Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging

Accelerating spin-echo EPI through combined patterned multislice excitation and simultaneous multislice acquisition

PURPOSE

To demonstrate further acceleration of spin echo MRI by combining the simultaneous multi-slice approach with the recently introduced patterned multislice excitation (PME) technique and evaluate application for rapid diffusion-weighted MRI.

THEORY AND METHODS

Implementation at 3T involved the design of RF pulses simultaneously acting on four separate slices within hardware limits on peak amplitude. This was accomplished by time-shifted sub-pulses and a dedicated switching scheme of the slice-select gradient. The new technique was evaluated on two clinical MRI systems with differing maximum gradient strength.

RESULTS

Four-fold acceleration was successfully achieved by combining PME with rate-2 SMS. Within fixed measurement time, the proposed approach allows increased averaging or more elaborate sampling of diffusion tensor space. Depending on implementation, gains in SNR per unit time were modest or small, which is attributed to out-of-slice saturation effects of the RF pulses.

CONCLUSION

Combination of PME with SMS-2 for further acceleration of diffusion imaging is feasible at 3T.

Aging modulates large-scale neural network interactions during speech comprehension

Speech comprehension in noisy environments constitutes a critical challenge in everyday life and affects people of all ages. This challenging listening situation can be alleviated using semantic context to predict upcoming words (i.e., predictability gain)-a process associated with the domain-specific semantic network. When no such context can be used, speech comprehension in challenging listening conditions relies on cognitive control functions, underpinned by domain-general networks. Most previous studies focused on regional activity of pre-selected cortical regions or networks in healthy young listeners. Thus, it remains unclear how domain-specific and domain-general networks interact during speech comprehension in noise and how this may change across the lifespan. Here, we used correlational psychophysiological interaction (cPPI) to investigate functional network interactions during sentence comprehension under noisy conditions with varying predictability in healthy young and older listeners. Relative to young listeners, older adults showed increased task-related activity in several domain-general networks but reduced between-network connectivity. Across groups, higher predictability was associated with increased positive coupling between semantic and attention networks and increased negative coupling between semantic and control networks. These results highlight the complex interplay between the semantic network and several domain-general networks underlying the predictability gain. The observed differences in connectivity profiles with age inform the current debate on whether age-related changes in neural activity and functional connectivity reflect compensation or dedifferentiation.

Unpacking the V1 map: Differential covariation of visual properties across spatial dimensions

Primary visual cortex (V1) has played a key role in understanding the organization of cerebral cortex. Both structural and functional properties vary sharply throughout the human V1 map. Despite large variation, underlying constancies computed from the covariation pattern of V1 properties have been proposed. Such constancies would imply that V1 is composed of multiple identical units whose visual properties differ only due to differences in their inputs. To test this, we used fMRI to investigate how V1 cortical magnification and preferred spatial frequency covary across eccentricity and polar angle, measured in 40 observers. V1 cortical magnification and preferred spatial frequency were strongly correlated across eccentricity and around polar angle, however their relation differed between these dimensions: they were proportional across eccentricity but not polar angle. The constant ratio of cortical magnification to preferred spatial frequency when measured as a function of eccentricity suggests a shared underlying cause of variation in the two properties, e.g., the gradient of retinal ganglion cell density across eccentricity. In contrast, the deviation from proportionality around polar angle implies that cortical variation differs from that in retina along this dimension. Thus, a constancy hypothesis is supported for one of the two spatial dimensions of V1, highlighting the importance of examining the full 2D-map to understand how V1 is organized.

Corticospinal tract reconstruction with tumor by using a novel direction filter based tractography method

The corticospinal tract (CST) is the primary neural pathway responsible for voluntary motor functions, and preoperative CST reconstruction is crucial for preserving nerve functions during neurosurgery. Diffusion magnetic resonance imaging-based tractography is the only noninvasive method to preoperatively reconstruct CST in clinical practice. However, for the largesize bundle CST with complex fiber geometry (fanning fibers), reconstructing its full extent remains challenging with local-derived methods without incorporating global information. Especially in the presence of tumors, the mass effect and partial volume effect cause abnormal diffusion signals. In this work, a CST reconstruction tractography method based on a novel direction filter was proposed, designed to ensure robust CST reconstruction in the clinical dataset with tumors. A direction filter based on a fourth-order differential equation was introduced for global direction estimation. By considering the spatial consistency and leveraging anatomical prior knowledge, the direction filter was computed by minimizing the energy between the target directions and initial fiber directions. On the basis of the new directions corresponding to CST obtained by the direction filter, the fiber tracking method was implemented to reconstruct the fiber trajectory. Additionally, a deep learning-based method along with tractography template prior information was employed to generate the regions of interest (ROIs) and initial fiber directions. Experimental results showed that the proposed method yields higher valid connections and lower no connections and exhibits the fewest broken fibers and short-connected fibers. The proposed method offers an effective tool to enhance CST-related surgical outcomes by optimizing tumor resection and preserving CST.

Deep learning based image enhancement for dynamic non-Cartesian MRI: Application to "silent" fMRI

Radial based non-Cartesian sequences may be used for silent functional MRI examinations particularly in settings where scanner noise could pose issues. However, to achieve reasonable temporal resolution, under-sampled 3D radial k-space commonly results in reduced image quality. In recent years, deep learning models for improving image quality have emerged. In this study, we investigate the applicability of deep learning image enhancement methods with a focus on preserving dynamic temporal signal changes. By utilizing high-resolution resting-state fMRI datasets from the Human Connectome Project (HCP) foundation, a ground-truth training set was constructed. The k-space trajectory coordinates of a so-called silent 'Looping Star' fMRI sequence was used to simulate non-Cartesian MRI data from the HCP datasets. Subsequently, these sparse resampled k-space were reconstructed, thereby generating pairs of simulated 'Looping Star' images and ground truth HCP images. The dataset served as the basis for training both 2D-UNet and 3D-UNet deep learning models for image enhancement. A comparative analysis was conducted, and the superior model was further fine-tuned. Evaluation of the final model's performance included standard image quality metrics as well as resting-state fMRI (rs-fMRI) analysis in the time-domain. The 3D-UNet outperformed the 2D-UNet in the image enhancement task, resulting in a significant reduction in error between the network input and the ground truth. Specifically, the 3D-UNet achieved a 97 % reduction in the mean square error between the simulated Looping Star input and the HCP ground truth in the pre-processed dataset. Moreover, the 3D-UNet successfully preserved voxel variations, observed as the correlated activity in the posterior cingulate cortex (PCC) during rs-fMRI analysis while simultaneously mitigating noise in the time-series images. In summary, image quality was improved and artifacts were effectively eliminated through the application of both 2D and 3D deep learning approaches. Comparative analysis of the networks indicated that the use of 3D convolutions is more advantageous than employing a deeper network with 2D convolutions, particularly in scenarios involving global artifacts. Furthermore by demonstrating that the trained neural network successfully preserved temporal characteristics in the BOLD signals, the results suggest applicability in fMRI studies.

The association between cardiorespiratory fitness and resting-state functional connectivity in adults with Down syndrome

INTRODUCTION

Resting-state functional connectivity (FC) of the default mode network (DMN) is linked to Alzheimer's disease in people with Down syndrome (DS). In adults without DS, cardiorespiratory fitness is associated with DMN FC; however, this has not been unexplored in DS.

METHODS

This analysis used baseline data from an intervention in adults with DS. Resting-state functional magnetic resonance imaging measured connectivity from the posterior cingulate cortex seed to DMN nodes. Fitness was measured by the maximal treadmill test. Pearson correlations and linear regressions were used to examine the associations between fitness and FC.

RESULTS

Data from 40 adults with DS (26.0 years, 58% female) showed fitness was associated with overall DMN connectivity (r = 0.472, p = 0.004) and medial prefrontal cortex connectivity (r = 0.431, p = 0.010). The association between fitness and DMN FC remained significant after adjustment for age and sex (β = 0.0072, p = 0.04).

DISCUSSION

Fitness may be associated with DMN FC in DS.

HIGHLIGHTS

In adults with DS, cardiorespiratory fitness was associated with overall DMN connectivity, which remained significant after adjusting for age and sex. No associations were found between moderate to vigorous physical activity and DMN connectivity. Increasing fitness may be a therapeutic strategy for AD prevention or delay in DS.

