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•First machine learning mega-analysis to investigate predictors of real-time fMRI neurofeedback success.•Inclusion of a pre-training no feedback was associated with higher neurofeedback performance.•Patients were associated with higher neurofeedback performance than healthy individuals.•More data (sharing) in the future will allow for design optimization and a better understanding of neurofeedback learning. Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional ‘buffer data’, an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more. [Display omitted]

One important approach to human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic, structural, or functional magnetic resonance imaging (fMRI) data. The intrinsic smoothness of brain data, however, poses a problem for current methods seeking to compare different parcellations. For example, criteria that simply compare within‐parcel to between‐parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the distance‐controlled boundary coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate existing parcellations of the human neocortex in their power to predict functional boundaries for an fMRI data set with many different tasks, as well as for resting‐state data. We find that common anatomical parcellations do not perform better than chance, suggesting that task‐based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting‐state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi‐modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define brain regions that are functionally maximally distinct. We propose a new unbiased evaluation criterion (distance‐controlled boundary coefficient [DCBC]) for brain parcellations to overcome the drawback of existing evaluation criteria that are biased by spatial smoothness. Using DCBC, task‐evoked, and resting‐state functional magnetic resonance imaging data, we found resting‐state group parcellations predict task‐based functional boundaries very well, anatomical atlases predict functional boundaries no better than chance, and multi‐modal parcellations do not improve on resting‐state parcellations.

•A functional ultrasound imaging in an fMRI-like environment with acoustic noise.•Positive rCBV response in auditory cortex and negative response in motor cortex.•Greater acoustic noise reduces functional connectivity in auditory and motor networks.•Functional connectivity by rsfUS under acoustic noise is similar to that by rsfMRI. Loud acoustic noise from the scanner during functional magnetic resonance imaging (fMRI) can affect functional connectivity (FC) observed in the resting state, but the exact effect of the MRI acoustic noise on resting state FC is not well understood. Functional ultrasound (fUS) is a neuroimaging method that visualizes brain activity based on relative cerebral blood volume (rCBV), a similar neurovascular coupling response to that measured by fMRI, but without the audible acoustic noise. In this study, we investigated the effects of different acoustic noise levels (silent, 80 dB, and 110 dB) on FC by measuring resting state fUS (rsfUS) in awake mice in an environment similar to fMRI measurement. Then, we compared the results to those of resting state fMRI (rsfMRI) conducted using an 11.7 Tesla scanner. RsfUS experiments revealed a significant reduction in FC between the retrosplenial dysgranular and auditory cortexes (0.56 ± 0.07 at silence vs 0.05 ± 0.05 at 110 dB, p=.01) and a significant increase in FC anticorrelation between the infralimbic and motor cortexes (−0.21 ± 0.08 at silence vs −0.47 ± 0.04 at 110 dB, p=.017) as acoustic noise increased from silence to 80 dB and 110 dB, with increased consistency of FC patterns between rsfUS and rsfMRI being found with the louder noise conditions. Event-related auditory stimulation experiments using fUS showed strong positive rCBV changes (16.5% ± 2.9% at 110 dB) in the auditory cortex, and negative rCBV changes (−6.7% ± 0.8% at 110 dB) in the motor cortex, both being constituents of the brain network that was altered by the presence of acoustic noise in the resting state experiments. Anticorrelation between constituent brain regions of the default mode network (such as the infralimbic cortex) and those of task-positive sensorimotor networks (such as the motor cortex) is known to be an important feature of brain network antagonism, and has been studied as a biological marker of brain disfunction and disease. This study suggests that attention should be paid to the acoustic noise level when using rsfMRI to evaluate the anticorrelation between the default mode network and task-positive sensorimotor network.

Previous attempts at characterizing the spatial specificity of the blood oxygenation level dependent functional MRI (BOLD fMRI) response by estimating its point-spread function (PSF) have conventionally relied on retinotopic spatial representations of visual stimuli in area V1. Consequently, their estimates were confounded by the width and scatter of receptive fields of V1 neurons. Here, we circumvent these limits by instead using the inherent cortical spatial organization of ocular dominance columns (ODCs) to determine the PSF for both Gradient Echo (GE) and Spin Echo (SE) BOLD imaging at 7 Tesla. By applying Markov chain Monte Carlo sampling on a probabilistic generative model of imaging ODCs, we quantified the PSFs that best predict the spatial structure and magnitude of differential ODCs' responses. Prior distributions for the ODC model parameters were determined by analyzing published data of cytochrome oxidase patterns from post-mortem histology of human V1 and of neurophysiological ocular dominance indices. The average PSF full-widths at half-maximum obtained from differential ODCs’ responses following the removal of voxels influenced by contributions from macroscopic blood vessels were 0.86 mm (SE) and 0.99 mm (GE). Our results provide a quantitative basis for the spatial specificity of BOLD fMRI at ultra-high fields, which can be used for planning and interpretation of high-resolution differential fMRI of fine-scale cortical organizations. [Display omitted] •The spread of BOLD response was previously estimated with retinotopic stimuli.•Previous estimates were confounded by width and scatter of neuronal receptive fields.•We apply Markov Chain Monte Carlo sampling to fit a model of imaging columns to data.•Average FWHM of BOLD spread, following blood vessel removal: 0.86 mm (SE) and 0.99 mm (GE).•Our findings support planning and interpretation of high-resolution differential fMRI.

