Mindfulness-based real-time fMRI neurofeedback: a randomized controlled trial to optimize dosing for depressed adolescents.

BACKGROUND: Adolescence is characterized by a heightened vulnerability for Major Depressive Disorder (MDD) onset, and currently, treatments are only effective for roughly half of adolescents with MDD. Accordingly, novel interventions are urgently needed. This study aims to establish mindfulness-based real-time fMRI neurofeedback (mbNF) as a non-invasive approach to downregulate the default mode network (DMN) in order to decrease ruminatory processes and depressive symptoms. METHODS: Adolescents (N = 90) with a current diagnosis of MDD ages 13-18-years-old will be randomized in a parallel group, two-arm, superiority trial to receive either 15 or 30 min of mbNF with a 1:1 allocation ratio. Real-time neurofeedback based on activation of the frontoparietal network (FPN) relative to the DMN will be displayed to participants via the movement of a ball on a computer screen while participants practice mindfulness in the scanner. We hypothesize that within-DMN (medial prefrontal cortex [mPFC] with posterior cingulate cortex [PCC]) functional connectivity will be reduced following mbNF (Aim 1: Target Engagement). Additionally, we hypothesize that participants in the 30-min mbNF condition will show greater reductions in within-DMN functional connectivity (Aim 2: Dosing Impact on Target Engagement). Aim 1 will analyze data from all participants as a single-group, and Aim 2 will leverage the randomized assignment to analyze data as a parallel-group trial. Secondary analyses will probe changes in depressive symptoms and rumination. DISCUSSION: Results of this study will determine whether mbNF reduces functional connectivity within the DMN among adolescents with MDD, and critically, will identify the optimal dosing with respect to DMN modulation as well as reduction in depressive symptoms and rumination. TRIAL REGISTRATION: This study has been registered with clinicaltrials.gov, most recently updated on July 6, 2023 (trial identifier: NCT05617495).

Dopamine-related striatal neurophysiology is associated with specialization of frontostriatal reward circuitry through adolescence.

Characterizing developmental changes in frontostriatal circuitry is critical to understanding adolescent development and can clarify neurobiological mechanisms underlying increased reward sensitivity and risk-taking and the emergence of psychopathology during this period. However, the role of striatal neurobiology in the development of frontostriatal circuitry through human adolescence remains largely unknown. We examined background connectivity during a reward-guided decision-making task ("reward-state"), in addition to resting-state, and assessed the association between age-related changes in frontostriatal connectivity and age-related changes in reward learning and risk-taking through adolescence. Further, we examined the contribution of dopaminergic processes to changes in frontostriatal circuitry and decision-making using MR-based assessments of striatal tissue-iron as a correlate of dopamine-related neurobiology. Connectivity between the nucleus accumbens (NAcc) and ventral anterior cingulate, subgenual cingulate, and orbitofrontal cortices decreased through adolescence into adulthood, and decreases in reward-state connectivity were associated with improvements reward-guided decision-making as well as with decreases in risk-taking. Finally, NAcc tissue-iron mediated age-related changes and was associated with variability in connectivity, and developmental increases in NAcc R2' corresponded with developmental decreases in connectivity. Our results provide evidence that dopamine-related striatal properties contribute to the specialization of frontostriatal circuitry, potentially underlying changes in risk-taking and reward sensitivity into adulthood.

Maturation of the human striatal dopamine system revealed by PET and quantitative MRI.

The development of the striatum dopamine (DA) system through human adolescence, a time of increased sensation seeking and vulnerability to the emergence of psychopathology, has been difficult to study due to pediatric restrictions on direct in vivo assessments of DA. Here, we applied neuroimaging in a longitudinal sample of n = 146 participants aged 12-30. R2', an MR measure of tissue iron which co-localizes with DA vesicles and is necessary for DA synthesis, was assessed across the sample. In the 18-30 year-olds (n = 79) we also performed PET using [11C]dihydrotetrabenazine (DTBZ), a measure of presynaptic vesicular DA storage, and [11C]raclopride (RAC), an indicator of D2/D3 receptor availability. We observed decreases in D2/D3 receptor availability with age, while presynaptic vesicular DA storage (as measured by DTBZ), which was significantly associated with R2' (standardized coefficient = 0.29, 95% CI = [0.11, 0.48]), was developmentally stable by age 18. Our results provide new evidence for maturational specialization of the striatal DA system through adolescence.

Fast Spatial Resolution Analysis of Quadratic Penalized Least-Squares Image Reconstruction With Separate Real and Imaginary Roughness Penalty: Application to fMRI.

Penalized least-squares iterative image reconstruction algorithms used for spatial resolution-limited imaging, such as functional magnetic resonance imaging (fMRI), commonly use a quadratic roughness penalty to regularize the reconstructed images. When used for complex-valued images, the conventional roughness penalty regularizes the real and imaginary parts equally. However, these imaging methods sometimes benefit from separate penalties for each part. The spatial smoothness from the roughness penalty on the reconstructed image is dictated by the regularization parameter(s). One method to set the parameter to a desired smoothness level is to evaluate the full width at half maximum of the reconstruction method's local impulse response. Previous work has shown that when using the conventional quadratic roughness penalty, one can approximate the local impulse response using an FFT-based calculation. However, that acceleration method cannot be applied directly for separate real and imaginary regularization. This paper proposes a fast and stable calculation for this case that also uses FFT-based calculations to approximate the local impulse responses of the real and imaginary parts. This approach is demonstrated with a quadratic image reconstruction of fMRI data that uses separate roughness penalties for the real and imaginary parts.

