Validation of real-time fMRI neurofeedback procedure for cognitive training using counterbalanced active-sham study design.

Investigation of neural mechanisms of real-time functional MRI neurofeedback (rtfMRI-nf) training requires an efficient study control approach. A common rtfMRI-nf study design involves an experimental group, receiving active rtfMRI-nf, and a control group, provided with sham rtfMRI-nf. We report the first study in which rtfMRI-nf procedure is controlled through counterbalancing training runs with active and sham rtfMRI-nf for each participant. Healthy volunteers (n = 18) used rtfMRI-nf to upregulate fMRI activity of an individually defined target region in the left dorsolateral prefrontal cortex (DLPFC) while performing tasks that involved mental generation of a random numerical sequence and serial summation of numbers in the sequence. Sham rtfMRI-nf was provided based on fMRI activity of a different brain region, not involved in these tasks. The experimental procedure included two training runs with the active rtfMRI-nf and two runs with the sham rtfMRI-nf, in a randomized order. The participants achieved significantly higher fMRI activation of the left DLPFC target region during the active rtfMRI-nf conditions compared to the sham rtfMRI-nf conditions. fMRI functional connectivity of the left DLPFC target region with the nodes of the central executive network was significantly enhanced during the active rtfMRI-nf conditions relative to the sham conditions. fMRI connectivity of the target region with the nodes of the default mode network was similarly enhanced. fMRI connectivity changes between the active and sham conditions exhibited meaningful associations with individual performance measures on the Working Memory Multimodal Attention Task, the Approach-Avoidance Task, and the Trail Making Test. Our results demonstrate that the counterbalanced active-sham study design can be efficiently used to investigate mechanisms of active rtfMRI-nf in direct comparison to those of sham rtfMRI-nf. Further studies with larger group sizes are needed to confirm the reported findings and evaluate clinical utility of this study control approach.

Dynamic Functional Connectivity in Pediatric Mild Traumatic Brain Injury.

Resting-state fMRI can be used to identify recurrent oscillatory patterns of functional connectivity within the human brain, also known as dynamic brain states. Alterations in dynamic brain states are highly likely to occur following pediatric mild traumatic brain injury (pmTBI) due to the active developmental changes. The current study used resting-state fMRI to investigate dynamic brain states in 200 patients with pmTBI (ages 8-18 years, median = 14 years) at the subacute (∼1-week post-injury) and early chronic (∼ 4 months post-injury) stages, and in 179 age- and sex-matched healthy controls (HC). A k-means clustering analysis was applied to the dominant time-varying phase coherence patterns to obtain dynamic brain states. In addition, correlations between brain signals were computed as measures of static functional connectivity. Dynamic connectivity analyses showed that patients with pmTBI spend less time in a frontotemporal default mode/limbic brain state, with no evidence of change as a function of recovery post-injury. Consistent with models showing traumatic strain convergence in deep grey matter and midline regions, static interhemispheric connectivity was affected between the left and right precuneus and thalamus, and between the right supplementary motor area and contralateral cerebellum. Changes in static or dynamic connectivity were not related to symptom burden or injury severity measures, such as loss of consciousness and post-traumatic amnesia. In aggregate, our study shows that brain dynamics are altered up to 4 months after pmTBI, in brain areas that are known to be vulnerable to TBI. Future longitudinal studies are warranted to examine the significance of our findings in terms of long-term neurodevelopment.

Neural correlates of cognitive control deficits in pediatric mild traumatic brain injury.

There is a growing body of research showing that cerebral pathophysiological processes triggered by pediatric mild traumatic brain injury (pmTBI) may extend beyond the usual clinical recovery timeline. It is paramount to further unravel these processes, because the possible long-term cognitive effects resulting from ongoing secondary injury in the developing brain are not known. In the current fMRI study, neural processes related to cognitive control were studied in 181 patients with pmTBI at sub-acute (SA; ~1 week) and early chronic (EC; ~4 months) stages post-injury. Additionally, a group of 162 age- and sex-matched healthy controls (HC) were recruited at equivalent time points. Proactive (post-cue) and reactive (post-probe) cognitive control were examined using a multimodal attention fMRI paradigm for either congruent or incongruent stimuli. To study brain network function, the triple-network model was used, consisting of the executive and salience networks (collectively known as the cognitive control network), and the default mode network. Additionally, whole-brain voxel-wise analyses were performed. Decreased deactivation was found within the default mode network at the EC stage following pmTBI during both proactive and reactive control. Voxel-wise analyses revealed sub-acute hypoactivation of a frontal area of the cognitive control network (left pre-supplementary motor area) during proactive control, with a reversed effect at the EC stage after pmTBI. Similar effects were observed in areas outside of the triple-network during reactive control. Group differences in activation during proactive control were limited to the visual domain, whereas for reactive control findings were more pronounced during the attendance of auditory stimuli. No significant correlations were present between task-related activations and (persistent) post-concussive symptoms. In aggregate, current results show alterations in neural functioning during cognitive control in pmTBI up to 4 months post-injury, regardless of clinical recovery. We propose that subacute decreases in activity reflect a general state of hypo-excitability due to the injury, while early chronic hyperactivation represents a compensatory mechanism to prevent default mode interference and to retain cognitive control.