Unveiling the neural network involved in mentally projecting the self through episodic autobiographical memories

Episodic autobiographical memory involves the ability to travel along the mental timeline, so that events of our own life can be recollected and re-experienced. In the present study, we tested the neural underpinnings of mental travel across past and future autobiographical events by using a spatiotemporal interference task. Participants were instructed to mentally travel across past and future personal (Episodic Autobiographical Memories; EAMs) and Public Events (PEs) during Functional Magnetic Resonance Imaging (fMRI). We found that a distributed network of brain regions (i.e., occipital, temporal, parietal, frontal, and subcortical regions) is implicated in mental projection across past and future independently from the memory category (EAMs or PEs). Interestingly, we observed that most of these regions exhibited a neural modulation as a function of the lifetime period and/or as a function of the compatibility with a back-to-front mental timeline, specifically for EAMs, indicating the key role of these regions in representing the temporal organization of personal but not public events. Present findings provide insights into how personal events are temporally organized within the human brain.

Caregiver or Playmate? Fathers' and mothers' brain responses to ball-play with children

Parents and children often engage in joint play-a domain where mothers and fathers are thought to exhibit disparate behaviors and impact child development via distinct mechanisms. However, little is known about the neural substrates of mother-child and father-child play. In this fMRI study, we sampled the brain activation of parents of preschoolers (N = 88) during a novel event-related adaptation of the virtual ball-tossing game "Cyberball." Mothers (N = 40) and fathers (N = 48) played "Cyberball" ostensibly with their own and an unrelated child, who consecutively included, excluded, and reincluded parents. We found that overall, exclusion yielded comparable neural activations in mothers and fathers associated with mentalizing, saliency, and emotion processing. We also observed a parent gender effect in several brain areas. While mothers exhibited increased reward- and attention-related activity during inclusion, fathers displayed increased mentalizing-related activity during exclusion. Furthermore, we tested parents' response to reinclusion, which revealed a selective decrease in reward-related activity. Finally, exploratory analyses showed that parental involvement was positively correlated with parental brain activity within attention- and mentalizing-related areas during inclusion, as opposed to other game phases, and that an anxious parenting style was associated with increased neural sensitivity for game events involving their own child. Overall, our study elucidates the common and distinct neural networks that mothers and fathers engage during play interactions with their children, supporting theories that postulate only a partial differentiation of paternal and maternal parenting systems.

Experimentally induced colitis impacts myelin development and home-cage behavior in young pigs regardless of supplementation with oral gamma-cyclodextrin-encapsulated tributyrin

Introduction

Colitis, a chronic intestinal disorder that causes inflammation of the colonic mucosa, has been linked with structural brain abnormalities. To combat intestinal inflammation, researchers have investigated how nutritional supplementation, such as butyric acid, may ameliorate untoward effects. By encapsulating and using conjugates of butyrate, such as butyrate glycerides (i.e., tributyrin), slower release to the lower portions of the gastrointestinal tract can be achieved. Additionally, butyrate supplementation has been linked with supporting brain function and regulating integrity.

Methods

In the present study, a total of 24 intact male pigs were artificially reared and randomly assigned to 1 of 3 treatment conditions: (1) a control milk replacer (CON), (2) control plus oral dextran sodium sulfate (DSS) to induce colitis, or (3) control supplemented with 9.0 mM of gamma-cyclodextrin encapsulated tributyrin (TBCD) plus oral DSS (TBCD+DSS). Pigs were orally administered DSS treatments daily from postnatal day (PND) 14-18. Continuous video recording began on PND 3 and ceased on PND 27 or 28, with videos processed and analyzed for home-cage tracking behavior. On PND 26 or 27, pigs underwent neuroimaging procedures to assess overall brain anatomy (MPRAGE), microstructure (DTI), and myelin (MWF).

Results and discussion

Home-cage spatial preference was not altered prior to DSS dosing or during the overall study period. However, TBCD+DSS pigs spent less (p < 0.05) time within quadrant 4 when compared with CON pigs. Across almost all 29 brain regions assessed, absolute volumes were observed to be smaller in the TBCD+DSS group compared with CON and DSS groups. However, once individual volumes were assessed relative to the whole brain, most treatment effects dissipated other than for gray matter volume (p = 0.041). Diffusivity was found to be altered in several regions across treatment groups, thereby indicating differences in fiber organization. In areas like the hippocampus and thalamus, when fractional anisotropy (FA) values were highest for a given treatment, in the other diffusion metrics (mean, radial, axial diffusivity) values were lowest for that same treatment, indicating more organized cellular structure. Several other diffusion trends and differences were observed across various regions. Lastly, myelin water fraction (MWF) values were lowest in DSS-treated groups compared with CON (p < 0.05) for the whole brain and left/right cortices.

Conclusion

Overall, fiber organization and myelination were observed to be altered by experimentally induced colitis and contrary to expectations, tributyrin supplementation did not ameliorate these effects. Future work is warranted to investigate other protective nutritional mechanisms for colitis.

Large-Scale High-Resolution Probabilistic Maps of the Human Superior Longitudinal Fasciculus Subdivisions and their Cortical Terminations

The superior longitudinal fasciculus (SLF) is the large white matter association tract connecting the prefrontal and posterior parietal cortices. Past studies in non-human primates have parcellated the SLF into three subdivisions and have outlined the specific cortico-cortical organization and terminations for each subdivision. However, it is difficult to characterize these structural connections in humans to the specificity of tract-tracing studies in animals. This has led to disagreement on how the SLF subdivisions are organized in the human brain, including if the dorsomedial SLF (SLF-I) is part of the cingulum subsystem. Here, we present a novel large-scale, probabilistic map of the SLF subdivisions, using high-resolution diffusion imaging data from the Human Connectome Project (HCP). We used image data from 302 adult males and 405 adult females to model the three SLF subdivisions in each hemisphere, and attempted to characterize the frontal and parietal termination points for each subdivision. SLF subdivisions were successfully modeled in each subject, showing the dorsomedial-to-ventrolateral organization similar to that in nonhuman primate histological studies. We also found minimal differences between SLF-I models with and without the cingulate gyrus excluded, suggesting that the SLF-I may be a separable tract from the cingulum. Lastly, the SLF subdivisions showed differentiable associations with major cognitive domains such as memory and executive functions. While histological confirmation is needed beyond tractography, these probabilistic masks offer a first step in guiding future exploration of frontoparietal organization by providing detailed characterization of the SLF subdivisions and their potential cortical terminations.Significance statement The prefrontal and posterior parietal areas are interconnected via the SLF, which has been characterized in great detail in monkeys. However, it is difficult to map the SLF organization in the human brain, and previous diffusion MRI findings have been inconsistent. Using diffusion MRI data from 707 individuals, our probabilistic tractography revealed dorsomedial-to-ventrolateral organization of the three SLF subdivisions and their cortical terminations. Our tractography also suggests limited shared volume between the SLF-I and the cingulum, a controversy in recent literature. The SLF subdivisions also differ in their cognitive associations. As a result, we created a large-scale, high-resolution probabilistic parcellation of the SLF, representing an advancement toward standardizing the mapping of human frontoparietal structural connections for clinical and scientific research.

Real-time fMRI using multi-band echo-volumar imaging with millimeter spatial resolution and sub-second temporal resolution at 3 tesla

Purpose

In this study we develop undersampled echo-volumar imaging (EVI) using multi-band/simultaneous multi-slab encoding in conjunction with multi-shot slab-segmentation to accelerate 3D encoding and to reduce the duration of EVI encoding within slabs. This approach combines the sampling efficiency of single-shot 3D encoding with the sensitivity advantage of multi-echo acquisition. We describe the pulse sequence development and characterize the spatial-temporal resolution limits and BOLD sensitivity of this approach for high-speed task-based and resting-state fMRI at 3 T. We study the feasibility of further acceleration using compressed sensing (CS) and assess compatibility with NORDIC denoising.

Methods

Multi-band echo volumar imaging (MB-EVI) combines multi-band encoding of up to 6 slabs with CAIPI shifting, accelerated EVI encoding within slabs using up to 4-fold GRAPPA accelerations, 2-shot kz-segmentation and partial Fourier acquisitions along the two phase-encoding dimensions. Task-based and resting-state fMRI at 3 Tesla was performed across a range of voxel sizes (between 1 and 3 mm isotropic), repetition times (118-650 ms), and number of slabs (up to 12). MB-EVI was compared with multi-slab EVI (MS-EVI) and multi-band-EPI (MB-EPI).

