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Gonzalez-Castillo, J.

Publications and source records attributed to Gonzalez-Castillo, J..

17 recordsLinked to original sources
bioRxiv2026

A new fMRI quality metric using multi-echo information: Theory, validation and implications

Consistent, high-quality data is key to the success of fMRI studies given the many confounding factors and undesired signals that contaminate these data. Several quality assurance (QA) metrics exist for fMRI (e.g., temporal signal-to-noise ratio (TSNR), percent ghosting, motion estimates), but none of them leverage relationships between echoes that are part of multi-echo (ME) fMRI acquisitions. Here, we fill this gap by proposing a new QA metric for for ME-fMRI that quantifies the likelihood a given ME scan is dominated by BOLD (Blood Oxygenation Level-Dependent) fluctuations. We refer to this metric as pBOLD; the probability of the signal change being primarily BOLD contrast-dominated. Having an estimate of overall BOLD weighting - both before and after preprocessing - is meaningful because BOLD is the intrinsic contrast mechanism used in fMRI to infer neural activity. We introduce pBOLD to the neuroimaging community by first describing the theoretical principles supporting the metric. Next, we validate pBOLD efficacy using a small dataset (N=7 scans) of constant- and cardiac-gated scans that have distinct levels of contributing BOLD fluctuations. Third, we apply pBOLD to a larger publicly available ME dataset (N=439 scans), to evaluate six different pre-processing pipelines, and show how pBOLD provides complementary information to TSNR. Our results show that ME-based denoising increases both pBOLD and TSNR relative to basic denoising; however, including the global signal (GS) as a regressor only improves TSNR, but worsens pBOLD. Further analyses looking at the BOLD-like characteristics of the GS and its relationship to cardiac and respiratory traces suggest that the observed decrease in pBOLD is likely due to a decrease in BOLD fluctuations of neural origin contributing to the GS, and not due to contributions from other physiological BOLD fluctuations (i.e., respiratory and cardiac function). Finally, we also demonstrate how pBOLD can be applied as a data quality metric, by showing how higher pBOLD results in better ability to predict phenotypes based on whole-brain functional connectivity matrices.

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bioRxiv2025

The human brain mechanisms of afterimages: From networks to cortical layers

Afterimages are common visual illusions that have long attracted popular and scientific interest, yet their neural mechanisms remain little understood. We used high-spatial-resolution fMRI to investigate human whole brain and cortical layer activity in primary visual cortex (V1) linked with afterimages and perceptually similar animated images - stimuli designed to imitate the appearance of afterimages. Both afterimages and perceptually similar images engaged overlapping, widespread brain activity, particularly in visual sensory regions that follow the contralateral circuitry of the primary visual pathway. However, afterimages elicited weaker fMRI signals across the brain compared to images, except in salience network regions, where activity was enhanced for afterimages. Salience network activity could reflect conflict or perceptual reality monitoring mechanisms that detect the illusory nature of afterimages. Cortical layer-specific analyses in V1 revealed afterimages selectively engaged middle and deep cortical layers, while perceptually similar images activated middle and superficial layers. In agreement, layer-specific decoding of afterimage and mock afterimage fMRI signals was present only in deep layers. One interpretation of these V1 cortical layer findings is that afterimage perception involves feedback or top-down neural input akin to self-generated or endogenous conscious perception (e.g., visual imagery). In addition, baseline eye behavior (pupil size and blinking) and fMRI signals in arousal, salience, and visual networks differed depending on whether afterimages were perceived or not perceived. These results challenge the prevailing framing on the neurophysiological origins of afterimages as arising from either retinal or central neural processes. As with image perception, typical afterimages emerge by the interaction between retinal and central neural mechanisms. These findings on the neural processing underlying afterimages may be broadly informative of the neural mechanisms of visual consciousness.