The diverse functional roles of the insula may emerge from its heavy connectivity to an extensive network of cortical and subcortical areas. Despite several previous attempts to investigate the hierarchical organization of the insula by applying the recently developed gradient approach to insula‐to‐whole brain connectivity data, little is known about whether and how there is variability across connectivity gradients of the insula to different cerebral systems. Resting‐state functional MRI data from 793 healthy subjects were used to discover and validate functional connectivity gradients of the insula, which were computed based on its voxel‐wise functional connectivity profiles to distinct cerebral systems. We identified three primary patterns of functional connectivity gradients of the insula to distinct cerebral systems. The connectivity gradients to the higher‐order transmodal associative systems, including the prefrontal, posterior parietal, temporal cortices, and limbic lobule, showed a ventroanterior‐dorsal axis across the insula; those to the lower‐order unimodal primary systems, including the motor, somatosensory, and occipital cortices, displayed radiating transitions from dorsoanterior toward both ventroanterior and dorsoposterior parts of the insula; the connectivity gradient to the subcortical nuclei exhibited an organization along the anterior–posterior axis of the insula. Apart from complementing and extending previous literature on the heterogeneous connectivity patterns of insula subregions, the presented framework may offer ample opportunities to refine our understanding of the role of the insula in many brain disorders. Resting‐state functional MRI data from 793 healthy subjects were used to discover and validate functional connectivity gradients of the insula, which were computed based on its voxel‐wise functional connectivity profiles to distinct cerebral systems. We identified three primary patterns of functional connectivity gradients of the insula to distinct cerebral systems.

Visual snow syndrome (VSS) is a neurological disorder characterized by a range of continuous visual disturbances. Little is known about the functional pathological mechanisms underlying VSS and their effect on brain network topology, studied using high‐resolution resting‐state (RS) 7 T MRI. Forty VSS patients and 60 healthy controls underwent RS MRI. Functional connectivity matrices were calculated, and global efficiency (network integration), modularity (network segregation), local efficiency (LE, connectedness neighbors) and eigenvector centrality (significance node in network) were derived using a dynamic approach (temporal fluctuations during acquisition). Network measures were compared between groups, with regions of significant difference correlated with known aberrant ocular motor VSS metrics (shortened latencies and higher number of inhibitory errors) in VSS patients. Lastly, nodal co‐modularity, a binary measure of node pairs belonging to the same module, was studied. VSS patients had lower modularity, supramarginal centrality and LE dynamics of multiple (sub)cortical regions, centered around occipital and parietal lobules. In VSS patients, lateral occipital cortex LE dynamics correlated positively with shortened prosaccade latencies (p = .041, r = .353). In VSS patients, occipital, parietal, and motor nodes belonged more often to the same module and demonstrated lower nodal co‐modularity with temporal and frontal regions. This study revealed reduced dynamic variation in modularity and local efficiency strength in the VSS brain, suggesting that brain network dynamics are less variable in terms of segregation and local clustering. Further investigation of these changes could inform our understanding of the pathogenesis of the disorder and potentially lead to treatment strategies. Visual snow syndrome (VSS) is a neurological disorder characterized by a range of continuous visual disturbances. Little is known about the functional pathological mechanisms underlying VSS and their effect on brain network topology, which was studied using high‐resolution resting‐state 7 T MRI. VSS patients demonstrated reduced dynamic variation in modularity and local efficiency strength, suggesting that brain network dynamics are less variable in terms of segregation and local clustering. Network dynamic alterations were centered around occipital cortices and related to ocular motor processing changes.