Results

Image quality and temporal SNR of MB-EVI was comparable to MS-EVI when using 2-3 mm spatial resolution. High sensitivity for mapping task-based activation and resting-state connectivity at short TR was measured. Online deconvolution of T2* signal decay markedly reduced spatial blurring and improved image contrast. The high temporal resolution of MB-EVI enabled sensitive mapping of high-frequency resting-state connectivity above 0.3 Hz with 3 mm isotropic voxel size (TR: 163 ms). Detection of task-based activation with 1 mm isotropic voxel size was feasible in scan times as short as 1 min 13 s. Compressed sensing with up to 2.4-fold retrospective undersampling showed negligible loss in image quality and moderate region-specific losses in BOLD sensitivity. NORDIC denoising significantly enhanced fMRI sensitivity without introducing image blurring.

Conclusion

Combining MS-EVI with multi-band encoding enables high overall acceleration factors and provides flexibility for maximizing spatial-temporal resolution and volume coverage. The high BOLD sensitivity of this hybrid MB-EVI approach and its compatibility with online image reconstruction enables high spatial-temporal resolution real-time task-based and resting state fMRI.

Distinct and content-specific neural representations of self- and other-produced actions in joint piano performance

During ensemble performance, musicians predict their own and their partners' action outcomes to smoothly coordinate in real time. The neural auditory-motor system is thought to contribute to these predictions by running internal forward models that simulate self- and other-produced actions slightly ahead of time. What remains elusive, however, is whether and how own and partner actions can be represented simultaneously and distinctively in the sensorimotor system, and whether these representations are content-specific. Here, we applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) data of duetting pianists to dissociate the neural representation of self- and other-produced actions during synchronous joint music performance. Expert pianists played familiar right-hand melodies in a 3 T MR-scanner, in duet with a partner who played the corresponding left-hand basslines in an adjacent room. In half of the pieces, pianists were motorically familiar (or unfamiliar) with their partner's left-hand part. MVPA was applied in primary motor and premotor cortices (M1, PMC), cerebellum, and planum temporale of both hemispheres to classify which piece was performed. Classification accuracies were higher in left than right M1, reflecting the content-specific neural representation of self-produced right-hand melodies. Notably, PMC showed the opposite lateralization, with higher accuracies in the right than left hemisphere, likely reflecting the content-specific neural representation of other-produced left-hand basslines. Direct physiological support for the representational alignment of partners' M1 and PMC should be gained in future studies using novel tools like interbrain representational similarity analyses. Surprisingly, motor representations in PMC were similarly precise irrespective of familiarity with the partner's part. This suggests that expert pianists may generalize contents of familiar actions to unfamiliar pieces with similar musical structure, based on the auditory perception of the partner's part. Overall, these findings support the notion of parallel, distinct, and content-specific self and other internal forward models that are integrated within cortico-cerebellar auditory-motor networks to support smooth coordination in musical ensemble performance and possibly other forms of social interaction.

Brain connectivity correlates of the impact of a digital intervention for individuals with subjective cognitive decline on depression and IL-18

Late-life depression represents a significant health concern, linked to disruptions in brain connectivity and immune functioning, mood regulation, and cognitive function. This pilot study explores a digital intervention targeting mental health, brain health, and immune functioning in individuals aged 55-60 with subjective cognitive decline, elevated stress and depressive symptoms. Seventeen participants engaged in a two-week intervention comprising spatial cognition, psychological techniques based on mindfulness, attention-training exercises, and cognitive behavioral therapy. Pre-and post-intervention changes in resting-state functional connectivity, inflammation, and psychological health were evaluated. Key findings include: (1) Reduced self-reported depression with a large effect size, (2) Decreased connectivity within the default mode network (DMN), (3) Enhanced anticorrelation between the DMN-Salience networks that was associated with improved depression scores (4) Reduced salivary IL-18 concentration with a medium effect size, correlated with decreased DMN-amygdala connectivity. There was a trend towards reduced anxiety, with no significant changes in quality of life. To our knowledge, this is the first study to investigate the effect of digital intervention on immune markers, clinical behavioral outcomes, and brain function, demonstrating positive synergistic potential across all three levels. These preliminary findings, which need replication in larger, controlled studies, have important implications for basic science and scalable digital interventions.

WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis

This paper introduces the Welsh Advanced Neuroimaging Database (WAND), a multi-scale, multi-modal imaging dataset comprising in vivo brain data from 170 healthy volunteers (aged 18-63 years), including 3 Tesla (3 T) magnetic resonance imaging (MRI) with ultra-strong (300 mT/m) magnetic field gradients, structural and functional MRI and nuclear magnetic resonance spectroscopy at 3 T and 7 T, magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS), together with trait questionnaire and cognitive data. Data are organised using the Brain Imaging Data Structure (BIDS). In addition to raw data, we provide brain-extracted T1-weighted images, and quality reports for diffusion, T1- and T2-weighted structural data, and blood-oxygen level dependent functional tasks. Reasons for participant exclusion are also included. Data are available for download through our GIN repository, a data access management system designed to reduce storage requirements. Users can interact with and retrieve data as needed, without downloading the complete dataset. Given the depth of neuroimaging phenotyping, leveraging ultra-high-gradient, high-field MRI, MEG and TMS, this dataset will facilitate multi-scale and multi-modal investigations of the healthy human brain.

Tale of Two n-Backs: Diverging Associations of Dorsolateral Prefrontal Cortex Activation With n-Back Task Performance

In studying the neural correlates of working memory (WM) ability via functional magnetic resonance imaging (fMRI) in health and disease, it is relatively uncommon for investigators to report associations between brain activation and measures of task performance. Additionally, how the choice of WM task impacts observed activation-performance relationships is poorly understood. We sought to illustrate the impact of WM task on brain-behavior correlations using two large, publicly available datasets. We conducted between-participants analyses of task-based fMRI data from two publicly available datasets: The Human Connectome Project (HCP; n = 866) and the Queensland Twin Imaging (QTIM) Study (n = 459). Participants performed two distinct variations of the n-back WM task with different stimuli, timings, and response paradigms. Associations between brain activation ([2-back - 0-back] contrast) and task performance (2-back % correct) were investigated separately in each dataset, as well as across datasets, within the dorsolateral prefrontal cortex (dlPFC), medial prefrontal cortex, and whole cortex. Global patterns of activation to task were similar in both datasets. However, opposite associations between activation and task performance were observed in bilateral pre-supplementary motor area and left middle frontal gyrus. Within the dlPFC, HCP participants exhibited a significantly greater activation-performance relationship in bilateral middle frontal gyrus relative to QTIM Study participants. The observation of diverging activation-performance relationships between two large datasets performing variations of the n-back task serves as a critical reminder for investigators to exercise caution when selecting WM tasks and interpreting neural activation in response to a WM task.

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.

Changes in Brain Functional Connectivity Underlying the Space-Number Association

Whether small number magnitudes are inherently represented as lying to the left of larger ones, the space-number association (SNA), is an important issue in mathematical cognition. In this fMRI study, we used a go/no-go implicit association task to investigate the brain activity and functional connectivity underlying the SNA. Arabic digits lower or higher than 5 and left- or right-pointing arrows were alternated as central targets. In a single-code task condition, participants responded to a specific number magnitude and to all arrows or to a specific arrow direction and to all number magnitudes. In a joint-code (JC) condition, responses were provided after congruent, for example, "go when a number is lower than 5 or an arrow points left," or incongruent, for example, "go when a number is lower than 5 or an arrow points right," SNAs. The SNA was only found in the JC condition, where responses were faster with congruent instructions. Analyses of fMRI functional connectivity showed that the SNA was matched with enhanced excitatory inputs from ACC, the left TPJ, and the left inferior frontal gyrus to the left and right intraparietal sulcus (IPS). Incongruent JC trials were associated with enhanced excitatory modulation from ACC to the left and right IPS. These results show that the SNA is associated with enhanced activation of top-down brain control and changes in the functional interaction between the left and right IPS. We conclude that the SNA does not depend on an inherent and bottom-up spatial coding of number magnitudes.