Greater preference consistency during the Willingness-to-Pay task is related to higher resting state connectivity between the ventromedial prefrontal cortex and the ventral striatum.

The significance of why a similar set of brain regions are associated with the default mode network and value-related neural processes remains to be clarified. Here, we examined i) whether brain regions exhibiting willingness-to-pay (WTP) task-related activity are intrinsically connected when the brain is at rest, ii) whether these regions overlap spatially with the default mode network, and iii) whether individual differences in choice behavior during the WTP task are reflected in functional brain connectivity at rest. Blood-oxygen-level dependent (BOLD) signal was measured by functional magnetic resonance imaging while subjects performed the WTP task and at rest with eyes open. Brain regions that tracked the value of bids during the WTP task were used as seed regions in an analysis of functional connectivity in the resting state data. The seed in the ventromedial prefrontal cortex was functionally connected to core regions of the WTP task-related network. Brain regions within the WTP task-related network, namely the ventral precuneus, ventromedial prefrontal and posterior cingulate cortex overlapped spatially with publically available maps of the default mode network. Also, those individuals with higher functional connectivity during rest between the ventromedial prefrontal cortex and the ventral striatum showed greater preference consistency during the WTP task. Thus, WTP task-related regions are an intrinsic network of the brain that corresponds spatially with the default mode network, and individual differences in functional connectivity within the WTP network at rest may reveal a priori biases in choice behavior.

Enhanced identification of BOLD-like components with multi-echo simultaneous multi-slice (MESMS) fMRI and multi-echo ICA.

The recent introduction of simultaneous multi-slice (SMS) acquisitions has enabled the acquisition of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data with significantly higher temporal sampling rates. In a parallel development, the use of multi-echo fMRI acquisitions in conjunction with a multi-echo independent component analysis (ME-ICA) approach has been introduced as a means to automatically distinguish functionally-related BOLD signal components from signal artifacts, with significant gains in sensitivity, statistical power, and specificity. In this work, we examine the gains that can be achieved with a combined approach in which data obtained with a multi-echo simultaneous multi-slice (MESMS) acquisition are analyzed with ME-ICA. We find that ME-ICA identifies significantly more BOLD-like components in the MESMS data as compared to data acquired with a conventional multi-echo single-slice acquisition. We demonstrate that the improved performance of MESMS derives from both an increase in the number of temporal samples and the enhanced ability to filter out high-frequency artifacts.

Mechanical failure modes of chronically implanted planar silicon-based neural probes for laminar recording.

Penetrating intracortical electrode arrays that record brain activity longitudinally are powerful tools for basic neuroscience research and emerging clinical applications. However, regardless of the technology used, signals recorded by these electrodes degrade over time. The failure mechanisms of these electrodes are understood to be a complex combination of the biological reactive tissue response and material failure of the device over time. While mechanical mismatch between the brain tissue and implanted neural electrodes have been studied as a source of chronic inflammation and performance degradation, the electrode failure caused by mechanical mismatch between different material properties and different structural components within a device have remained poorly characterized. Using Finite Element Model (FEM) we simulate the mechanical strain on a planar silicon electrode. The results presented here demonstrate that mechanical mismatch between iridium and silicon leads to concentrated strain along the border of the two materials. This strain is further focused on small protrusions such as the electrical traces in planar silicon electrodes. These findings are confirmed with chronic in vivo data (133-189 days) in mice by correlating a combination of single-unit electrophysiology, evoked multi-unit recordings, electrochemical impedance spectroscopy, and scanning electron microscopy from traces and electrode sites with our modeling data. Several modes of mechanical failure of chronically implanted planar silicon electrodes are found that result in degradation and/or loss of recording. These findings highlight the importance of strains and material properties of various subcomponents within an electrode array.

Resting-state fMRI activity predicts unsupervised learning and memory in an immersive virtual reality environment.

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment.

The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures.

In resting-state functional magnetic resonance imaging (fMRI), functional connectivity measures can be influenced by the presence of a strong global component. A widely used pre-processing method for reducing the contribution of this component is global signal regression, in which a global mean time series signal is projected out of the fMRI time series data prior to the computation of connectivity measures. However, the use of global signal regression is controversial because the method can bias the correlation values to have an approximately zero mean and may in some instances create artifactual negative correlations. In addition, while many studies treat the global signal as a non-neural confound that needs to be removed, evidence from electrophysiological and fMRI measures in primates suggests that the global signal may contain significant neural correlates. In this study, we used simultaneously acquired fMRI and electroencephalographic (EEG) measures of resting-state activity to assess the relation between the fMRI global signal and EEG measures of vigilance in humans. We found that the amplitude of the global signal (defined as the standard deviation of the global signal) exhibited a significant negative correlation with EEG vigilance across subjects studied in the eyes-closed condition. In addition, increases in EEG vigilance due to the ingestion of caffeine were significantly associated with both a decrease in global signal amplitude and an increase in the average level of anti-correlation between the default mode network and the task-positive network.