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist).

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Self-regulation of ventromedial prefrontal cortex activation using real-time fMRI neurofeedback-Influence of default mode network.

The ventromedial prefrontal cortex (vmPFC) is involved in regulation of negative emotion and decision-making, emotional and behavioral control, and active resilient coping. This pilot study examined the feasibility of training healthy subjects (n = 27) to self-regulate the vmPFC activity using a real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf). Participants in the experimental group (EG, n = 18) were provided with an ongoing vmPFC hemodynamic activity (rtfMRI-nf signal represented as variable-height bar). Individuals were instructed to raise the bar by self-relevant value-based thinking. Participants in the control group (CG, n = 9) performed the same task; however, they were provided with computer-generated sham neurofeedback signal. Results demonstrate that (a) both the CG and the EG show a higher vmPFC fMRI signal at the baseline than during neurofeedback training; (b) no significant positive training effect was seen in the vmPFC across neurofeedback runs; however, the medial prefrontal cortex, middle temporal gyri, inferior frontal gyri, and precuneus showed significant decreasing trends across the training runs only for the EG; (c) the vmPFC rtfMRI-nf signal associated with the fMRI signal across the default mode network (DMN). These findings suggest that it may be difficult to modulate a single DMN region without affecting other DMN regions. Observed decreased vmPFC activity during the neurofeedback task could be due to interference from the fMRI signal within other DMN network regions, as well as interaction with task-positive networks. Even though participants in the EG did not show significant positive increase in the vmPFC activity among neurofeedback runs, they were able to learn to accommodate the demand of self-regulation task to maintain the vmPFC activity with the help of a neurofeedback signal.

Real-time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm.

Real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG allows volitional modulation of BOLD activity of target brain regions and investigation of related electrophysiological activity. We applied this approach to study correlations between thalamic BOLD activity and alpha EEG rhythm. Healthy volunteers in the experimental group (EG, n = 15) learned to upregulate BOLD activity of the target region consisting of the mediodorsal (MD) and anterior (AN) thalamic nuclei using rtfMRI-nf during retrieval of happy autobiographical memories. Healthy subjects in the control group (CG, n = 14) were provided with a sham feedback. The EG participants were able to significantly increase BOLD activities of the MD and AN. Functional connectivity between the MD and the inferior precuneus was significantly enhanced during the rtfMRI-nf task. Average individual changes in the occipital alpha EEG power significantly correlated with the average MD BOLD activity levels for the EG. Temporal correlations between the occipital alpha EEG power and BOLD activities of the MD and AN were significantly enhanced, during the rtfMRI-nf task, for the EG compared to the CG. Temporal correlations with the alpha power were also significantly enhanced for the posterior nodes of the default mode network, including the precuneus/posterior cingulate, and for the dorsal striatum. Our findings suggest that the temporal correlation between the MD BOLD activity and posterior alpha EEG power is modulated by the interaction between the MD and the inferior precuneus, reflected in their functional connectivity. Our results demonstrate the potential of the rtfMRI-nf with simultaneous EEG for noninvasive neuromodulation studies of human brain function.

Amygdala real-time functional magnetic resonance imaging neurofeedback for major depressive disorder: A review.

Advances in imaging technologies have allowed for the analysis of functional magnetic resonance imaging data in real-time (rtfMRI), leading to the development of neurofeedback (nf) training. This rtfMRI-nf training utilizes functional magnetic resonance imaging (fMRI) tomographic localization capacity to allow a person to see and regulate the localized hemodynamic signal from his or her own brain. In this review, we summarize the results of several studies that have developed and applied neurofeedback training to healthy and depressed individuals with the amygdala as the neurofeedback target and the goal to increase the hemodynamic response during positive autobiographical memory recall. We review these studies and highlight some of the challenges and advances in developing an rtfMRI-nf paradigm for broader use in psychiatric populations. The work described focuses on our line of research aiming to develop the rtfMRI-nf into an intervention, and includes a discussion of the selection of a region of interest for feedback, selecting a control condition, behavioral and cognitive effects of training, and predicting which participants are most likely to respond well to training. While the results of these studies are encouraging and suggest the clinical potential of amygdala rtfMRI-nf in alleviating symptoms of major depressive disorder, larger studies are warranted to confirm its efficacy.

Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR).