The impact of transcutaneous vagus nerve stimulation on anterior cingulate cortex activity in a cognitive control task

Transcutaneous vagus nerve stimulation (tVNS) offers a non-invasive method to enhance noradrenergic neurotransmission in the human brain, thereby increasing cognitive control. Here, we investigate if changes in cognitive control induced by tVNS are mediated through locus coeruleus-induced modifications of neural activity in the anterior cingulate cortex. Young healthy participants engaged in a simple cognitive control task focusing on response inhibition and a more complex task that involved both response inhibition and working memory, inside a magnetic resonance imaging scanner. The tasks were executed using a randomized within-subject design, with participants undergoing auricular tVNS and sham stimulation in separate sessions. tVNS significantly changed performance in the simple control task reflected in a greater propensity to respond. Furthermore, we observed a significant increase in neural activity in the anterior cingulate cortex during the simple cognitive control task under tVNS. Functional connectivity analyses revealed positive coupling between neural activity in the locus coeruleus and anterior cingulate cortex, however, this was not modulated by tVNS. The findings suggest that non-invasive stimulation of the vagus nerve can modulate neural activity in the anterior cingulate cortex. While these neural effects suggest an impact of tVNS in a key region involved in conflict monitoring and cognitive control, the behavioral effects are more indicative of a shift in response bias rather than enhanced cognitive control.

Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns

Over the past two decades, rapid advancements in magnetic resonance technology have significantly enhanced the imaging resolution of functional Magnetic Resonance Imaging (fMRI), far surpassing its initial capabilities. Beyond mapping brain functional architecture at unprecedented scales, high-spatial-resolution acquisitions have also inspired and enabled several novel analytical strategies that can potentially improve the sensitivity and neuronal specificity of fMRI. With small voxels, one can sample from different levels of the vascular hierarchy within the cerebral cortex and resolve the temporal progression of hemodynamic changes from parenchymal to pial vessels. We propose that this characteristic pattern of temporal progression across cortical depths can aid in distinguishing neurogenic blood-oxygenation-level-dependent (BOLD) signals from typical nuisance factors arising from non-BOLD origins, such as head motion and pulsatility. In this study, we examine the feasibility of applying cross-cortical depth temporal delay patterns to automatically categorize BOLD and non-BOLD signal components in modern-resolution BOLD-fMRI data. We construct an independent component analysis (ICA)-based framework for fMRI de-noising, analogous to previously proposed multi-echo (ME) ICA, except that here we explore the across-depth instead of across-echo dependence to distinguish BOLD and non-BOLD components. The efficacy of this framework is demonstrated using visual task data at three graded spatiotemporal resolutions (voxel sizes = 1.1, 1.5, and 2.0 mm isotropic at temporal intervals = 1700, 1120, and 928 ms). The proposed framework leverages prior knowledge of the spatiotemporal properties of BOLD-fMRI and serves as an alternative to ME-ICA for cleaning moderate- and high-spatial-resolution fMRI data when multi-echo acquisitions are not available.

Perceptual learning and neural correlates of virtual navigation in subjective cognitive decline: A pilot study

Spatial navigation deficits in age-related diseases involve brain changes affecting spatial memory and verbal cognition. Studies in blind and blindfolded individuals show that multisensory training can induce neuroplasticity through visual cortex recruitment. This proof-of-concept study introduces a digital navigation training protocol, integrating egocentric and allocentric strategies with multisensory stimulation and visual masking to enhance spatial cognition and brain connectivity in 17 individuals (mean age 57.2 years) with subjective cognitive decline. Results indicate improved spatial memory performance correlated with recruitment of the visual area 6-thalamic pathway and enhanced connectivity between memory, executive frontal areas, and default mode network (DMN) regions. Additionally, increased connectivity between allocentric and egocentric navigation areas via the retrosplenial complex (RSC) hub was observed. These findings suggest that this training has the potential to induce perceptual learning and neuroplasticity through key functional connectivity hubs, offering potential widespread cognitive benefits by enhancing critical brain network functions.

Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

Normative Neuroimaging Library: Designing a Comprehensive and Demographically Diverse Dataset of Healthy Controls to Support Traumatic Brain Injury Diagnostic and Therapeutic Development

The past decade has seen impressive advances in neuroimaging, moving from qualitative to quantitative outputs. Available techniques now allow for the inference of microscopic changes occurring in white and gray matter, along with alterations in physiology and function. These existing and emerging techniques hold the potential of providing unprecedented capabilities in achieving a diagnosis and predicting outcomes for traumatic brain injury (TBI) and a variety of other neurological diseases. To see this promise move from the research lab into clinical care, an understanding is needed of what normal data look like for all age ranges, sex, and other demographic and socioeconomic categories. Clinicians can only use the results of imaging scans to support their decision-making if they know how the results for their patient compare with a normative standard. This potential for utilizing magnetic resonance imaging (MRI) in TBI diagnosis motivated the American College of Radiology and Cohen Veterans Bioscience to create a reference database of healthy individuals with neuroimaging, demographic data, and characterization of psychological functioning and neurocognitive data that will serve as a normative resource for clinicians and researchers for development of diagnostics and therapeutics for TBI and other brain disorders. The goal of this article is to introduce the large, well-curated Normative Neuroimaging Library (NNL) to the research community. NNL consists of data collected from ∼1900 healthy participants. The highlights of NNL are (1) data are collected across a diverse population, including civilians, veterans, and active-duty service members with an age range (18-64 years) not well represented in existing datasets; (2) comprehensive structural and functional neuroimaging acquisition with state-of-the-art sequences (including structural, diffusion, and functional MRI; raw scanner data are preserved, allowing higher quality data to be derived in the future; standardized imaging acquisition protocols across sites reflect sequences and parameters often recommended for use with various neurological and psychiatric conditions, including TBI, post-traumatic stress disorder, stroke, neurodegenerative disorders, and neoplastic disease); and (3) the collection of comprehensive demographic details, medical history, and a broad structured clinical assessment, including cognition and psychological scales, relevant to multiple neurological conditions with functional sequelae. Thus, NNL provides a demographically diverse population of healthy individuals who can serve as a comparison group for brain injury study and clinical samples, providing a strong foundation for precision medicine. Use cases include the creation of imaging-derived phenotypes (IDPs), derivation of reference ranges of imaging measures, and use of IDPs as training samples for artificial intelligence-based biomarker development and for normative modeling to help identify injury-induced changes as outliers for precision diagnosis and targeted therapeutic development. On its release, NNL is poised to support the use of advanced imaging in clinician decision support tools, the validation of imaging biomarkers, and the investigation of brain-behavior anomalies, moving the field toward precision medicine.

Elevated plasma p-tau231 is associated with reduced generalization and medial temporal lobe dynamic network flexibility among healthy older African Americans

BACKGROUND

Phosphorylated tau (p-tau) and amyloid beta (Aβ) in human plasma may provide an affordable and minimally invasive method to evaluate Alzheimer's disease (AD) pathophysiology. The medial temporal lobe (MTL) is susceptible to changes in structural integrity that are indicative of the disease progression. Among healthy adults, higher dynamic network flexibility within the MTL was shown to mediate better generalization of prior learning, a measure which has been demonstrated to predict cognitive decline and neural changes in preclinical AD longitudinally. Recent developments in cognitive, neural, and blood-based biomarkers of AD risk that may correspond with MTL changes. However, there is no comprehensive study on how these generalization biomarkers, long-term memory, MTL dynamic network flexibility, and plasma biomarkers are interrelated. This study investigated (1) the relationship between long-term memory, generalization performance, and MTL dynamic network flexibility and (2) how plasma p-tau231, p-tau181, and Aβ42/Aβ40 influence generalization, long-term memory, and MTL dynamics in cognitively unimpaired older African Americans.

METHODS

148 participants (Meanage: 70.88,SDage: 6.05) were drawn from the ongoing longitudinal study, Pathways to Healthy Aging in African Americans conducted at Rutgers University-Newark. Cognition was evaluated with the Rutgers Acquired Equivalence Task (generalization task) and Rey Auditory Learning Test (RAVLT) delayed recall. MTL dynamic network connectivity was measured from functional Magnetic Resonance Imaging data. Plasma p-tau231, p-tau181, and Aβ42/Aβ40 were measured from blood samples.

RESULTS

There was a significant positive correlation between generalization performance and MTL Dynamic Network Flexibility (t = 3.372, β = 0.280, p < 0.001). There were significant negative correlations between generalization performance and plasma p-tau231 (t = -3.324, β = -0.265, p = 0.001) and p-tau181 (t = -2.408, β = -0.192, p = 0.017). A significant negative correlation was found between plasma p-tau231 and MTL Dynamic Network Flexibility (t = -2.825, β = -0.232, p = 0.005).

CONCLUSIONS

Increased levels of p-tau231 are associated with impaired generalization abilities and reduced dynamic network flexibility within the MTL. Plasma p-tau231 may serve as a potential biomarker for assessing cognitive decline and neural changes in cognitively unimpaired older African Americans.

Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

An fMRI study examining the role of the extended amygdala and amygdala in emotion and inhibitory control in native versus second language processing

The role of the extended amygdala and amygdala in mediating emotion and inhibitory control in native language versus second language processing is currently not well understood. The current study examined activity in the extended amygdala and amygdala when twelve healthy young adults were exposed to emotional-linguistic stimuli in either their native language (i.e., Japanese) or in a second language (i.e., English) using a go/no-go task while undergoing fMRI scans. Data was bootstrapped using random resampling. A significant interaction was observed for the amygdala and extended amygdala activity for language (English vs. Japanese), emotional-linguistic valence (positive, negative, neutral) and inhibitory control (go/no-go condition). Furthermore, main effects were observed for language and valence for the amygdala and extended amygdala. Main effects were observed for inhibitory control for the extended amygdala and right amygdala but not for the left amygdala, which did not show a main effect for inhibitory control. Significant interactions and main effects were also observed for behavioral outcomes (go/no-go reaction time and accuracy scores) for the amygdala and extended amygdala. Post hoc analyses found that under conditions of inhibitory control participants had less activation in the extended amygdala and amygdala when processing emotional information in English (i.e., second language) compared to Japanese (i.e., native language). In summary, our findings suggest that the amygdala and extended amygdala may mediate emotion and inhibitory control when participants process information in their native (Japanese) versus a second language (English).

A combined DTI-fMRI approach for optimizing the delineation of posteromedial versus anterolateral entorhinal cortex

In the entorhinal cortex (EC), attempts have been made to identify the human homologue regions of the medial (MEC) and lateral (LEC) subregions using either functional magnetic resonance imaging (fMRI) or diffusion tensor imaging (DTI). However, there are still discrepancies between entorhinal subdivisions depending on the choice of connectivity seed regions and the imaging modality used. While DTI can be used to follow the white matter tracts of the brain, fMRI can identify functionally connected brain regions. In this study, we used both DTI and resting-state fMRI in 103 healthy adults to investigate both structural and functional connectivity between the EC and associated cortical brain regions. Differential connectivity with these regions was then used to predict the locations of the human homologues of MEC and LEC. Our results from combining DTI and fMRI support a subdivision into posteromedial (pmEC) and anterolateral (alEC) EC and reveal a confined border between the pmEC and alEC. Furthermore, the EC subregions obtained by either imaging modality showed similar distinct whole-brain connectivity profiles. Optimizing the delineation of the human homologues of MEC and LEC with a combined, cross-validated DTI-fMRI approach allows to define a likely border between the two subdivisions and has implications for both cognitive and translational neuroscience research.

Characterization and Mitigation of a Simultaneous Multi-Slice fMRI Artifact: Multiband Artifact Regression in Simultaneous Slices

Simultaneous multi-slice (multiband) acceleration in fMRI has become widespread, but may be affected by novel forms of signal artifact. Here, we demonstrate a previously unreported artifact manifesting as a shared signal between simultaneously acquired slices in all resting-state and task-based multiband fMRI datasets we investigated, including publicly available consortium data from the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) Study. We propose Multiband Artifact Regression in Simultaneous Slices (MARSS), a regression-based detection and correction technique that successfully mitigates this shared signal in unprocessed data. We demonstrate that the signal isolated by MARSS correction is likely nonneural, appearing stronger in neurovasculature than gray matter. Additionally, we evaluate MARSS both against and in tandem with sICA+FIX denoising, which is implemented in HCP resting-state data, to show that MARSS mitigates residual artifact signal that is not modeled by sICA+FIX. MARSS correction leads to study-wide increases in signal-to-noise ratio, decreases in cortical coefficient of variation, and mitigation of systematic artefactual spatial patterns in participant-level task betas. Finally, MARSS correction has substantive effects on second-level t-statistics in analyses of task-evoked activation. We recommend that investigators apply MARSS to multiband fMRI datasets with moderate or higher acceleration factors, in combination with established denoising methods.

Customization of neonatal functional magnetic resonance imaging: A preclinical phantom-based study

Over the past few decades, the use of functional magnetic resonance imaging (fMRI) on neonates and very young children has increased dramatically in research and clinical settings. However, the specific characteristics of this population and the MRI standards largely derived from adult studies, pose serious practical challenges. The current study aims to provide general methodological guidelines for customized neonatal fMRI by assessing the performance of various fMRI hardware and software applications. Specifically, this article focuses on MR equipment (head coils) and MR sequences (singleband vs. multiband). We computed and compared the signal-to-noise ratio (SNR) and the temporal SNR (tSNR) in different fMRI protocols using a small-size spherical phantom in three different commercial receiver-only head-neck coils. Our findings highlight the importance of coil selection and fMRI sequence planning in optimizing neonatal fMRI. For SNR, the prescan normalize filter resulted in significantly higher values overall, while in general there was no difference between the different sequences. In terms of head coil performance, the 20-channel head coil showed slightly but significantly higher values compared to the others. For tSNR, there was no difference in the usage of the prescan normalize filter, but the values were significantly higher in the singleband EPI sequences compared to the multiband. In contrast to the SNR, the pediatric head coil seems to have an advantage for tSNR. We provide five practical guidelines to assist researchers and clinicians in developing fMRI studies in neonates and young infants. These recommendations are especially relevant considering ethical constraints and exogenous challenges of neonatal fMRI.

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 Domain-Specific Neural Basis of Auditory Statistical Learning in 5-7-Year-Old Children

Statistical learning (SL) is the ability to rapidly track statistical regularities and learn patterns in the environment. Recent studies show that SL is constrained by domain-specific features, rather than being a uniform learning mechanism across domains and modalities. This domain-specificity has been reflected at the neural level, as SL occurs in regions primarily involved in processing of specific modalities or domains of input. However, our understanding of how SL is constrained by domain-specific features in the developing brain is severely lacking. The present study aims to identify the functional neural profiles of auditory SL of linguistic and nonlinguistic regularities among children. Thirty children between 5 and 7 years old completed an auditory fMRI SL task containing interwoven sequences of structured and random syllable/tone sequences. Using traditional group univariate analyses and a group-constrained subject-specific analysis, frontal and temporal cortices showed significant activation when processing structured versus random sequences across both linguistic and nonlinguistic domains. However, conjunction analyses failed to identify overlapping neural indices across domains. These findings are the first to compare brain regions supporting SL of linguistic and nonlinguistic regularities in the developing brain and indicate that auditory SL among developing children may be constrained by domain-specific features.

Direct segmentation of cortical cytoarchitectonic domains using ultra-high-resolution whole-brain diffusion MRI

We assess the potential of detecting cortical laminar patterns and areal borders by directly clustering voxel values of microstructural parameters derived from high-resolution mean apparent propagator (MAP) magnetic resonance imaging (MRI), as an alternative to conventional template-warping-based cortical parcellation methods. We acquired MAP-MRI data with 200μm resolution in a fixed macaque monkey brain. To improve the sensitivity to cortical layers, we processed the data with a local anisotropic Gaussian filter determined voxel-wise by the plane tangent to the cortical surface. We directly clustered all cortical voxels using only the MAP-derived microstructural imaging biomarkers, with no information regarding their relative spatial location or dominant diffusion orientations. MAP-based 3D cytoarchitectonic segmentation revealed laminar patterns similar to those observed in the corresponding histological images. Moreover, transition regions between these laminar patterns agreed more accurately with histology than the borders between cortical areas estimated using conventional atlas/template-warping cortical parcellation. By cross-tabulating all cortical labels in the atlas- and MAP-based segmentations, we automatically matched the corresponding MAP-derived clusters (i.e., cytoarchitectonic domains) across the left and right hemispheres. Our results demonstrate that high-resolution MAP-MRI biomarkers can effectively delineate three-dimensional cortical cytoarchitectonic domains in single individuals. Their intrinsic tissue microstructural contrasts enable the construction of whole-brain mesoscopic cortical atlases.

