acquisition than conventional fMRI and superior spatial localization than electroencephalography/magnetoencephalography. MREG was acquired at a 0.1-second temporal resolution in a healthy adult during interleaved wakefulness and sleep to demonstrate its capability for detecting delta-band power changes associated with sleep, previously demonstrated by electroencephalography. For each voxel, the brain activity of MREG signal was used to compute a spectrogram and a whole-brain image of delta-band spectral power. The delta-band power was observed to increase during sleep compared with the awake states using measures from MREG voxelwise spectrograms and sequential whole-brain spatial maps of slow-wave power. This work introduces an MR technique for measuring brain slow-wave activity that is sensitive to changes in the magnitude and frequency of brain activity in sleep.

Healthy aging delays and dedifferentiates high-level visual representations

Healthy aging impacts visual information processing with consequences for subsequent high-level cognition and everyday behavior, but the underlying neural changes in visual representations remain unknown. Here, we investigate the nature of representations underlying object recognition in older compared to younger adults by tracking them in time using electroencephalography (EEG), across space using functional magnetic resonance imaging (fMRI), and by probing their behavioral relevance using similarity judgments. Applying a multivariate analysis framework to combine experimental assessments, four key findings about how brain aging impacts object recognition emerge. First, aging selectively delays the formation of object representations, profoundly changing the chronometry of visual processing. Second, the delay in the formation of object representations emerges in high-level rather than low- and mid-level ventral visual cortex, supporting the theory that brain areas developing last deteriorate first. Third, aging reduces content selectivity in the high-level ventral visual cortex, indicating age-related neural dedifferentiation as the mechanism of representational change. Finally, we demonstrate that the identified representations of the aging brain are behaviorally relevant, ascertaining ecological relevance. Together, our results reveal the impact of healthy aging on the visual brain.

Towards whole brain mapping of the haemodynamic response function

Functional magnetic resonance imaging time-series are conventionally processed by linear modelling the evoked response as the convolution of the experimental conditions with a stereotyped haemodynamic response function (HRF). However, the neural signal in response to a stimulus can vary according to task, brain region, and subject-specific conditions. Moreover, HRF shape has been suggested to carry physiological information. The BOLD signal across a range of sensorial and cognitive tasks was fitted using a sine series expansion, and modelled signals were deconvolved, thus giving rise to a task-specific deconvolved HRF (dHRF), which was characterized in terms of amplitude, latency, time-to-peak and full-width at half maximum for each task. We found that the BOLD response shape changes not only across activated regions and tasks, but also across subjects despite the age homogeneity of the cohort. Largest variabilities were observed in mean amplitude and latency across tasks and regions, while time-to-peak and full width at half maximum were relatively more consistent. Additionally, the dHRF was found to deviate from canonicity in several brain regions. Our results suggest that the choice of a standard, uniform HRF may be not optimal for all fMRI analyses and may lead to model misspecifications and statistical bias.

Ultrafast fMRI reveals serial queuing of information processing during multitasking in the human brain

The human brain is heralded for its massive parallel processing capacity, yet influential cognitive models suggest that there is a central bottleneck of information processing distinct from perceptual and motor stages that limits our ability to carry out two cognitively demanding tasks at once, resulting in the serial queuing of task information processing. Here we used ultrafast (199 ms TR), high-field (7T) fMRI with multivariate analyses to distinguish brain activity between two arbitrary sensorimotor response selection tasks when the tasks were temporally overlapping. We observed serial processing of task-specific activity in the fronto-parietal multiple-demand (MD) network, while processing in earlier sensory stages unfolded largely in parallel. Moreover, the MD network combined with modality-specific motor areas to define the functional characteristic of the central bottleneck at the stage of response selection. These results provide direct neural evidence for serial queuing of information processing and pinpoint the neural substrates undergirding the central bottleneck.

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.

Multi-study fMRI outlooks on subcortical BOLD responses in the stop-signal paradigm

This study investigates the functional network underlying response inhibition in the human brain, particularly the role of the basal ganglia in successful action cancellation. Functional magnetic resonance imaging (fMRI) approaches have frequently used the stop-signal task to examine this network. We merge five such datasets, using a novel aggregatory method allowing the unification of raw fMRI data across sites. This meta-analysis, along with other recent aggregatory fMRI studies, does not find evidence for the innervation of the hyperdirect or indirect cortico-basal-ganglia pathways in successful response inhibition. What we do find, is large subcortical activity profiles for failed stop trials. We discuss possible explanations for the mismatch of findings between the fMRI results presented here and results from other research modalities that have implicated nodes of the basal ganglia in successful inhibition. We also highlight the substantial effect smoothing can have on the conclusions drawn from task-specific general linear models. First and foremost, this study presents a proof of concept for meta-analytical methods that enable the merging of extensive, unprocessed, or unreduced datasets. It demonstrates the significant potential that open-access data sharing can offer to the research community. With an increasing number of datasets being shared publicly, researchers will have the ability to conduct meta-analyses on more than just summary data.

Trimodal brain imaging: A novel approach for simultaneous investigation of human brain function

While advancements have improved the extent to which individual brain imaging approaches capture information regarding spatial or temporal dynamics of brain activity, the connections between these aspects and their relation to psychological functioning remain only partially understood. Acquisition and integration across multiple brain imaging modalities allows for the possible clarification of these connections. The present review provides an overview of three complementary modalities - functional magnetic resonance imaging (fMRI), electroencephalography/event-related potentials (EEG/ERP), and event-related optical signals (EROS) - and discusses progress and considerations for each modality, along with a summary of a novel protocol for acquiring them simultaneously. Initial evidence points to the feasibility of acquiring and integrating multiple measures of brain function that allows for addressing questions in ways not otherwise possible using traditional approaches. Simultaneous trimodal brain imaging in humans provides new possibilities for clarifying spatiotemporal dynamics of brain activity and for identifying multifaceted associations with measures of individual differences and important health outcomes.

Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates

While neurofeedback represents a promising tool for neuroscience and a brain self-regulation approach to psychological rehabilitation, the field faces several problems and challenges. Current research has shown great variability and even failure among human participants in learning to self-regulate target features of brain activity with neurofeedback. A better understanding of cognitive mechanisms, psychological factors and neural substrates underlying self-regulation might help improve neurofeedback's scientific and clinical practices. This article reviews the current understanding of the neural mechanisms of brain self-regulation by drawing on findings from human and animal studies in neurofeedback, brain-computer/machine interfaces and neuroprosthetics. In this article, we look closer at the following topics: cognitive processes and psychophysiological factors affecting self-regulation, theoretical models and neural substrates underlying self-regulation, and finally, we provide an outlook on the outstanding gaps in knowledge and technical challenges. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

SAD: semi-supervised automatic detection of BOLD activations in high temporal resolution fMRI data

OBJECTIVE

Despite the prevalent use of the general linear model (GLM) in fMRI data analysis, assuming a pre-defined hemodynamic response function (HRF) for all voxels can lead to reduced reliability and may distort the inferences derived from it. To overcome the necessity of presuming a specific model for the hemodynamic response, we introduce a semi-supervised automatic detection (SAD) method.

MATERIALS AND METHODS

The proposed SAD method employs a Bi-LSTM neural network to classify high temporal resolution fMRI data. Network training utilized an fMRI dataset with 75-ms temporal resolution in an iterative scheme. Classification performance was evaluated on a second fMRI dataset from the same participant, collected on a different day. Comparative analysis with the standard GLM approach was conducted to evaluate the cooperative effectiveness of the SAD method.

RESULTS

The SAD method performed well based on the classification scores: true-positive rate = 0.961, area under the receiver operating curve = 0.998, true-negative rate = 0.99, F1-score = 0.979, False-negative rate = 0.038, false-discovery rate = 0.002, false-positive rate = 0.002 at 75-ms temporal resolution.

CONCLUSION

SAD can detect hemodynamic responses at 75-ms temporal resolution without relying on a specific shape of an HRF. Future work could expand the use cases to include more participants and different fMRI paradigms.

Brainwide hemodynamics predict neural rhythms across sleep and wakefulness in humans

The brain exhibits rich oscillatory dynamics that play critical roles in vigilance and cognition, such as the neural rhythms that define sleep. These rhythms continuously fluctuate, signaling major changes in vigilance, but the brainwide dynamics underlying these oscillations are unknown. Using simultaneous EEG and fast fMRI in humans drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI networks predict alpha (8-12 Hz) and delta (1-4 Hz) fluctuations. We predicted moment-to-moment EEG power variations from fMRI activity in held-out subjects, and found that information about alpha rhythms was highly separable in two networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify the large-scale network patterns that underlie alpha and delta rhythms, and establish a novel framework for investigating multimodal, brainwide dynamics.

Fast 3D fMRI acquisition with high spatial resolutions over a reduced FOV

PURPOSE

To develop and demonstrate a fast 3D fMRI acquisition technique with high spatial resolution over a reduced FOV, named k-t 3D reduced FOV imaging (3D-rFOVI).

METHODS

Based on 3D gradient-echo EPI, k-t 3D-rFOVI used a 2D RF pulse to reduce the FOV in the in-plane phase-encoding direction, boosting spatial resolution without increasing echo train length. For image acceleration, full sampling was applied in the central k-space region along the through-slab direction (kz) for all time frames, while randomized undersampling was used in outer kz regions at different time frames. Images were acquired at 3T and reconstructed using a method based on partial separability. fMRI detection sensitivity of k-t 3D-rFOVI was quantitively analyzed with simulation data. Human visual fMRI experiments were performed to evaluate k-t 3D-rFOVI and compare it with a commercial multiband EPI sequence.

RESULTS

The simulation data showed that k-t 3D-rFOVI can detect 100% of fMRI activations with an acceleration factor (R) of 2 and ˜80% with R = 6. In the human fMRI data acquired with 1.5-mm spatial resolution and 800-ms volume TR (TRvol), k-t 3D-rFOVI with R = 4 detected 46% more activated voxels in the visual cortex than the multiband EPI. Additional fMRI experiments showed that k-t 3D-rFOVI can achieve TRvol of 480 ms with R = 6, while reliably detecting visual activation.

CONCLUSIONS

k-t 3D-rFOVI can simultaneously achieve a high spatial resolution (1.5-mm isotropically) and short TRvol (480-ms) at 3T. It offers a robust acquisition technique for fast fMRI studies over a focused brain volume.

The temporal specificity of BOLD fMRI is systematically related to anatomical and vascular features of the human brain

The ability to detect fast responses with functional MRI depends on the speed of hemodynamic responses to neural activity, because hemodynamic responses act as a temporal low-pass filter which blurs rapid changes. However, the shape and timing of hemodynamic responses are highly variable across the brain and across stimuli. This heterogeneity of responses implies that the temporal specificity of fMRI signals, or the ability of fMRI to preserve fast information, could also vary substantially across the cortex. In this work we investigated how local differences in hemodynamic response timing affect the temporal specificity of fMRI. We used ultra-high field (7T) fMRI at high spatiotemporal resolution, studying the primary visual cortex (V1) as a model area for investigation. We used visual stimuli oscillating at slow and fast frequencies to probe the temporal specificity of individual voxels. As expected, we identified substantial variability in temporal specificity, with some voxels preserving their responses to fast neural activity more effectively than others. We investigated which voxels had the highest temporal specificity, and tested whether voxel timing was related to anatomical and vascular features. We found that low temporal specificity is only weakly explained by the presence of large veins or cerebral cortical depth. Notably, however, temporal specificity depended strongly on a voxel's position along the anterior-posterior anatomical axis of V1, with voxels within the calcarine sulcus being capable of preserving close to 25% of their amplitude as the frequency of stimulation increased from 0.05Hz to 0.20Hz, and voxels nearest to the occipital pole preserving less than 18%. These results indicate that detection biases in high-resolution fMRI will depend on the anatomical and vascular features of the area being imaged, and that these biases will differ depending on the timing of the underlying neuronal activity. While we attribute this variance primarily to hemodynamic effects, neuronal nonlinearities may also influence response timing. Importantly, this spatial heterogeneity of temporal specificity suggests that it could be exploited to achieve higher specificity in some locations, and that tailored data analysis strategies may help improve the detection and interpretation of fast fMRI responses.

Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging - A Symposium Review

Advances in the spatiotemporal resolution and field-of-view of neuroimaging tools are driving mesoscale studies for translational neuroscience. On October 10, 2023, the Center for Mesoscale Mapping (CMM) at the Massachusetts General Hospital (MGH) Athinoula A. Martinos Center for Biomedical Imaging and the Massachusetts Institute of Technology (MIT) Health Sciences Technology based Neuroimaging Training Program (NTP) hosted a symposium exploring the state-of-the-art in this rapidly growing area of research. "Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging" brought together researchers who use a broad range of imaging techniques to study brain structure and function at the convergence of the microscopic and macroscopic scales. The day-long event centered on areas in which the CMM has established expertise, including the development of emerging technologies and their application to clinical translational needs and basic neuroscience questions. The in-person symposium welcomed more than 150 attendees, including 57 faculty members, 61 postdoctoral fellows, 35 students, and four industry professionals, who represented institutions at the local, regional, and international levels. The symposium also served the training goals of both the CMM and the NTP. The event content, organization, and format were planned collaboratively by the faculty and trainees. Many CMM faculty presented or participated in a panel discussion, thus contributing to the dissemination of both the technologies they have developed under the auspices of the CMM and the findings they have obtained using those technologies. NTP trainees who benefited from the symposium included those who helped to organize the symposium and/or presented posters and gave "flash" oral presentations. In addition to gaining experience from presenting their work, they had opportunities throughout the day to engage in one-on-one discussions with visiting scientists and other faculty, potentially opening the door to future collaborations. The symposium presentations provided a deep exploration of the many technological advances enabling progress in structural and functional mesoscale brain imaging. Finally, students worked closely with the presenting faculty to develop this report summarizing the content of the symposium and putting it in the broader context of the current state of the field to share with the scientific community. We note that the references cited here include conference abstracts corresponding to the symposium poster presentations.

Spatiotemporal relationships between neuronal, metabolic, and hemodynamic signals in the awake and anesthetized mouse brain

Neurovascular coupling (NVC) and neurometabolic coupling (NMC) provide the basis for functional magnetic resonance imaging and positron emission tomography to map brain neurophysiology. While increases in neuronal activity are often accompanied by increases in blood oxygen delivery and oxidative metabolism, these observations are not the rule. This decoupling is important when interpreting brain network organization (e.g., resting-state functional connectivity [RSFC]) because it is unclear whether changes in NMC/NVC affect RSFC measures. We leverage wide-field optical imaging in Thy1-jRGECO1a mice to map cortical calcium activity in pyramidal neurons, flavoprotein autofluorescence (representing oxidative metabolism), and hemodynamic activity during wake and ketamine/xylazine anesthesia. Spontaneous dynamics of all contrasts exhibit patterns consistent with RSFC. NMC/NVC relative to excitatory activity varies over the cortex. Ketamine/xylazine profoundly alters NVC but not NMC. Compared to awake RSFC, ketamine/xylazine affects metabolic-based connectomes moreso than hemodynamic-based measures of RSFC. Anesthesia-related differences in NMC/NVC timing do not appreciably alter RSFC structure.

A Randomization-Based, Model-Free Approach to Functional Neuroimaging: A Proof of Concept

Functional neuroimaging analysis takes noisy multidimensional measurements as input and produces statistical inferences regarding the functional properties of brain regions as output. Such inferences are most commonly model-based, in that they assume a model of how neural activity translates to the measured signal (blood oxygenation level-dependent signal in the case of functional MRI). The use of models increases statistical sensitivity and makes it possible to ask fine-grained theoretical questions. However, this comes at the cost of making theoretical assumptions about the underlying data-generating process. An advantage of model-free approaches is that they can be used in cases where model assumptions are known not to hold. To this end, we introduce a randomization-based, model-free approach to functional neuroimaging. TWISTER randomization makes it possible to infer functional selectivity from correlations between experimental runs. We provide a proof of concept in the form of a visuomotor mapping experiment and discuss the possible strengths and limitations of this new approach in light of our empirical results.

Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields

When multiple visual stimuli are presented simultaneously in the receptive field, the neural response is suppressed compared to presenting the same stimuli sequentially. The prevailing hypothesis suggests that this suppression is due to competition among multiple stimuli for limited resources within receptive fields, governed by task demands. However, it is unknown how stimulus-driven computations may give rise to simultaneous suppression. Using fMRI, we find simultaneous suppression in single voxels, which varies with both stimulus size and timing, and progressively increases up the visual hierarchy. Using population receptive field (pRF) models, we find that compressive spatiotemporal summation rather than compressive spatial summation predicts simultaneous suppression, and that increased simultaneous suppression is linked to larger pRF sizes and stronger compressive nonlinearities. These results necessitate a rethinking of simultaneous suppression as the outcome of stimulus-driven compressive spatiotemporal computations within pRFs, and open new opportunities to study visual processing capacity across space and time.

Functional ultrasound imaging and neuronal activity: how accurate is the spatiotemporal match?

Over the last decade, functional ultrasound (fUS) has risen as a critical tool in functional neuroimaging, leveraging hemodynamic changes to infer neural activity indirectly. Recent studies have established a strong correlation between neural spike rates (SR) and functional ultrasound signals. However, understanding their spatial distribution and variability across different brain areas is required to thoroughly interpret fUS signals. In this regard, we conducted simultaneous fUS imaging and Neuropixels recordings during stimulus-evoked activity in awake mice within three regions the visual pathway. Our findings indicate that the temporal dynamics of fUS and SR signals are linearly correlated, though the correlation coefficients vary among visual regions. Conversely, the spatial correlation between the two signals remains consistent across all regions with a spread of approximately 300 micrometers. Finally, we introduce a model that integrates the spatial and temporal components of the fUS signal, allowing for a more accurate interpretation of fUS images.

High Spatiotemporal Resolution Radial Encoding Single-Vessel fMRI

High-field preclinical functional MRI (fMRI) is enabled the high spatial resolution mapping of vessel-specific hemodynamic responses, that is single-vessel fMRI. In contrast to investigating the neuronal sources of the fMRI signal, single-vessel fMRI focuses on elucidating its vascular origin, which can be readily implemented to identify vascular changes relevant to vascular dementia or cognitive impairment. However, the limited spatial and temporal resolution of fMRI is hindered hemodynamic mapping of intracortical microvessels. Here, the radial encoding MRI scheme is implemented to measure BOLD signals of individual vessels penetrating the rat somatosensory cortex. Radial encoding MRI is employed to map cortical activation with a focal field of view (FOV), allowing vessel-specific functional mapping with 50 × 50 µm2 in-plane resolution at a 1 to 2 Hz sampling rate. Besides detecting refined hemodynamic responses of intracortical micro-venules, the radial encoding-based single-vessel fMRI enables the distinction of fMRI signals from vessel and peri-vessel voxels due to the different contribution of intravascular and extravascular effects.

Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields

When multiple visual stimuli are presented simultaneously in the receptive field, the neural response is suppressed compared to presenting the same stimuli sequentially. The prevailing hypothesis suggests that this suppression is due to competition among multiple stimuli for limited resources within receptive fields, governed by task demands. However, it is unknown how stimulus-driven computations may give rise to simultaneous suppression. Using fMRI, we find simultaneous suppression in single voxels, which varies with both stimulus size and timing, and progressively increases up the visual hierarchy. Using population receptive field (pRF) models, we find that compressive spatiotemporal summation rather than compressive spatial summation predicts simultaneous suppression, and that increased simultaneous suppression is linked to larger pRF sizes and stronger compressive nonlinearities. These results necessitate a rethinking of simultaneous suppression as the outcome of stimulus-driven compressive spatiotemporal computations within pRFs, and open new opportunities to study visual processing capacity across space and time.

Natural Fat Nanoemulsions for Enhanced Optical Coherence Tomography Neuroimaging and Tumor Imaging in the Second Near-Infrared Window

Optical coherence tomography (OCT) imaging mainly uses backscattered light to visualize the structural and functional information on biological tissues. In particular, OCT angiography can not only map the capillary networks but also capture the blood flow in the tissue microenvironment, making it a good candidate for neuroimaging and tumor imaging in vivo and in real time. To further improve the detection accuracy of cancer or brain disorders, it is essential to develop a natural and nontoxic contrast agent for enhanced OCT imaging in the second near-infrared (NIR-II) window. In this study, a superior biocompatible and highly scattering NIR-II fat nanoemulsion was constructed to improve OCT imaging contrast and depth for monitoring the vascular network changes of the cerebral cortex or tumor. In vivo experimental results demonstrated that a natural fat nanoemulsion can serve as an excellent probe for enhanced OCT neuroimaging and tumor imaging.

Decoding the dynamic perception of risk and speed using naturalistic stimuli: A multivariate, whole-brain analysis

Time-resolved decoding of speed and risk perception in car driving is important for understanding the perceptual processes related to driving safety. In this study, we used an fMRI-compatible trackball with naturalistic stimuli to record dynamic ratings of perceived risk and speed and investigated the degree to which different brain regions were able to decode these. We presented participants with first-person perspective videos of cars racing on the same course. These videos varied in terms of subjectively perceived speed and risk profiles, as determined during a behavioral pilot. During the fMRI experiment, participants used the trackball to dynamically rate subjective risk in a first and speed in a second session and assessed overall risk and speed after watching each video. A standard multivariate correlation analysis based on these ratings revealed sparse decodability in visual areas only for the risk ratings. In contrast, the dynamic rating-based correlation analysis uncovered frontal, visual, and temporal region activation for subjective risk and dorsal visual stream and temporal region activation for subjectively perceived speed. Interestingly, further analyses showed that the brain regions for decoding risk changed over time, whereas those for decoding speed remained constant. Overall, our results demonstrate the advantages of time-resolved decoding to help our understanding of the dynamic networks associated with decoding risk and speed perception in realistic driving scenarios.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Whisker-evoked neurovascular coupling is preserved during hypoglycemia in mouse cortical arterioles and capillaries

Hypoglycemia is a serious complication of insulin treatment of diabetes that can lead to coma and death. Neurovascular coupling, which mediates increased local blood flow in response to neuronal activity, increases glucose availability to active neurons. This mechanism could be essential for neuronal health during hypoglycemia, when total glucose supplies are low. Previous studies suggest, however, that neurovascular coupling (a transient blood flow increase in response to an increase in neuronal activity) may be reduced during hypoglycemia. Such a reduction in blood flow increase would exacerbate the effects of hypoglycemia, depriving active neurons of glucose. We have reexamined the effects of hypoglycemia on neurovascular coupling by simultaneously monitoring neuronal and vascular responses to whisker stimulation in the awake mouse somatosensory cortex. We find that neurovascular coupling at both penetrating arterioles and at 2nd order capillaries did not change significantly during insulin-induced hypoglycemia compared to euglycemia. In addition, we show that the basal diameter of both arterioles and capillaries increases during hypoglycemia (10.3 and 9.7% increases, respectively). Our results demonstrate that both neurovascular coupling and basal increases in vessel diameter are active mechanisms which help to maintain an adequate supply of glucose to the brain during hypoglycemia.

Characterizing Spatiotemporal Population Receptive Fields in Human Visual Cortex with fMRI

The use of fMRI and computational modeling has advanced understanding of spatial characteristics of population receptive fields (pRFs) in human visual cortex. However, we know relatively little about the spatiotemporal characteristics of pRFs because neurons' temporal properties are one to two orders of magnitude faster than fMRI BOLD responses. Here, we developed an image-computable framework to estimate spatiotemporal pRFs from fMRI data. First, we developed a simulation software that predicts fMRI responses to a time-varying visual input given a spatiotemporal pRF model and solves the model parameters. The simulator revealed that ground-truth spatiotemporal parameters can be accurately recovered at the millisecond resolution from synthesized fMRI responses. Then, using fMRI and a novel stimulus paradigm, we mapped spatiotemporal pRFs in individual voxels across human visual cortex in 10 participants (both females and males). We find that a compressive spatiotemporal (CST) pRF model better explains fMRI responses than a conventional spatial pRF model across visual areas spanning the dorsal, lateral, and ventral streams. Further, we find three organizational principles of spatiotemporal pRFs: (1) from early to later areas within a visual stream, spatial and temporal windows of pRFs progressively increase in size and show greater compressive nonlinearities, (2) later visual areas show diverging spatial and temporal windows across streams, and (3) within early visual areas (V1-V3), both spatial and temporal windows systematically increase with eccentricity. Together, this computational framework and empirical results open exciting new possibilities for modeling and measuring fine-grained spatiotemporal dynamics of neural responses using fMRI.

