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).

NEUROFEEDBACK USING FUNCTIONAL SPECTROSCOPY.

Neurofeedback based on real-time measurement of the blood oxygenation level-dependent (BOLD) signal has potential for treatment of neurological disorders and behavioral enhancement. Commonly employed methods are based on functional magnetic resonance imaging (fMRI) sequences that sacrifice speed and accuracy for whole-brain coverage, which is unnecessary in most applications. We present multi-voxel functional spectroscopy (MVFS): a system for computing the BOLD signal from multiple volumes of interest (VOI) in real-time that improves speed and accuracy of neurofeedback. MVFS consists of a functional spectroscopy (FS) pulse sequence, a BOLD reconstruction component, a neural activation estimator, and a stimulus system. The FS pulse sequence is a single-voxel, magnetic resonance spectroscopy sequence without water suppression that has been extended to allow acquisition of a different VOI at each repetition and real-time subject head motion compensation. The BOLD reconstruction component determines the T2* decay rate, which is directly related to BOLD signal strength. The neural activation estimator discounts nuisance signals and scales the activation relative to the amount of ROI noise. Finally, the neurofeedback system presents neural activation-dependent stimuli to experimental subjects with an overall delay of less than 1s. Here we present the MVFS system, validation of certain components, examples of its usage in a practical application, and a direct comparison of FS and echo-planar imaging BOLD measurements. We conclude that in the context of realtime BOLD imaging, MVFS can provide superior accuracy and temporal resolution compared with standard fMRI methods.

Roles of default-mode network and supplementary motor area in human vigilance performance: evidence from real-time fMRI.

We used real-time functional magnetic resonance imaging (fMRI) to determine which regions of the human brain have a role in vigilance as measured by reaction time (RT) to variably timed stimuli. We first identified brain regions where activation before stimulus presentation predicted RT. Slower RT was preceded by greater activation in the default-mode network, including lateral parietal, precuneus, and medial prefrontal cortices; faster RT was preceded by greater activation in the supplementary motor area (SMA). We examined the roles of these brain regions in vigilance by triggering trials based on brain states defined by blood oxygenation level-dependent activation measured using real-time fMRI. When activation of relevant neural systems indicated either a good brain state (increased activation of SMA) or a bad brain state (increased activation of lateral parietal cortex and precuneus) for performance, a target was presented and RT was measured. RTs on trials triggered by a good brain state were significantly faster than RTs on trials triggered by a bad brain state. Thus human performance was controlled by monitoring brain states that indicated high or low vigilance. These findings identify neural systems that have a role in vigilance and provide direct evidence that the default-mode network has a role in human performance. The ability to control and enhance human behavior based on brain state may have broad implications.

When the brain is prepared to learn: enhancing human learning using real-time fMRI.

The rate of learning or memory formation varies over time for any individual, partly due to moment-to-moment fluctuation of brain state. Functional neuroimaging has revealed the neural correlates of learning and memory, but here we asked if neuroimaging can causally enhance human learning by detection of brain states that reveal when a person is prepared or not prepared to learn. The parahippocampal cortex (PHC) is essential for memory formation for scenes. Here, activation in PHC was monitored in real-time, and scene presentations were triggered when participants entered "good" or "bad" brain states for learning of novel scenes. Subsequent recognition memory was more accurate for scenes presented in "good" than "bad" brain states. These findings show that neuroimaging can identify in real-time brain states that enhance or depress learning and memory formation, and knowledge about such brain states may be useful for accelerating education and training. Further, the use of functional neuroimaging as a causal, rather than correlative, tool to study the human brain may open new insights into the neural basis of human cognition.

Computing moment-to-moment BOLD activation for real-time neurofeedback.

Estimating moment-to-moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain-machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment-to-moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment-to-moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI time series, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method.

Locating the functional and anatomical boundaries of human primary visual cortex.

The primary visual cortex (V1) can be delineated both functionally by its topographic map of the visual field and anatomically by its distinct pattern of laminar myelination. Although it is commonly assumed that the specialized anatomy V1 exhibits corresponds in location with functionally defined V1, demonstrating this in human has not been possible thus far due to the difficulty of determining the location of V1 both functionally and anatomically in the same individual. In this study we use MRI to measure the anatomical and functional V1 boundaries in the same individual and demonstrate close agreement between them. Functional V1 location was measured by parcellating occipital cortex of 10 living humans into visual cortical areas based on the topographic map of the visual field measured using functional MRI. Anatomical V1 location was estimated for these same subjects using a surface-based probabilistic atlas derived from high-resolution structural MRI of the stria of Gennari in 10 intact ex vivo human hemispheres. To ensure that the atlas prediction was correct, it was validated against V1 location measured using an observer-independent cortical parcellation based on the laminar pattern of cell density in serial brain sections from 10 separate individuals. The close agreement between the independent anatomically and functionally derived V1 boundaries indicates that the whole extent of V1 can be accurately predicted based on cortical surface reconstructions computed from structural MRI scans, eliminating the need for functional localizers of V1. In addition, that the primary cortical folds predict the location of functional V1 suggests that the mechanism giving rise to V1 location is tied to the development of the cortical folds.