Multi-echo Acquisition and Thermal Denoising Advances Precision Functional Imaging

The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development. Precise characterization of systems organization during periods of high plasticity is likely to be essential for discoveries promoting lifelong health. Obtaining precision fMRI data during development has unique challenges that highlight the importance of establishing new methods to improve data acquisition, processing, and analysis. Here, we investigate two methods that can facilitate attaining this goal: multi-echo (ME) data acquisition and thermal noise removal with Noise Reduction with Distribution Corrected (NORDIC) principal component analysis. We applied these methods to precision fMRI data from adults, children, and newborn infants. In adults, both ME acquisitions and NORDIC increased temporal signal to noise ratio (tSNR) as well as the split-half reliability of functional connectivity matrices, with the combination helping more than either technique alone. The benefits of NORDIC denoising replicated in both our developmental samples. ME acquisitions revealed longer and more variable T2* relaxation times across the brain in infants relative to older children and adults, leading to major differences in the echo weighting for optimally combining ME data. This result suggests ME acquisitions may be a promising tool for optimizing developmental fMRI, albeit application in infants needs further investigation. The present work showcases methodological advances that improve Precision Functional Mapping in adults and developmental populations and, at the same time, highlights the need for further improvements in infant specific fMRI.

Inhibitory mechanisms in the prefrontal-cortex differentially mediate Putamen activity during valence-based learning

Learning from appetitive and aversive stimuli involves interactions between the prefrontal cortex and subcortical structures. Preclinical and theoretical studies indicate that inhibition is essential in regulating the relevant neural circuitry. Here, we demonstrate that GABA, the main inhibitory neurotransmitter in the central nervous system, differentially affects how the dACC interacts with subcortical structures during appetitive and aversive learning in humans. Participants engaged in tasks involving appetitive and aversive learning, while using functional magnetic resonance spectroscopy (MRS) at 7T to track GABA concentrations in the dACC, alongside whole-brain fMRI scans to assess BOLD activation. During appetitive learning, dACC GABA concentrations were negatively correlated with learning performance and BOLD activity measured from the dACC and the Putamen. These correlations were absent during aversive learning, where dACC GABA concentrations negatively correlated with the connectivity between the dACC and the Putamen. Our results show that inhibition in the dACC mediates appetitive and aversive learning in humans through distinct mechanisms.

Precision Network Modeling of Transcranial Magnetic Stimulation Across Individuals Suggests Therapeutic Targets and Potential for Improvement

Higher-order cognitive and affective functions are supported by large-scale networks in the brain. Dysfunction in different networks is proposed to associate with distinct symptoms in neuropsychiatric disorders. However, the specific networks targeted by current clinical transcranial magnetic stimulation (TMS) approaches are unclear. While standard-of-care TMS relies on scalp-based landmarks, recent FDA-approved TMS protocols use individualized functional connectivity with the subgenual anterior cingulate cortex (sgACC) to optimize TMS targeting. Leveraging previous work on precision network estimation and recent advances in network-level TMS targeting, we demonstrate that clinical TMS approaches target different functional networks between individuals. Homotopic scalp positions (left F3 and right F4) target different networks within and across individuals, and right F4 generally favors a right-lateralized control network. We also modeled the impact of targeting the dorsolateral prefrontal cortex (dlPFC) zone anticorrelated with the sgACC and found that the individual-specific anticorrelated region variably targets a network coupled to reward circuitry. Combining individualized, precision network mapping and electric field (E-field) modeling, we further illustrate how modeling can be deployed to prospectively target distinct closely localized association networks in the dlPFC with meaningful spatial selectivity and E-field intensity and retrospectively assess network engagement. Critically, we demonstrate the feasibility and reliability of this approach in an independent cohort of participants (including those with Major Depressive Disorder) who underwent repeated sessions of TMS to distinct networks, with precise targeting derived from a low-burden single session of data. Lastly, our findings emphasize differences between selectivity and maximal intensity, highlighting the need to consider both metrics in precision TMS efforts.

Insomnia-related brain functional correlates in first-episode drug-naïve major depressive disorder revealed by resting-state fMRI

Introduction

Insomnia is a common comorbidity symptom in major depressive disorder (MDD) patients. Abnormal brain activities have been observed in both MDD and insomnia patients, however, the central pathological mechanisms underlying the co-occurrence of insomnia in MDD patients are still unclear. This study aimed to explore the differences of spontaneous brain activity between MDD patients with and without insomnia, as well as patients with different level of insomnia.

Methods

A total of 88 first-episode drug-naïve MDD patients including 44 with insomnia (22 with high insomnia and 22 with low insomnia) and 44 without insomnia, as well as 44 healthy controls (HC), were enrolled in this study. The level of depression and insomnia were evaluated by HAMD-17, adjusted HAMD-17 and its sleep disturbance subscale in all subjects. Resting-state functional and structural magnetic resonance imaging data were acquired from all participants and then were preprocessed by the software of DPASF. Regional homogeneity (ReHo) values of brain regions were calculated by the software of REST and were compared. Finally, receiver operating characteristic (ROC) curves were conducted to determine the values of abnormal brain regions for identifying MDD patients with insomnia and evaluating the severity of insomnia.

Results

Analysis of variance showed that there were significant differences in ReHo values in the left middle frontal gyrus, left pallidum, right superior frontal gyrus, right medial superior frontal gyrus and right rectus gyrus among three groups. Compared with HC, MDD patients with insomnia showed increased ReHo values in the medial superior frontal gyrus, middle frontal gyrus, triangular inferior frontal gyrus, calcarine fissure and right medial superior frontal gyrus, medial orbital superior frontal gyrus, as well as decreased ReHo values in the left middle occipital gyrus, pallidum and right superior temporal gyrus, inferior temporal gyrus, middle cingulate gyrus, hippocampus, putamen. MDD patients without insomnia demonstrated increased ReHo values in the left middle frontal gyrus, orbital middle frontal gyrus, anterior cingulate gyrus and right triangular inferior frontal gyrus, as well as decreased ReHo values in the left rectus gyrus, postcentral gyrus and right rectus gyrus, fusiform gyrus, pallidum. In addition, MDD patients with insomnia had decreased ReHo values in the left insula when compared to those without insomnia. Moreover, MDD patients with high insomnia exhibited increased ReHo values in the right middle temporal gyrus, and decreased ReHo values in the left orbital superior frontal gyrus, lingual gyrus, right inferior parietal gyrus and postcentral gyrus compared to those with low insomnia. ROC analysis demonstrated that impaired brain region might be helpful for identifying MDD patients with insomnia and evaluating the severity of insomnia.

Conclusion

These findings suggested that MDD patients with insomnia had wider abnormalities of brain activities in the prefrontal-limbic circuits including increased activities in the prefrontal cortex, which might be the compensatory mechanism underlying insomnia in MDD. In addition, decreased activity of left insula might be associated with the occurrence of insomnia in MDD patients and decreased activities of the frontal-parietal network might cause more serious insomnia related to MDD.

Distinct functional connectivity patterns during naturalistic learning by adolescent first versus second language speakers

Spoken lessons (lectures) are commonly used in schools as a medium for conveying educational content. In adolescence, experience-expectant maturation of language and cognitive systems supports learning; however, little is known about whether or how learners' language experiences interact with this integration process during learning. We examined functional connectivity using fMRI in 38 Spanish-English bilingual (L1-Spanish) and English monolingual (L1-English) adolescents during a naturalistic science video lesson in English. Seed analyses including the left inferior frontal gyrus (pars opercularis) and posterior middle temporal gyrus showed that L1-Spanish adolescents, when learning in their second language (L2), displayed widespread bilateral functional connectivity throughout the cortex while L1-English adolescents displayed mostly left-lateralized connectivity with core language regions over the course of the science lesson. Furthermore, we identified functional seed connectivity associated with better learning outcomes for adolescents with diverse language backgrounds. Importantly, functional connectivity patterns in L1-Spanish adolescents while learning in English also correlate with their Spanish cloze reading. Findings suggest that functional networks associated with higher-order language processing and cognitive control are differentially engaged for L1 vs. L2 speakers while learning new information through spoken language.

From Maps to Models: A Survey on the Reliability of Small Studies of Task-Based fMRI

Task-based functional magnetic resonance imaging is a powerful tool for studying brain function, but neuroimaging research produces ongoing concerns regarding small-sample studies and how to interpret them. Although it is well understood that larger samples are preferable, many situations require researchers to make judgments from small studies, including reviewing the existing literature, analyzing pilot data, or assessing subsamples. Quantitative guidance on how to make these judgments remains scarce. To address this, we leverage the Human Connectome Project's Young Adult dataset to survey various analyses-from regional activation maps to predictive models. We find that, for some classic analyses such as detecting regional activation or cluster peak location, studies with as few as 40 subjects are adequate, although this depends crucially on effect sizes. For predictive modeling, similar sizes can be adequate for detecting whether features are predictable, but at least an order of magnitude more (at least hundreds) may be required for developing consistent predictions. These results offer valuable insights for designing and interpreting fMRI studies, emphasizing the importance of considering effect size, sample size, and analysis approach when assessing the reliability of findings. We hope that this survey serves as a reference for identifying which kinds of research questions can be reliably answered with small-scale studies.