Whole-brain multivariate hemodynamic deconvolution for functional MRI with stability selection

Conventionally, analysis of functional MRI (fMRI) data relies on available information about the experimental paradigm to establish hypothesized models of brain activity. However, this information can be inaccurate, incomplete or unavailable in multiple scenarios such as resting-state, naturalistic paradigms or clinical conditions. In these cases, blind estimates of neuronal-related activity can be obtained with paradigm-free analysis methods such as hemodynamic deconvolution. Yet, current formulations of the hemodynamic deconvolution problem have three important limitations: (1) their efficacy strongly depends on the appropriate selection of regularization parameters, (2) being univariate, they do not take advantage of the information present across the brain, and (3) they do not provide any measure of statistical certainty associated with each detected event. Here we propose a novel approach that addresses all these limitations. Specifically, we introduce multivariate sparse paradigm free mapping (Mv-SPFM), a novel hemodynamic deconvolution algorithm that operates at the whole brain level and adds spatial information via a mixed-norm regularization term over all voxels. Additionally, Mv-SPFM employs a stability selection procedure that removes the need to select regularization parameters and also lets us obtain an estimate of the true probability of having a neuronal-related BOLD event at each voxel and time-point based on the area under the curve (AUC) of the stability paths. Besides, we present a formulation tailored for multi-echo fMRI acquisitions (MvME-SPFM), which allows us to better isolate fluctuations of BOLD origin on the basis of their linear dependence with the echo time (TE) and to assign physiologically interpretable units (i.e., changes in the apparent transverse relaxation ΔR2∗) to the resulting deconvolved events. Remarkably, we demonstrate that Mv-SPFM achieves comparable performance even when using a single-echo formulation. We demonstrate that this algorithm outperforms existing state-of-the-art deconvolution approaches, and shows higher spatial and temporal agreement with the activation maps and BOLD signals obtained with a standard model-based linear regression approach, even at the level of individual neuronal events. Furthermore, we show that by employing stability selection, the performance of the algorithm depends less on the selection of temporal and spatial regularization parameters λ and ρ. Consequently, the proposed algorithm provides more reliable estimates of neuronal-related activity, here in terms of ΔR2∗, for the study of the dynamics of brain activity when no information about the timings of the BOLD events is available. This algorithm will be made publicly available as part of the splora Python package.

Neural Circuitry-Related Biomarkers for Drug Development in Psychiatry: An Industry Perspective

Drug development in psychiatry has been hampered by the lack of reliable ways to determine the neurobiological effects of the assets tested, difficulties in identifying patient subsets more amenable to benefit from a given asset, and issues with executing trials in a manner that would convincingly provide answers. An emerging idea in many companies is to validate tools to address changes in neural circuits by pharmacological tools as a key piece in quantifying the effects of our drugs. Here, we review past, present, and emerging approaches to capture the outcome of the modulation of brain circuits. The field is now ripe for implementing these approaches in drug development.

Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI

Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.

Neural signatures of emotional inference and experience align during social consensus

Humans seamlessly transform dynamic social signals into inferences about the internal states of the people around them. To understand the neural processes that sustain this transformation, we collected fMRI data from participants (N = 100) while they rated the emotional intensity of people (targets) describing significant life events. Targets rated themselves on the same scale to indicate the intended "ground truth" emotional intensity of their videos. Next, we developed two multivariate models of observer brain activity- the first predicted the "ground truth" (r = 0.50, p < 0.0001) and the second predicted observer inferences (r = 0.53, p < 0.0001). When individuals make more accurate inferences, there is greater moment-by-moment concordance between these two models, suggesting that an observer's brain activity contains latent representations of other people's emotional states. Using naturalistic socioemotional stimuli and machine learning, we developed reliable brain signatures that predict what an observer thinks about a target, what the target thinks about themselves, and the correspondence between them. These signatures can be applied in clinical data to better our understanding of socioemotional dysfunction.

A selection and targeting framework of cortical locations for line-scanning fMRI

Depth-resolved functional magnetic resonance imaging (fMRI) is an emerging field growing in popularity given the potential of separating signals from different computational processes in cerebral cortex. Conventional acquisition schemes suffer from low spatial and temporal resolutions. Line-scanning methods allow depth-resolved fMRI by sacrificing spatial coverage to sample blood oxygenated level-dependent (BOLD) responses at ultra-high temporal and spatial resolution. For neuroscience applications, it is critical to be able to place the line accurately to (1) sample the right neural population and (2) target that neural population with tailored stimuli or tasks. To this end, we devised a multi-session framework where a target cortical location is selected based on anatomical and functional properties. The line is then positioned according to this information in a separate second session, and we tailor the experiment to focus on the target location. Anatomically, the precision of the line placement was confirmed by projecting a nominal representation of the acquired line back onto the surface. Functional estimates of neural selectivities in the line, as quantified by a visual population-receptive field model, resembled the target selectivities well for most subjects. This functional precision was quantified in detail by estimating the distance between the visual field location of the targeted vertex and the location in visual cortex (V1) that most closely resembled the line-scanning estimates; this distance was on average ~5.5 mm. Given the dimensions of the line, differences in acquisition, session, and stimulus design, this validates that line-scanning can be used to probe local neural sensitivities across sessions. In summary, we present an accurate framework for line-scanning MRI; we believe such a framework is required to harness the full potential of line-scanning and maximize its utility. Furthermore, this approach bridges canonical fMRI experiments with electrophysiological experiments, which in turn allows novel avenues for studying human physiology non-invasively.

Functional neuroimaging as a catalyst for integrated neuroscience

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

Wireless agents for brain recording and stimulation modalities

New sensors and modulators that interact wirelessly with medical modalities unlock uncharted avenues for in situ brain recording and stimulation. Ongoing miniaturization, material refinement, and sensitization to specific neurophysiological and neurochemical processes are spurring new capabilities that begin to transcend the constraints of traditional bulky and invasive wired probes. Here we survey current state-of-the-art agents across diverse realms of operation and evaluate possibilities depending on size, delivery, specificity and spatiotemporal resolution. We begin by describing implantable and injectable micro- and nano-scale electronic devices operating at or below the radio frequency (RF) regime with simple near field transmission, and continue with more sophisticated devices, nanoparticles and biochemical molecular conjugates acting as dynamic contrast agents in magnetic resonance imaging (MRI), ultrasound (US) transduction and other functional tomographic modalities. We assess the ability of some of these technologies to deliver stimulation and neuromodulation with emerging probes and materials that provide minimally invasive magnetic, electrical, thermal and optogenetic stimulation. These methodologies are transforming the repertoire of readily available technologies paired with compatible imaging systems and hold promise toward broadening the expanse of neurological and neuroscientific diagnostics and therapeutics.

Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here, we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

The impact of the human thalamus on brain-wide information processing

The thalamus is a small, bilateral structure in the diencephalon that integrates signals from many areas of the CNS. This critical anatomical position allows the thalamus to influence whole-brain activity and adaptive behaviour. However, traditional research paradigms have struggled to attribute specific functions to the thalamus, and it has remained understudied in the human neuroimaging literature. Recent advances in analytical techniques and increased accessibility to large, high-quality data sets have brought forth a series of studies and findings that (re-)establish the thalamus as a core region of interest in human cognitive neuroscience, a field that otherwise remains cortico-centric. In this Perspective, we argue that using whole-brain neuroimaging approaches to investigate the thalamus and its interaction with the rest of the brain is key for understanding systems-level control of information processing. To this end, we highlight the role of the thalamus in shaping a range of functional signatures, including evoked activity, interregional connectivity, network topology and neuronal variability, both at rest and during the performance of cognitive tasks.

Characterizing spatiotemporal population receptive fields in human visual cortex with fMRI

The use of fMRI and computational modeling has advanced understanding of spatial characteristics of population receptive fields (pRFs) in human visual cortex. However, we know relatively little about the spatiotemporal characteristics of pRFs because neurons' temporal properties are one to two orders of magnitude faster than fMRI BOLD responses. Here, we developed an image-computable framework to estimate spatiotemporal pRFs from fMRI data. First, we developed a simulation software that predicts fMRI responses to a time varying visual input given a spatiotemporal pRF model and solves the model parameters. The simulator revealed that ground-truth spatiotemporal parameters can be accurately recovered at the millisecond resolution from synthesized fMRI responses. Then, using fMRI and a novel stimulus paradigm, we mapped spatiotemporal pRFs in individual voxels across human visual cortex in 10 participants. We find that a compressive spatiotemporal (CST) pRF model better explains fMRI responses than a conventional spatial pRF model across visual areas spanning the dorsal, lateral, and ventral streams. Further, we find three organizational principles of spatiotemporal pRFs: (i) from early to later areas within a visual stream, spatial and temporal integration windows of pRFs progressively increase in size and show greater compressive nonlinearities, (ii) later visual areas show diverging spatial and temporal integration windows across streams, and (iii) within early visual areas (V1-V3), both spatial and temporal integration windows systematically increase with eccentricity. Together, this computational framework and empirical results open exciting new possibilities for modeling and measuring fine-grained spatiotemporal dynamics of neural responses in the human brain using fMRI.

Intracranial study in humans: Neural spectral changes during watching comedy movie of Charlie Chaplin

Humor plays a prominent role in our lives. Thus, understanding the cognitive and neural mechanisms of humor is particularly important. Previous studies that investigated neural substrates of humor used functional MRI and to a lesser extent EEG. In the present study, we conducted intracranial recording in human patients, enabling us to obtain the signal with high temporal precision from within specific brain locations. Our analysis focused on the temporal lobe and the surrounding areas, the temporal lobe was most densely covered in our recording. Thirteen patients watched a fragment of a Charlie Chaplin movie. An independent group of healthy participants rated the same movie fragment, helping us to identify the most funny and the least funny frames of the movie. We compared neural activity occurring during the most funny and least funny frames across frequencies in the range of 1-170 Hz. The most funny compared to least funny parts of the movie were associated with activity modulation in the broadband high-gamma (70-170 Hz; mostly activation) and to a lesser extent gamma band (40-69Hz; activation) and low frequencies (1-12 Hz, delta, theta, alpha bands; mostly deactivation). With regard to regional specificity, we found three types of brain areas: (I) temporal pole, middle and inferior temporal gyrus (both anterior and posterior) in which there was both activation in the high-gamma/gamma bands and deactivation in low frequencies; (II) ventral part of the temporal lobe such as the fusiform gyrus, in which there was mostly deactivation the low frequencies; (III) posterior temporal cortex and its environment, such as the middle occipital and the temporo-parietal junction, in which there was activation in the high-gamma/gamma band. Overall, our results suggest that humor appreciation might be achieved by neural activity across the frequency spectrum.

Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, as differences can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings

Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both. However, other work has proposed that representations of drawings and natural images become similar only after substantial processing has taken place, suggesting distinct mechanisms. To arbitrate between those alternatives, we measured brain responses resolved in space and time using fMRI and MEG, respectively, while human participants (female and male) viewed images of objects depicted as photographs, line drawings, or sketch-like drawings. Using multivariate decoding, we demonstrate that object category information emerged similarly fast and across overlapping regions in occipital, ventral-temporal, and posterior parietal cortex for all types of depiction, yet with smaller effects at higher levels of visual abstraction. In addition, cross-decoding between depiction types revealed strong generalization of object category information from early processing stages on. Finally, by combining fMRI and MEG data using representational similarity analysis, we found that visual information traversed similar processing stages for all types of depiction, yet with an overall stronger representation for photographs. Together, our results demonstrate broad commonalities in the neural dynamics of object recognition across types of depiction, thus providing clear evidence for shared neural mechanisms underlying recognition of natural object images and abstract drawings.SIGNIFICANCE STATEMENT When we see a line drawing, we effortlessly recognize it as an object in the world despite its simple and abstract style. Here we asked to what extent this correspondence in perception is reflected in the brain. To answer this question, we measured how neural processing of objects depicted as photographs and line drawings with varying levels of detail (from natural images to abstract line drawings) evolves over space and time. We find broad commonalities in the spatiotemporal dynamics and the neural representations underlying the perception of photographs and even abstract drawings. These results indicate a shared basic mechanism supporting recognition of drawings and natural images.

Characterization of Hemodynamic Alteration in Parkinson's Disease and Effect on Resting-State Connectivity

Functional magnetic resonance imaging (fMRI) is a convolution of latent neural activity and the hemodynamic response function (HRF). According to prior studies, the neurodegenerative process in idiopathic Parkinson's Disease (PD) interacts significantly with neuromuscular abnormalities. Although these underlying neuromuscular changes might influence the temporal characteristics of HRF and fMRI signals, relatively few studies have explored this possibility. We hypothesized that such alterations would engender changes in estimated functional connectivity (FC) in fMRI space compared to latent neural space. To test these theories, we calculated voxel-level HRFs by deconvolving resting-state fMRI data from PD patients (n = 61) and healthy controls (HC) (n = 47). Significant group differences in HRF (P < 0.05, Gaussian random field-corrected) were observed in several regions previously associated with PD. Subsequently, we focused on putamen-seed-based FC differences between the PD and HC groups using fMRI and latent neural signals. The results suggested that neglecting HRF variability may cultivate false-positive and false-negative FC group differences. Furthermore, HRF was related to dopamine receptor type 2 (DRD2) gene expression (P < 0.001, t = -7.06, false discover rate-corrected). Taken together, these findings reveal HRF variation and its possible underlying molecular mechanism in PD, and suggest that deconvolution could reduce the impact of HRF variation on FC group differences.