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bioRxiv2025

Rapid decoding of neural information representation from ultra-fast functional magnetic resonance imaging signals

High spatio-temporal resolution is crucial for neuroimaging techniques to improve our understanding of human brain function. While the fMRI signal is slow and shows a spread in latencies over space, the precision of hemodynamic response latency for each voxel is preserved and has been shown to be able to detect oscillatory hemodynamic changes approaching 1 Hz, suggesting its potential to reveal rapid neural dynamics. To examine how fast neural information can be derived from fMRI signals, we performed experiments that acquire high-field (7T) fMRI signals at an ultra-fast sampling rate (TR = 125 ms) from the visual cortex while participants observed naturalistic object stimuli. We applied multivariate pattern decoders to extract presented object-category information from the acquired signals at each sampling time after stimulus onset. Results showed that decoding accuracy rose above statistical significance less than 2 s after signal onset, faster than the peak latency of the hemodynamic response. The peak latency of the decoding accuracy was independent of variations in the hemodynamic latency of voxels used for decoding. The application of sparse decoders further revealed that rapid and accurate decoding was possible by pruning vein-rich voxels off from the multivariate voxel input to the decoders. These results suggest that a combination of ultra-fast sampling and multivariate decoding allows fast and temporally precise analysis of neural activity using fMRI signals. Significance statementFunctional magnetic resonance imaging (fMRI) is the most successful method to evaluate human brain function at fine spatial scales but is thought to lack temporal resolution because of slow hemodynamics. We challenge this conventional notion by fast sampling of fMRI signals, combined with multivariate decoding to extract information content represented in the fMRI signals. Results showed that the information content can be extracted faster than the magnitude of hemodynamic response rises, independent of the large spatial variation of hemodynamic latencies across individual voxels, and even after removing venous voxels from decoders. Our method is thus effective at filtering out slow and variable hemodynamic components, leading to the extraction of rapid and temporally precise components reflecting neuronal activity in human fMRI signals.

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bioRxiv2024

In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants rest matters.

Resting-state fMRI (rs-fMRI) scans--namely those lacking experimentally-controlled stimuli or cognitive demands--are often used to identify aberrant patterns of functional connectivity (FC) in clinical populations. To minimize interpretational uncertainty, researchers control for across-cohort disparities in age, gender, co-morbidities, and head motion. Yet, studies rarely, if ever, consider the possibility that systematic differences in inner experience (i.e., what subjects think and feel during the scan) may directly affect FC measures. Here we demonstrate that is the case using a rs-fMRI dataset comprising 471 scans annotated with experiential data. Wide-spread significant differences in FC are observed between scans that systematically differ in terms of reported in-scanner experience. Additionally, we show that FC can successfully predict specific aspects of in-scanner experience in a manner similar to how it predicts demographics, cognitive abilities, clinical outcomes and labels. Together, these results highlight the key role of in-scanner experience in shaping rs-fMRI estimates of FC.

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bioRxiv2024

Cross-species real time detection of trends in pupil size fluctuation

Pupillometry is a popular method because pupil size is easily measured, sensitive to central neural activity, and associated with behavior, cognition, emotion, and perception. Currently, there is no method for online monitoring phases of pupil size fluctuation. We introduce rtPupilPhase - an open source software that automatically detects trends in pupil size in real time, enabling novel implementations of real time pupillometry towards achieving numerous research and translational goals. We validated the performance of rtPupilPhase on human, rodent, and monkey pupil data and propose future applications of real time pupillometry.

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bioRxiv2023

Visual imagery vividness correlates with afterimage brightness and sharpness

Afterimages are illusory, visual conscious perceptions. A widely accepted theory is that afterimages are caused by retinal signaling that continues after the physical disappearance of a light stimulus. However, afterimages have been reported without preceding visual, sensory stimulation (e.g., conditioned afterimages and afterimages induced by illusory vision). These observations suggest the role of top-down, brain mechanisms in afterimage conscious perception. Therefore, some afterimages may share perceptual features with sensory-independent conscious perceptions (e.g., imagery, hallucinations, and dreams) that occur without bottom-up, sensory input. In the current investigation, we tested for a link between the vividness of visual imagery and afterimage conscious perception. Participants reported their vividness of visual imagery and perceived sharpness, contrast, and duration of negative afterimages. The afterimage perceptual features were acquired using perception matching paradigms that were validated on image stimuli. Relating these perceptual reports revealed that the vividness of visual imagery positively correlated with afterimage contrast and sharpness. These behavioral results support shared neural mechanisms between visual imagery and afterimages. This study encourages future research combining neurophysiology recording methods and afterimage paradigms to directly examine the neural mechanisms of afterimage conscious perception.