Altered brain perfusion and oxygen levels relate to sleepiness and attention in post-COVID syndrome

OBJECTIVE

Persisting neurological symptoms after COVID-19 affect up to 10% of patients and can manifest in fatigue and cognitive complaints. Based on recent evidence, we evaluated whether cerebral hemodynamic changes contribute to post-COVID syndrome (PCS).

METHODS

Using resting-state functional magnetic resonance imaging, we investigated brain perfusion and oxygen level estimates in 47 patients (44.4 ± 11.6 years; F:M = 38:9) and 47 individually matched healthy control participants. Group differences were calculated using two-sample t-tests. Multivariable linear regression was used for associations of each regional perfusion and oxygen level measure with cognition and sleepiness measures. Exploratory hazard ratios were calculated for each brain metric with clinical measures.

RESULTS

Patients presented with high levels of fatigue (79%) and daytime sleepiness (45%). We found widespread decreased brain oxygen levels, most evident in the white matter (false discovery rate adjusted-p-value (p-FDR) = 0.038) and cortical grey matter (p-FDR = 0.015). Brain perfusion did not differ between patients and healthy participants. However, delayed patient caudate nucleus perfusion was associated with better executive function (p-FDR = 0.008). Delayed perfusion in the cortical grey matter and hippocampus were associated with a reduced risk of daytime sleepiness (hazard ratio (HR) = 0.07, p = 0.037 and HR = 0.06, p = 0.034). Decreased putamen oxygen levels were associated with a reduced risk of poor cognitive outcome (HR = 0.22, p = 0.019). Meanwhile, lower thalamic oxygen levels were associated with a higher risk of cognitive fatigue (HR = 6.29, p = 0.017).

INTERPRETATION

Our findings of lower regional brain blood oxygen levels suggest increased cerebral metabolism in PCS, which potentially holds a compensatory function. These hemodynamic changes were related to symptom severity, possibly representing metabolic adaptations.

Value of fractional-order calculus (FROC) model diffusion-weighted imaging combined with simultaneous multi-slice (SMS) acceleration technology for evaluating benign and malignant breast lesions

BACKGROUND

This study explores the diagnostic value of combining fractional-order calculus (FROC) diffusion-weighted model with simultaneous multi-slice (SMS) acceleration technology in distinguishing benign and malignant breast lesions.

METHODS

178 lesions (73 benign, 105 malignant) underwent magnetic resonance imaging with diffusion-weighted imaging using multiple b-values (14 b-values, highest 3000 s/mm2). Independent samples t-test or Mann-Whitney U test compared image quality scores, FROC model parameters (D,, ), and ADC values between two groups. Multivariate logistic regression analysis identified independent variables and constructed nomograms. Model discrimination ability was assessed with receiver operating characteristic (ROC) curve and calibration chart. Spearman correlation analysis and Bland-Altman plot evaluated parameter correlation and consistency.

RESULTS

Malignant lesions exhibited lower D, and ADC values than benign lesions (P < 0.05), with higher values (P < 0.05). In SSEPI-DWI and SMS-SSEPI-DWI sequences, the AUC and diagnostic accuracy of D value are maximal, with D value demonstrating the highest diagnostic sensitivity, while value exhibits the highest specificity. The D and combined model had the highest AUC and accuracy. D and ADC values showed high correlation between sequences, and moderate. Bland-Altman plot demonstrated unbiased parameter values.

CONCLUSION

SMS-SSEPI-DWI FROC model provides good image quality and lesion characteristic values within an acceptable time. It shows consistent diagnostic performance compared to SSEPI-DWI, particularly in D and values, and significantly reduces scanning time.

A Novel Registration Framework for Aligning Longitudinal Infant Brain Tensor Images

Registering longitudinal infant brain images is challenging, as the infant brain undergoes rapid changes in size, shape and tissue contrast in the first months and years of life. Diffusion tensor images (DTI) have relatively consistent tissue properties over the course of infancy compared to commonly used T1 or T2-weighted images, presenting great potential for infant brain registration. Moreover, groupwise registration has been widely used in infant neuroimaging studies to reduce bias introduced by predefined atlases that may not be well representative of samples under study. To date, however, no methods have been developed for groupwise registration of tensor-based images. Here, we propose a novel registration approach to groupwise align longitudinal infant DTI images to a sample-specific common space. Longitudinal infant DTI images are first clustered into more homogenous subgroups based on image similarity using Louvain clustering. DTI scans are then aligned within each subgroup using standard tensor-based registration. The resulting images from all subgroups are then further aligned onto a sample-specific common space. Results show that our approach significantly improved registration accuracy both globally and locally compared to standard tensor-based registration and standard fractional anisotropy-based registration. Additionally, clustering based on image similarity yielded significantly higher registration accuracy compared to no clustering, but comparable registration accuracy compared to clustering based on chronological age. By registering images groupwise to reduce registration bias and capitalizing on the consistency of features in tensor maps across early infancy, our groupwise registration framework facilitates more accurate alignment of longitudinal infant brain images.

Spurious correlations in surface-based functional brain imaging

The study of functional MRI data is increasingly performed after mapping from volumetric voxels to surface vertices. Processing pipelines commonly used to achieve this mapping produce meshes with uneven vertex spacing, with closer neighbours in sulci compared to gyri. Consequently, correlations between the fMRI time series of neighbouring sulcal vertices are stronger than expected. However, the causes, extent, and impacts of this bias are not well understood or widely appreciated. We explain the origins of these biases, and using in-silico models of fMRI data, illustrate how they lead to spurious results. The bias leads to leakage of anatomical cortical folding information into fMRI time series. We show that many common analyses can be affected by this "gyral bias", including test-retest reliability, fingerprinting, functional parcellations, regional homogeneity, and brain-behaviour associations. Finally, we provide recommendations to avoid or remedy this spatial bias.

Trying Harder: How Cognitive Effort Sculpts Neural Representations during Working Memory

While the exertion of mental effort improves performance on cognitive tasks, the neural mechanisms by which motivational factors impact cognition remain unknown. Here, we used fMRI to test how changes in cognitive effort, induced by changes in task difficulty, impact neural representations of working memory (WM). Participants (both sexes) were precued whether WM difficulty would be hard or easy. We hypothesized that hard trials demanded more effort as a later decision required finer mnemonic precision. Behaviorally, pupil size was larger and response times were slower on hard compared with easy trials suggesting our manipulation of effort succeeded. Neurally, we observed robust persistent activity during delay periods in the prefrontal cortex (PFC), especially during hard trials. Yet, details of the memoranda could not be decoded from patterns in prefrontal activity. In the patterns of activity in the visual cortex, however, we found strong decoding of memorized targets, where accuracy was higher on hard trials. To potentially link these across-region effects, we hypothesized that effort, carried by persistent activity in the PFC, impacts the quality of WM representations encoded in the visual cortex. Indeed, we found that the amplitude of delay period activity in the frontal cortex predicted decoded accuracy in the visual cortex on a trial-wise basis. These results indicate that effort-related feedback signals sculpt population activity in the visual cortex, improving mnemonic fidelity.

Selective activations and functional connectivities to the sight of faces, scenes, body parts and tools in visual and non-visual cortical regions leading to the human hippocampus

Connectivity maps are now available for the 360 cortical regions in the Human Connectome Project Multimodal Parcellation atlas. Here we add function to these maps by measuring selective fMRI activations and functional connectivity increases to stationary visual stimuli of faces, scenes, body parts and tools from 956 HCP participants. Faces activate regions in the ventrolateral visual cortical stream (FFC), in the superior temporal sulcus (STS) visual stream for face and head motion; and inferior parietal visual (PGi) and somatosensory (PF) regions. Scenes activate ventromedial visual stream VMV and PHA regions in the parahippocampal scene area; medial (7m) and lateral parietal (PGp) regions; and the reward-related medial orbitofrontal cortex. Body parts activate the inferior temporal cortex object regions (TE1p, TE2p); but also visual motion regions (MT, MST, FST); and the inferior parietal visual (PGi, PGs) and somatosensory (PF) regions; and the unpleasant-related lateral orbitofrontal cortex. Tools activate an intermediate ventral stream area (VMV3, VVC, PHA3); visual motion regions (FST); somatosensory (1, 2); and auditory (A4, A5) cortical regions. The findings add function to cortical connectivity maps; and show how stationary visual stimuli activate other cortical regions related to their associations, including visual motion, somatosensory, auditory, semantic, and orbitofrontal cortex value-related, regions.