Dynamic Effective Connectivity using Physiologically informed Dynamic Causal Model with Recurrent Units: A functional Magnetic Resonance Imaging simulation study

Functional MRI (fMRI) is an indirect reflection of neuronal activity. Using generative biophysical model of fMRI data such as Dynamic Causal Model (DCM), the underlying neuronal activities of different brain areas and their causal interactions (i.e., effective connectivity) can be calculated. Most DCM studies typically consider the effective connectivity to be static for a cognitive task within an experimental run. However, changes in experimental conditions during complex tasks such as movie-watching might result in temporal variations in the connectivity strengths. In this fMRI simulation study, we leverage state-of-the-art Physiologically informed DCM (P-DCM) along with a recurrent window approach and discretization of the equations to infer the underlying neuronal dynamics and concurrently the dynamic (time-varying) effective connectivities between various brain regions for task-based fMRI. Results from simulation studies on 3- and 10-region models showed that functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) responses and effective connectivity time-courses can be accurately predicted and distinguished from faulty graphical connectivity models representing cognitive hypotheses. In summary, we propose and validate a novel approach to determine dynamic effective connectivity between brain areas during complex cognitive tasks by combining P-DCM with recurrent units.

Mesoscopic in vivo human T2* dataset acquired using quantitative MRI at 7 Tesla

Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for advancing neuroscience and bridging the resolution gap with animal models. Despite the variety of MRI contrasts measured in recent years at the mesoscopic scale, in vivo quantitative imaging of T₂^* has not been performed. Here we provide a dataset containing empirical T₂^* measurements acquired at 0.35 × 0.35 × 0.35 mm³ voxel resolution using 7 Tesla MRI. To demonstrate unique features and high quality of this dataset, we generate flat map visualizations that reveal fine-scale cortical substructures such as layers and vessels, and we report quantitative depth-dependent T₂^* (as well as R₂^*) values in primary visual cortex and auditory cortex that are highly consistent across subjects. This dataset is freely available at https://doi.org/10.17605/OSF.IO/N5BJ7, and may prove useful for anatomical investigations of the human brain, as well as for improving our understanding of the basis of the T₂^*-weighted (f)MRI signal.

Do sparse brain activity patterns underlie human cognition?

Accumulating multivariate pattern analysis (MVPA) results from fMRI studies suggest that information is represented in fingerprint patterns of activations and deactivations during perception, emotions, and cognition. We postulate that these fingerprint patterns might reflect neuronal-population level sparse code documented in two-photon calcium imaging studies in animal models, i.e., information represented in specific and reproducible ensembles of a few percent of active neurons amidst widespread inhibition in neural populations. We suggest that such representations constitute a fundamental organizational principle via interacting across multiple levels of brain hierarchy, thus giving rise to perception, emotions, and cognition.

Multi-Echo Investigations of Positive and Negative CBF and Concomitant BOLD Changes: Positive and negative CBF and BOLD changes

Unlike the positive blood oxygenation level-dependent (BOLD) response (PBR), commonly taken as an indication of an 'activated' brain region, the physiological origin of negative BOLD signal changes (i.e. a negative BOLD response, NBR), also referred to as 'deactivation' is still being debated. In this work, an attempt was made to gain a better understanding of the underlying mechanism by obtaining a comprehensive measure of the contributing cerebral blood flow (CBF) and its relationship to the NBR in the human visual cortex, in comparison to a simultaneously induced PBR in surrounding visual regions. To overcome the low signal-to-noise ratio (SNR) of CBF measurements, a newly developed multi-echo version of a center-out echo planar-imaging (EPI) readout was employed with pseudo-continuous arterial spin labeling (pCASL). It achieved very short echo and inter-echo times and facilitated a simultaneous detection of functional CBF and BOLD changes at 3 T with improved sensitivity. Evaluations of the absolute and relative changes of CBF and the effective transverse relaxation rate,R₂^* the coupling ratios, and their dependence on CBF at rest, CBF_rest indicated differences between activated and deactivated regions. Analysis of the shape of the respective functional responses also revealed faster negative responses with more pronounced post-stimulus transients. Resulting differences in the flow-metabolism coupling ratios were further examined for potential distinctions in the underlying neuronal contributions.

It's about time: Linking dynamical systems with human neuroimaging to understand the brain

Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

HiHi fMRI: a data-reordering method for measuring the hemodynamic response of the brain with high temporal resolution and high SNR

There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with high temporal resolution opens up new avenues to study the in vivo functioning of the human brain with functional magnetic resonance imaging. Because the speed of sampling and the signal level are intrinsically connected in magnetic resonance imaging via the T1 relaxation time, optimization efforts usually must make a trade-off to increase the temporal sampling rate at the cost of the signal level. We present a method, which combines a sparse event-related stimulus paradigm with subsequent data reshuffling to achieve high temporal resolution while maintaining high signal levels (HiHi). The proof-of-principle is presented by separately measuring the single-voxel time course of the BOLD response in both the primary visual and primary motor cortices with 100-ms temporal resolution.

The glymphatic system: Current understanding and modeling

We review theoretical and numerical models of the glymphatic system, which circulates cerebrospinal fluid and interstitial fluid around the brain, facilitating solute transport. Models enable hypothesis development and predictions of transport, with clinical applications including drug delivery, stroke, cardiac arrest, and neurodegenerative disorders like Alzheimer's disease. We sort existing models into broad categories by anatomical function: Perivascular flow, transport in brain parenchyma, interfaces to perivascular spaces, efflux routes, and links to neuronal activity. Needs and opportunities for future work are highlighted wherever possible; new models, expanded models, and novel experiments to inform models could all have tremendous value for advancing the field.