The capacity of functional MRI (fMRI) to resolve cortical columns depends on several factors. These include the spatial scale of the columnar pattern, the point-spread of the fMRI response, the voxel size, and the signal-to-noise ratio (SNR) considering thermal and physiological noise. However, it remains unknown how these factors combine, and what is the voxel size that optimizes fMRI of cortical columns. Here we combine current knowledge into a quantitative model of fMRI of realistic patterns of cortical columns with different spatial scales and degrees of irregularity. We compare different approaches for identifying patterns of cortical columns, including univariate and multivariate based detection, multi-voxel pattern analysis (MVPA) based decoding, and high-resolution imaging and reconstruction of the pattern of cortical columns. We present the dependence of the performance of each approach on the parameters of the imaged pattern as well as those of the data acquisition. In addition, we predict voxel sizes that optimize fMRI of cortical columns under various scenarios. We found that all measures associated with multivariate detection and decoding could be approximately calculated from a measure we termed “multivariate contrast-to-noise ratio” (mv-CNR), which is a function of the contrast-to-noise ratio (CNR) and number of voxels. Furthermore, mv-CNR implied that the optimal voxel width for detection and decoding is independent of changes in response amplitude, SNR and imaged volume that are not caused by changes in voxel size. For regular patterns, optimal voxel widths for detection, decoding and imaging/reconstructing the pattern of cortical columns were approximately half the main cycle length of the organization. Optimal voxel widths for irregular patterns were less dependent on the main cycle length, and differed between univariate detection, multivariate detection and decoding, and reconstruction. We compared the effects of different factors of Gradient Echo fMRI at 3 Tesla (T), Gradient Echo fMRI at 7T, and Spin-Echo fMRI at 7T on the detection, decoding, and reconstruction measures considered and found that in all cases the width of the fMRI point-spread had the most significant effect. In contrast, different response amplitudes and noise characteristics played a relatively minor role. We recommend specific voxel widths for optimal univariate detection, for multivariate detection and decoding, and for high-resolution imaging of cortical columns under these three data-acquisition scenarios. Our study supports the planning, optimization, and interpretation of high-resolution fMRI of cortical columns and the decoding of information conveyed by these columns. [Display omitted] •We model differential fMRI of realistic patterns of cortical columns.•We evaluate detection probability, MVPA, and high-resolution imaging of the columns.•Optimal voxel sizes for detection and MVPA differ than those for imaging the columns.•We recommend specific voxel widths for imaging under different scenarios at 3T and 7T.•We propose how to approach fMRI of unknown columnar patterns.

Advanced meditation such as jhana meditation can produce various altered states of consciousness (jhanas) and cultivate rewarding psychological qualities including joy, peace, compassion, and attentional stability. Mapping the neurobiological substrates of jhana meditation can inform the development and application of advanced meditation to enhance well‐being. Only two prior studies have attempted to investigate the neural correlates of jhana meditation, and the rarity of adept practitioners has largely restricted the size and extent of these studies. Therefore, examining the consistency and reliability of observed brain responses associated with jhana meditation can be valuable. In this study, we aimed to characterize functional magnetic resonance imaging (fMRI) reliability within a single subject over repeated runs in canonical brain networks during jhana meditation performed by an adept practitioner over 5 days (27 fMRI runs) inside an ultra‐high field 7 Tesla MRI scanner. We found that thalamus and several cortical networks, that is, the somatomotor, limbic, default‐mode, control, and temporo‐parietal, demonstrated good within‐subject reliability across all jhanas. Additionally, we found that several other relevant brain networks (e.g., attention, salience) showed noticeable increases in reliability when fMRI measurements were adjusted for variability in self‐reported phenomenology related to jhana meditation. Overall, we present a preliminary template of reliable brain areas likely underpinning core neurocognitive elements of jhana meditation, and highlight the utility of neurophenomenological experimental designs for better characterizing neuronal variability associated with advanced meditative states. Graphical illustration of the most reliable brain areas associated with advanced and rare meditation states, called jhanas, determined from an intensively sampled 7 T functional magnetic resonance imaging (fMRI) dataset, using an approach of intraclass correlation modified for estimating within‐subject fMRI reliability at the level of canonical brain networks.

The anterior cingulate area (ACC) is an integral part of the prefrontal cortex in mice and supports cognitive functions, including attentional processes, motion planning and execution as well as remote memory, fear and pain. Previous anatomical and functional imaging studies demonstrated that the ACC is interconnected with numerous brain regions, such as motor and sensory cortices, amygdala and limbic areas, suggesting it serves as a hub in functional networks. However, the exact role of the ACC in regulating functional network activity and connectivity remains to be elucidated. Recently developed neuromodulatory techniques, such as Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) allow for precise control of neuronal activity. In this study, we used an inhibitory kappa-opioid receptor DREADD (KORD) to temporally inhibit neuronal firing in the right ACC of mice and assessed functional network activity and connectivity using non-invasive functional magnetic resonance imaging (MRI). We demonstrated that KORD-induced inhibition of the right ACC induced blood oxygenation-level dependent (BOLD) signal decreases and increases in connected brain regions of both hemispheres. More specifically, altered neuronal activity could be observed in functional brain networks including connections with sensory cortex, thalamus, basolateral amygdala and ventral pallidum, areas involved in attention processes, working memory, fear behavior and reward respectively. Furthermore, these modulations in neuronal activity were associated with decreased intra- and interhemispheric functional connectivity. Our results consolidate the hub role of the mouse ACC in functional networks and further demonstrate that the combination of the DREADD technology and non-invasive functional imaging methods is a valuable tool for unraveling mechanisms of network function and dysfunction by reversible inactivation of selected targets. •ACC differentially controls neuronal activity in specific functional brain networks.•ACC inhibition disrupts functional integrity in specific functional brain networks.•KOR-DREADDs - fMRI is a valuable tool for studying brain network functioning.