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bioRxiv2023

Commentary on Pang et al. (2023) Nature

Pang et al. (2023) present novel analyses demonstrating that brain dynamics can be understood as resulting from the excitation of geometric modes, derived from the shape of the brain. Notably, they demonstrate that linear combinations of geometric modes can reconstruct patterns of fMRI data more accurately, and with fewer dimensions, than comparable connectivity-derived modes. Equipped with these results, and underpinned by neural field theory, the authors contend that the geometry of the cortical surface provides a more parsimonious explanation of brain activity than structural brain connectivity. This claim runs counter to prevailing theories of information flow in the brain, which emphasize the role of long-distance axonal projections and fasciculated white matter in relaying signals between cortical regions (Honey et al. 2009; Deco et al. 2011; Seguin et al., 2023). While we acknowledge that cortical geometry plays an important role in shaping human brain function, we feel that the presented work falls short of establishing that the brains geometry is "a more fundamental constraint on dynamics than complex interregional connectivity" (Pang et al. 2023). Here, we provide 1) a brief critique of the papers framing and 2) evidence showing that their methodology lacks specificity to the brains orientation and shape. Ultimately, we recognize that the geometric mode approach is a powerful representational framework for brain dynamics analysis, but we also believe that there are key caveats to consider alongside the claims made in the manuscript.

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bioRxiv2023

Brain network dynamics in transitions of consciousness reorganize according to task engagement

Substantial changes in behavior, physiology, and brain function occur when alertness decreases 1- 5. These changes in brain function involve increased synchronization between cortical areas 6,7 as well as alterations in sensory processing pathways and networks connecting the thalamus and cortex 5,8-11. Cognitive tasks engage overlapping functional networks with sensory pathways facilitating information processing 12,13, and thalamocortical and corticocortical networks supporting task performance 14,15. Frontoparietal circuits play a crucial role in cognitive tasks 16 and states of decreased consciousness 17. To develop an integrated framework of consciousness and cognition, it is important to understand how fluctuations in alertness and cognitive processing interact in these shared circuits 18. Our hypothesis is that during periods of low alertness, individuals who actively maintain task engagement would recruit additional frontoparietal and sensory processing networks, while thalamocortical dynamics that typically change during sleep onset would remain unaffected. Our findings demonstrated that as alertness decreased, passively listening to auditory tones led to increased synchronization in the parietal lobe, whereas actively performing an auditory task resulted in increased long-range frontoparietal synchronization. During decreasing alertness, passive listening (but not active task engagement) was associated with widespread increased synchronization between the thalamus and cortex. In contrast, active task engagement (but not passive listening) led to increased synchronization between the auditory cortex and the rest of the brain. These results reveal the functional mechanisms of the brains flexible reorganization during transitions of consciousness when individuals are actively engaged in cognitive processes.

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bioRxiv2023

A unifying model for discordant and concordant results in human neuroimaging studies of facial viewpoint selectivity

Our ability to recognize faces regardless of viewpoint is a key property of the primate visual system. Traditional theories hold that facial viewpoint is represented by view-selective mechanisms at early visual processing stages and that representations become increasingly tolerant to viewpoint changes in higher-level visual areas. Newer theories, based on single-neuron monkey electrophysiological recordings, suggest an additional intermediate processing stage invariant to mirror-symmetric face views. Consistent with traditional theories, human studies combining neuroimaging and multivariate pattern analysis (MVPA) methods have provided evidence of view-selectivity in early visual cortex. However, contradictory results have been reported in higher-level visual areas concerning the existence in humans of mirror-symmetrically tuned representations. We believe these results reflect low-level stimulus confounds and data analysis choices. To probe for low-level confounds, we analyzed images from two popular face databases. Analyses of mean image luminance and contrast revealed biases across face views described by even polynomials--i.e., mirror-symmetric. To explain major trends across human neuroimaging studies of viewpoint selectivity, we constructed a network model that incorporates three biological constraints: cortical magnification, convergent feedforward projections, and interhemispheric connections. Given the identified low-level biases, we show that a gradual increase of interhemispheric connections across network layers is sufficient to replicate findings of mirror-symmetry in high-level processing stages, as well as view-tuning in early processing stages. Data analysis decisions--pattern dissimilarity measure and data recentering--accounted for the variable observation of mirror-symmetry in late processing stages. The model provides a unifying explanation of MVPA studies of viewpoint selectivity. We also show how common analysis choices can lead to erroneous conclusions.