Brain-correlates of processing local dependencies within a statistical learning paradigm

Statistical learning refers to the implicit mechanism of extracting regularities in our environment. Numerous studies have investigated the neural basis of statistical learning. However, how the brain responds to violations of auditory regularities based on prior (implicit) learning requires further investigation. Here, we used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of processing events that are irregular based on learned local dependencies. A stream of consecutive sound triplets was presented. Unbeknown to the subjects, triplets were either (a) standard, namely triplets ending with a high probability sound or, (b) statistical deviants, namely triplets ending with a low probability sound. Participants (n = 33) underwent a learning phase outside the scanner followed by an fMRI session. Processing of statistical deviants activated a set of regions encompassing the superior temporal gyrus bilaterally, the right deep frontal operculum including lateral orbitofrontal cortex, and the right premotor cortex. Our results demonstrate that the violation of local dependencies within a statistical learning paradigm does not only engage sensory processes, but is instead reminiscent of the activation pattern during the processing of local syntactic structures in music and language, reflecting the online adaptations required for predictive coding in the context of statistical learning.

Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping

A general linear model is widely used for analyzing fMRI data, in which the blood oxygenation-level dependent (BOLD) signals in gray matter (GM) evoked in response to neural stimulation are modeled by convolving the time course of the expected neural activity with a canonical hemodynamic response function (HRF) obtained a priori. The maps of brain activity produced reflect the magnitude of local BOLD responses. However, detecting BOLD signals in white matter (WM) is more challenging as the BOLD signals are weaker and the HRF is different, and may vary more across the brain. Here we propose a model-free approach to detect changes in BOLD signals in WM by measuring task-evoked increases of BOLD signal synchrony in WM fibers. The proposed approach relies on a simple assumption that, in response to a functional task, BOLD signals in relevant fibers are modulated by stimulus-evoked neural activity and thereby show greater synchrony than when measured in a resting state, even if their magnitudes do not change substantially. This approach is implemented in two technical stages. First, for each voxel a fiber-architecture-informed spatial window is created with orientation distribution functions constructed from diffusion imaging data. This provides the basis for defining neighborhoods in WM that share similar local fiber architectures. Second, a modified principal component analysis (PCA) is used to estimate the synchrony of BOLD signals in each spatial window. The proposed approach is validated using a 3T fMRI dataset from the Human Connectome Project (HCP) at a group level. The results demonstrate that neural activity can be reliably detected as increases in fMRI signal synchrony within WM fibers that are engaged in a task with high sensitivities and reproducibility.

Transient brain-wide coactivations and structured transitions revealed in hemodynamic imaging data

Brain-wide patterns in resting human brains, as either structured functional connectivity (FC) or recurring brain states, have been widely studied in the neuroimaging literature. In particular, resting-state FCs estimated over windowed timeframe neuroimaging data from sub-minutes to minutes using correlation or blind source separation techniques have reported many brain-wide patterns of significant behavioral and disease correlates. The present pilot study utilized a novel whole-head cap-based high-density diffuse optical tomography (DOT) technology, together with data-driven analysis methods, to investigate recurring transient brain-wide patterns in spontaneous fluctuations of hemodynamic signals at the resolution of single timeframes from thirteen healthy adults in resting conditions. Our results report that a small number, i.e., six, of brain-wide coactivation patterns (CAPs) describe major spatiotemporal dynamics of spontaneous hemodynamic signals recorded by DOT. These CAPs represent recurring brain states, showing spatial topographies of hemispheric symmetry, and exhibit highly anticorrelated pairs. Moreover, a structured transition pattern among the six brain states is identified, where two CAPs with anterior-posterior spatial patterns are significantly involved in transitions among all brain states. Our results further elucidate two brain states of global positive and negative patterns, indicating transient neuronal coactivations and co-deactivations, respectively, over the entire cortex. We demonstrate that these two brain states are responsible for the generation of a subset of peaks and troughs in global signals (GS), supporting the recent reports on neuronal relevance of hemodynamic GS. Collectively, our results suggest that transient neuronal events (i.e., CAPs), global brain activity, and brain-wide structured transitions co-exist in humans and these phenomena are closely related, which extend the observations of similar neuronal events recently reported in animal hemodynamic data. Future studies on the quantitative relationship among these transient events and their relationships to windowed FCs along with larger sample size are needed to understand their changes with behaviors and diseased conditions.

Building bridges: simultaneous multimodal neuroimaging approaches for exploring the organization of brain networks

Brain organization is evident across spatiotemporal scales as well as from structural and functional data. Yet, translating from micro- to macroscale (vice versa) as well as between different measures is difficult. Reconciling disparate observations from different modes is challenging because each specializes within a restricted spatiotemporal milieu, usually has bounded organ coverage, and has access to different contrasts. True intersubject biological heterogeneity, variation in experiment implementation (e.g., use of anesthesia), and true moment-to-moment variations in brain activity (maybe attributable to different brain states) also contribute to variability between studies. Ultimately, for a deeper and more actionable understanding of brain organization, an ability to translate across scales, measures, and species is needed. Simultaneous multimodal methods can contribute to bettering this understanding. We consider four modes, three optically based: multiphoton imaging, single-photon (wide-field) imaging, and fiber photometry, as well as magnetic resonance imaging. We discuss each mode as well as their pairwise combinations with regard to the definition and study of brain networks.

Neuroimaging Methods to Map In Vivo Changes of OXPHOS and Oxidative Stress in Neurodegenerative Disorders

Mitochondrial dysfunction is a pathophysiological hallmark of most neurodegenerative diseases. Several clinical trials targeting mitochondrial dysfunction have been performed with conflicting results. Reliable biomarkers of mitochondrial dysfunction in vivo are thus needed to optimize future clinical trial designs. This narrative review highlights various neuroimaging methods to probe mitochondrial dysfunction. We provide a general overview of the current biological understanding of mitochondrial dysfunction in degenerative brain disorders and how distinct neuroimaging methods can be employed to map disease-related changes. The reviewed methodological spectrum includes positron emission tomography, magnetic resonance, magnetic resonance spectroscopy, and near-infrared spectroscopy imaging, and how these methods can be applied to study alterations in oxidative phosphorylation and oxidative stress. We highlight the advantages and shortcomings of the different neuroimaging methods and discuss the necessary steps to use these for future research. This review stresses the importance of neuroimaging methods to gain deepened insights into mitochondrial dysfunction in vivo, its role as a critical disease mechanism in neurodegenerative diseases, the applicability for patient stratification in interventional trials, and the quantification of individual treatment responses. The in vivo assessment of mitochondrial dysfunction is a crucial prerequisite for providing individualized treatments for neurodegenerative disorders.