Resting state networks in patients with acute disorders of consciousness after severe traumatic brain injury

OBJECTIVES

This study aims to describe resting state networks (RSN) in patients with disorders of consciousness (DOC)s after acute severe traumatic brain injury (TBI).

METHODS

Adult patients with TBI with a GCS score <8 who remained in a coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS), between 2017 and 2020 were included. Blood-oxygen-level dependent imaging was performed to compare their RSN with 10 healthy volunteers.

RESULTS

Of a total of 293 patients evaluated, only 13 patients were included according to inclusion criteria: 7 in coma (54%), 2 in MCS (15%), and 4 (31%) had an UWS. RSN analysis showed that the default mode network (DMN) was present and symmetric in 6 patients (46%), absent in 1 (8%), and asymmetric in 6 (46%). The executive control network (ECN) was present in all patients but was asymmetric in 3 (23%). The right ECN was absent in 2 patients (15%) and the left ECN in 1 (7%). The medial visual network was present in 11 (85%) patients. Finally, the cerebellar network was symmetric in 8 patients (62%), asymmetric in 1 (8%), and absent in 4 (30%).

CONCLUSIONS

A substantial impairment in activation of RSN is demonstrated in patients with DOC after severe TBI in comparison with healthy subjects. Three patterns of activation were found: normal/complete activation, 2) asymmetric activation or partially absent, and 3) absent activation.

Immersive scene representation in human visual cortex with ultra-wide-angle neuroimaging

While human vision spans 220°, traditional functional MRI setups display images only up to central 10-15°. Thus, it remains unknown how the brain represents a scene perceived across the full visual field. Here, we introduce a method for ultra-wide angle display and probe signatures of immersive scene representation. An unobstructed view of 175° is achieved by bouncing the projected image off angled-mirrors onto a custom-built curved screen. To avoid perceptual distortion, scenes are created with wide field-of-view from custom virtual environments. We find that immersive scene representation drives medial cortex with far-peripheral preferences, but shows minimal modulation in classic scene regions. Further, scene and face-selective regions maintain their content preferences even with extreme far-periphery stimulation, highlighting that not all far-peripheral information is automatically integrated into scene regions computations. This work provides clarifying evidence on content vs. peripheral preferences in scene representation and opens new avenues to research immersive vision.

Dynamic off-resonance correction improves functional image analysis in fMRI of awake behaving non-human primates

Introduction

Use of functional MRI in awake non-human primate (NHPs) has recently increased. Scanning animals while awake makes data collection possible in the absence of anesthetic modulation and with an extended range of possible experimental designs. Robust awake NHP imaging however is challenging due to the strong artifacts caused by time-varying off-resonance changes introduced by the animal's body motion. In this study, we sought to thoroughly investigate the effect of a newly proposed dynamic off-resonance correction method on brain activation estimates using extended awake NHP data.

Methods

We correct for dynamic B0 changes in reconstruction of highly accelerated simultaneous multi-slice EPI acquisitions by estimating and correcting for dynamic field perturbations. Functional MRI data were collected in four male rhesus monkeys performing a decision-making task in the scanner, and analyses of improvements in sensitivity and reliability were performed compared to conventional image reconstruction.

Results

Applying the correction resulted in reduced bias and improved temporal stability in the reconstructed time-series data. We found increased sensitivity to functional activation at the individual and group levels, as well as improved reliability of statistical parameter estimates.

Conclusions

Our results show significant improvements in image fidelity using our proposed correction strategy, as well as greatly enhanced and more reliable activation estimates in GLM analyses.

Individual Brain Charting dataset extension, third release for movie watching and retinotopy data

The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.

Hypertension is associated with reduced resting-state medial temporal lobe dynamic network flexibility in older African Americans

Hypertension disproportionately affects African Americans and is a risk factor for Alzheimer's disease (AD). We investigated the relationship of blood pressure (BP) with medial temporal lobe (MTL) dynamic network flexibility (a novel AD biomarker) and cognitive generalization in older African Americans. In a cross-sectional study, 37 normotensive (systolic BP <130 mmHg, 82.5% F, 64.4 ± 4.9 years; 14.3 ± 2.1 years of education) versus 79 hypertensive (systolic BP ≥130 mmHg, 79.5% F, 66.8 ± 4.1 years; 14.0 ± 0.2 years of education) participants were enrolled. All participants completed a 10-min resting-state functional magnetic resonance imaging scan to assess MTL dynamic network flexibility and two generalization tasks to assess cognition. Anthropometrics and aerobic fitness (via 6-min walk test) were also determined. There was no difference in BMI (29.7 ± 6.4 vs. 31.9 ± 6.3 kg/m2, p = 0.083) or aerobic fitness (15.5 ± 2.6 vs. 15.1 ± 2.6 mL/kg/min; p = 0.445) between normotensive and hypertensive groups. However, normotensive participants had higher MTL dynamic network flexibility compared to hypertensive participants (0.42 ± 0.23 vs. 0.32 ± 0.25 mL, p = 0.040), and this was associated with higher mean arterial blood pressure (r = -0.21, p = 0.036). Therefore, hypertensive older African Americans demonstrated lower MTL dynamic network flexibility compared to their normotensive counterparts independent of BMI and aerobic fitness. Further studies are required to determine how blood pressure mediates AD risk in African Americans.

A tale of two n-backs: Diverging associations of dorsolateral prefrontal cortex activation with n-back task performance

Background

In studying the neural correlates of working memory (WM) ability via functional magnetic resonance imaging (fMRI) in health and disease, it is relatively uncommon for investigators to report associations between brain activation and measures of task performance. Additionally, how the choice of WM task impacts observed activation-performance relationships is poorly understood. We sought to illustrate the impact of WM task on brain-behavior correlations using two large, publicly available datasets.

Methods

We conducted between-participants analyses of task-based fMRI data from two publicly available datasets: the Human Connectome Project (HCP; n = 866) and the Queensland Twin Imaging (QTIM) Study (n = 459). Participants performed two distinct variations of the n-back WM task with different stimuli, timings, and response paradigms. Associations between brain activation ([2-back - 0-back] contrast) and task performance (2-back % correct) were investigated separately in each dataset, as well as across datasets, within the dorsolateral prefrontal cortex (dlPFC), medial prefrontal cortex, and whole cortex.

Results

Global patterns of activation to task were similar in both datasets. However, opposite associations between activation and task performance were observed in bilateral pre-supplementary motor area and left middle frontal gyrus. Within the dlPFC, HCP participants exhibited a significantly greater activation-performance relationship in bilateral middle frontal gyrus relative to QTIM Study participants.

Conclusions

The observation of diverging activation-performance relationships between two large datasets performing variations of the n-back task serves as a critical reminder for investigators to exercise caution when selecting WM tasks and interpreting neural activation in response to a WM task.

Sparse Blind Spherical Deconvolution of diffusion weighted MRI

Diffusion-weighted magnetic resonance imaging provides invaluable insights into in-vivo neurological pathways. However, accurate and robust characterization of white matter fibers microstructure remains challenging. Widely used spherical deconvolution algorithms retrieve the fiber Orientation Distribution Function (ODF) by using an estimation of a response function, i.e., the signal arising from individual fascicles within a voxel. In this paper, an algorithm of blind spherical deconvolution is proposed, which only assumes the axial symmetry of the response function instead of its exact knowledge. This algorithm provides a method for estimating the peaks of the ODF in a voxel without any explicit response function, as well as a method for estimating signals associated with the peaks of the ODF, regardless of how those peaks were obtained. The two stages of the algorithm are tested on Monte Carlo simulations, as well as compared to state-of-the-art methods on real in-vivo data for the orientation retrieval task. Although the proposed algorithm was shown to attain lower angular errors than the state-of-the-art constrained spherical deconvolution algorithm on synthetic data, it was outperformed by state-of-the-art spherical deconvolution algorithms on in-vivo data. In conjunction with state-of-the art methods for axon bundles direction estimation, the proposed method showed its potential for the derivation of per-voxel per-direction metrics on synthetic as well as in-vivo data.