The intrinsic shape of human and macaque primary visual cortex.

Previous studies have reported considerable variability in primary visual cortex (V1) shape in both humans and macaques. Here, we demonstrate that much of this variability is due to the pattern of cortical folds particular to an individual and that V1 shape is similar among individual humans and macaques as well as between these 2 species. Human V1 was imaged ex vivo using high-resolution (200 microm) magnetic resonance imaging at 7 T. Macaque V1 was identified in published histological serial section data. Manual tracings of the stria of Gennari were used to construct a V1 surface, which was computationally flattened with minimal metric distortion of the cortical surface. Accurate flattening allowed investigation of intrinsic geometric features of cortex, which are largely independent of the highly variable cortical folds. The intrinsic shape of V1 was found to be similar across human subjects using both nonparametric boundary matching and a simple elliptical shape model fit to the data and is very close to that of the macaque monkey. This result agrees with predictions derived from current models of V1 topography. In addition, V1 shape similarity suggests that similar developmental mechanisms are responsible for establishing V1 shape in these 2 species.

Cortical folding patterns and predicting cytoarchitecture.

The human cerebral cortex is made up of a mosaic of structural areas, frequently referred to as Brodmann areas (BAs). Despite the widespread use of cortical folding patterns to perform ad hoc estimations of the locations of the BAs, little is understood regarding 1) how variable the position of a given BA is with respect to the folds, 2) whether the location of some BAs is more variable than others, and 3) whether the variability is related to the level of a BA in a putative cortical hierarchy. We use whole-brain histology of 10 postmortem human brains and surface-based analysis to test how well the folds predict the locations of the BAs. We show that higher order cortical areas exhibit more variability than primary and secondary areas and that the folds are much better predictors of the BAs than had been previously thought. These results further highlight the significance of cortical folding patterns and suggest a common mechanism for the development of the folds and the cytoarchitectonic fields.

Accurate prediction of V1 location from cortical folds in a surface coordinate system.

Previous studies demonstrated substantial variability of the location of primary visual cortex (V1) in stereotaxic coordinates when linear volume-based registration is used to match volumetric image intensities [Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T., and Zilles, K. (2000). Brodmann's areas 17 and 18 brought into stereotaxic space-where and how variable? Neuroimage, 11(1):66-84]. However, other qualitative reports of V1 location [Smith, G. (1904). The morphology of the occipital region of the cerebral hemisphere in man and the apes. Anatomischer Anzeiger, 24:436-451; Stensaas, S.S., Eddington, D.K., and Dobelle, W.H. (1974). The topography and variability of the primary visual cortex in man. J Neurosurg, 40(6):747-755; Rademacher, J., Caviness, V.S., Steinmetz, H., and Galaburda, A.M. (1993). Topographical variation of the human primary cortices: implications for neuroimaging, brain mapping, and neurobiology. Cereb Cortex, 3(4):313-329] suggested a consistent relationship between V1 and the surrounding cortical folds. Here, the relationship between folds and the location of V1 is quantified using surface-based analysis to generate a probabilistic atlas of human V1. High-resolution (about 200 microm) magnetic resonance imaging (MRI) at 7 T of ex vivo human cerebral hemispheres allowed identification of the full area via the stria of Gennari: a myeloarchitectonic feature specific to V1. Separate, whole-brain scans were acquired using MRI at 1.5 T to allow segmentation and mesh reconstruction of the cortical gray matter. For each individual, V1 was manually identified in the high-resolution volume and projected onto the cortical surface. Surface-based intersubject registration [Fischl, B., Sereno, M.I., Tootell, R.B., and Dale, A.M. (1999b). High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp, 8(4):272-84] was performed to align the primary cortical folds of individual hemispheres to those of a reference template representing the average folding pattern. An atlas of V1 location was constructed by computing the probability of V1 inclusion for each cortical location in the template space. This probabilistic atlas of V1 exhibits low prediction error compared to previous V1 probabilistic atlases built in volumetric coordinates. The increased predictability observed under surface-based registration suggests that the location of V1 is more accurately predicted by the cortical folds than by the shape of the brain embedded in the volume of the skull. In addition, the high quality of this atlas provides direct evidence that surface-based intersubject registration methods are superior to volume-based methods at superimposing functional areas of cortex and therefore are better suited to support multisubject averaging for functional imaging experiments targeting the cerebral cortex.