Caffeine-Induced Global Reductions in Resting-State BOLD Connectivity Reflect Widespread Decreases in MEG Connectivity.

In resting-state functional magnetic resonance imaging (fMRI), the temporal correlation between spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal from different brain regions is used to assess functional connectivity. However, because the BOLD signal is an indirect measure of neuronal activity, its complex hemodynamic nature can complicate the interpretation of differences in connectivity that are observed across conditions or subjects. For example, prior studies have shown that caffeine leads to widespread reductions in BOLD connectivity but were not able to determine if neural or vascular factors were primarily responsible for the observed decrease. In this study, we used source-localized magnetoencephalography (MEG) in conjunction with fMRI to further examine the origins of the caffeine-induced changes in BOLD connectivity. We observed widespread and significant (p < 0.01) reductions in both MEG and fMRI connectivity measures, suggesting that decreases in the connectivity of resting-state neuro-electric power fluctuations were primarily responsible for the observed BOLD connectivity changes. The MEG connectivity decreases were most pronounced in the beta band. By demonstrating the similarity in MEG and fMRI based connectivity changes, these results provide evidence for the neural basis of resting-state fMRI networks and further support the potential of MEG as a tool to characterize resting-state connectivity.

Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI.

Resting-state functional connectivity magnetic resonance imaging is proving to be an essential tool for the characterization of functional networks in the brain. Two of the major networks that have been identified are the default mode network (DMN) and the task positive network (TPN). Although prior work indicates that these two networks are anti-correlated, the findings are controversial because the anti-correlations are often found only after the application of a pre-processing step, known as global signal regression, that can produce artifactual anti-correlations. In this paper, we show that, for subjects studied in an eyes-closed rest state, caffeine can significantly enhance the detection of anti-correlations between the DMN and TPN without the need for global signal regression. In line with these findings, we find that caffeine also leads to widespread decreases in connectivity and global signal amplitude. Using a recently introduced geometric model of global signal effects, we demonstrate that these decreases are consistent with the removal of an additive global signal confound. In contrast to the effects observed in the eyes-closed rest state, caffeine did not lead to significant changes in global functional connectivity in the eyes-open rest state.

Spectral-spatial pulse design for through-plane phase precompensatory slice selection in T2*-weighted functional MRI.

T(2)*-weighted functional MR images suffer from signal loss artifacts caused by the magnetic susceptibility differences between air cavities and brain tissues. We propose a novel spectral-spatial pulse design that is slice-selective and capable of mitigating the signal loss. The two-dimensional spectral-spatial pulses create precompensatory phase variations that counteract through-plane dephasing, relying on the assumption that resonance frequency offset and through-plane field gradient are spatially correlated. The pulses can be precomputed before functional MRI experiments and used repeatedly for different slices in different subjects. Experiments with human subjects showed that the pulses were effective in slice selection and loss mitigation at different brain regions.

Regularized field map estimation in MRI.

In fast magnetic resonance (MR) imaging with long readout times, such as echo-planar imaging (EPI) and spiral scans, it is important to correct for the effects of field inhomogeneity to reduce image distortion and blurring. Such corrections require an accurate field map, a map of the off-resonance frequency at each voxel. Standard field map estimation methods yield noisy field maps, particularly in image regions with low spin density. This paper describes regularized methods for field map estimation from two or more MR scans having different echo times. These methods exploit the fact that field maps are generally smooth functions. The methods use algorithms that decrease monotonically a regularized least-squares cost function, even though the problem is highly nonlinear. Results show that the proposed regularized methods significantly improve the quality of field map estimates relative to conventional unregularized methods.

Fast joint reconstruction of dynamic R2* and field maps in functional MRI.

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is conventionally done by reconstructing T(2)(*)-weighted images. However, since the images are unitless they are nonquantifiable in terms of important physiological parameters. An alternative approach is to reconstruct R(2)(*) maps which are quantifiable and have comparable BOLD contrast as T(2)(*)-weighted images. However, conventional R(2)(*) mapping involves long readouts and ignores relaxation during readout. Another problem with fMRI imaging is temporal drift/fluctuations in off-resonance. Conventionally, a field map is collected at the start of the fMRI study to correct for off-resonance, ignoring any temporal changes. Here, we propose a new fast regularized iterative algorithm that jointly reconstructs R(2)(*) and field maps for all time frames in fMRI data. To accelerate the algorithm we linearize the MR signal model, enabling the use of fast regularized iterative reconstruction methods. The regularizer was designed to account for the different resolution properties of both R(2)(*) and field maps and provide uniform spatial resolution. For fMRI data with the same temporal frame rate as data collected for T(2)(*)-weighted imaging the resulting R(2)(*) maps performed comparably to T(2)(*)-weighted images in activation detection while also correcting for spatially global and local temporal changes in off-resonance.