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bioRxiv2023

Manifold Learning for fMRI time-varying FC

Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolve over time in meaningful ways at temporal scales going from years (e.g., development) to seconds (e.g., within-scan time-varying FC (tvFC)). Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) expected to retain its most informative aspects (e.g., relationships to behavior, disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)--namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies--are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (i.e., minimum number of latent dimensions; ID) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LE), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but LE could only capture one at a time. We observed substantial variability in embedding quality across MLTs, and within-MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.

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bioRxiv2022

Highlight Results, Don't Hide Them: Enhance interpretation, reduce biases and improve reproducibility

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, we propose that studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice---focusing on highlighting results, rather than hiding all but the strongest ones---can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses. HighlightsO_LIMost studies do not present all results of their analysis, hiding subthreshold ones. C_LIO_LIHiding results negatively affects the interpretation and understanding of the study. C_LIO_LINeuroimagers should present all results of their study, highlighting key ones. C_LIO_LIUsing the public NARPS data, we show several benefits of the "highlighting" approach. C_LIO_LIThe highlighting approach improves individual studies and meta-analyses. C_LI

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bioRxiv2022

Improved whole-brain multivariate hemodynamic deconvolution for multi-echo fMRI with stability selection

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

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bioRxiv2021

The Ubiquitous Vigilance Signal in fMRI Time Series Data

Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations ([~]0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.

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bioRxiv2019

A deconvolution algorithm for multiecho functional MRI: Multiecho Sparse Paradigm Free Mapping

This work introduces a novel algorithm for deconvolution of the BOLD signal in multiecho fMRI data: Multiecho Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the transverse relaxation [Formula] without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of [Formula] changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of [Formula] having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map [Formula] associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.

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bioRxiv2019

Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest

Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of distinct mental processing, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states--commonly used to summarize and interpret resting dFC--can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.

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bioRxiv2018

A framework for offline evaluation and optimization of real-time algorithms for use in neurofeedback, demonstrated on an instantaneous proxy for correlations

Interest in real-time fMRI neurofeedback has grown exponentially over the past few years, both for use as a basic science research tool, and as part of the search for novel clinical interventions for neurological and psychiatric illnesses. In order to expand the range of questions which can be addressed with this tool however, new neurofeedback methods must be developed, going beyond feedback of activations in a single region. These new methods, several of which have already been proposed, are by their nature complex, involving many possible parameters. Here we suggest a framework for evaluating and optimizing algorithms for use in a real-time setting, before beginning the neurofeedback experiment, by offline simulations of algorithm output using a previously collected dataset. We demonstrate the application of this framework on the instantaneous proxy for correlations which we developed for training connectivity between different network nodes, identify the optimal parameters for use with this algorithm, and compare it to more traditional correlation methods. We also examine the effects of advanced imaging techniques, such as multi-echo acquisition, and the integration of these into the realtime processing stream.

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bioRxiv2017

Direct Modulation Of Aberrant Brain Network Connectivity Through Real-Time NeuroFeedback

The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained 3 brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. 17 ASD participants and 10 control participants were scanned over multiple sessions (123 sessions in total). Desired network connectivity patterns were reinforced in real-time, without participants awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.\n\nSignificance StatementMany disorders are characterized by underlying abnormalities in network connectivity. These abnormalities are difficult to address with explicit training procedures (which are unlikely to target the specific abnormalities). Covert neurofeedback however, can directly target these networks, positively reinforcing the desired connections. We have developed a method for reinforcing correlations in real-time, and show that such training is effective, inducing significant, long-lasting changes in connectivity between aberrant networks in Autism Spectrum Disorder. This provides a potential mechanism for modulating aberrant correlation structures in other clinical groups as well.

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