Head motions during functional magnetic resonance imaging (fMRI) impair fMRI data quality and introduce systematic artifacts that can affect interpretation of fMRI results. Electroencephalography (EEG) recordings performed simultaneously with fMRI provide high-temporal-resolution information about ongoing brain activity as well as head movements. Recently, an EEG-assisted retrospective motion correction (E-REMCOR) method was introduced. E-REMCOR utilizes EEG motion artifacts to correct the effects of head movements in simultaneously acquired fMRI data on a slice-by-slice basis. While E-REMCOR is an efficient motion correction approach, it involves an independent component analysis (ICA) of the EEG data and identification of motion-related ICs. Here we report an automated implementation of E-REMCOR, referred to as aE-REMCOR, which we developed to facilitate the application of E-REMCOR in large-scale EEG-fMRI studies. The aE-REMCOR algorithm, implemented in MATLAB, enables an automated preprocessing of the EEG data, an ICA decomposition, and, importantly, an automatic identification of motion-related ICs. aE-REMCOR has been used to perform retrospective motion correction for 305 fMRI datasets from 16 subjects, who participated in EEG-fMRI experiments conducted on a 3T MRI scanner. Performance of aE-REMCOR has been evaluated based on improvement in temporal signal-to-noise ratio (TSNR) of the fMRI data, as well as correction efficiency defined in terms of spike reduction in fMRI motion parameters. The results show that aE-REMCOR is capable of substantially reducing head motion artifacts in fMRI data. In particular, when there are significant rapid head movements during the scan, a large TSNR improvement and high correction efficiency can be achieved. Depending on a subject's motion, an average TSNR improvement over the brain upon the application of aE-REMCOR can be as high as 27%, with top ten percent of the TSNR improvement values exceeding 55%. The average correction efficiency over the 305 fMRI scans is 18% and the largest achieved efficiency is 71%. The utility of aE-REMCOR on the resting state fMRI connectivity of the default mode network is also examined. The motion-induced position-dependent error in the DMN connectivity analysis is shown to be reduced when aE-REMCOR is utilized. These results demonstrate that aE-REMCOR can be conveniently and efficiently used to improve fMRI motion correction in large clinical EEG-fMRI studies.

Contrast enhancement by combining T1- and T2-weighted structural brain MR Images.

PURPOSE: In order to more precisely differentiate cerebral structures in neuroimaging studies, a novel technique for enhancing the tissue contrast based on a combination of T1-weighted (T1w) and T2-weighted (T2w) MRI images was developed. METHODS: The combined image (CI) was calculated as CI = (T1w - sT2w)/(T1w + sT2w), where sT2w is the scaled T2-weighted image. The scaling factor was calculated to adjust the gray- matter (GM) voxel intensities in the T2w image so that their median value equaled that of the GM voxel intensities in the T1w image. The image intensity homogeneity within a tissue and the discriminability between tissues in the CI versus the separate T1w and T2w images were evaluated using the segmentation by the FMRIB Software Library (FSL) and FreeSurfer (Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital, Boston, MA) software. RESULTS: The combined image significantly improved homogeneity in the white matter (WM) and GM compared to the T1w images alone. The discriminability between WM and GM also improved significantly by applying the CI approach. Significant enhancements to the homogeneity and discriminability also were achieved in most subcortical nuclei tested, with the exception of the amygdala and the thalamus. CONCLUSION: The tissue discriminability enhancement offered by the CI potentially enables more accurate neuromorphometric analyses of brain structures.

Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback.

Neurofeedback is a promising approach for non-invasive modulation of human brain activity with applications for treatment of mental disorders and enhancement of brain performance. Neurofeedback techniques are commonly based on either electroencephalography (EEG) or real-time functional magnetic resonance imaging (rtfMRI). Advances in simultaneous EEG-fMRI have made it possible to combine the two approaches. Here we report the first implementation of simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI-EEG-nf). It is based on a novel system for real-time integration of simultaneous rtfMRI and EEG data streams. We applied the rtfMRI-EEG-nf to training of emotional self-regulation in healthy subjects performing a positive emotion induction task based on retrieval of happy autobiographical memories. The participants were able to simultaneously regulate their BOLD fMRI activation in the left amygdala and frontal EEG power asymmetry in the high-beta band using the rtfMRI-EEG-nf. Our proof-of-concept results demonstrate the feasibility of simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG) activities of the human brain. They suggest potential applications of rtfMRI-EEG-nf in the development of novel cognitive neuroscience research paradigms and enhanced cognitive therapeutic approaches for major neuropsychiatric disorders, particularly depression.

Multifaceted neural and vascular pathologies after pediatric mild traumatic brain injury.

Dynamic changes in neurodevelopment and cognitive functioning occur during adolescence, including a switch from reactive to more proactive forms of cognitive control, including response inhibition. Pediatric mild traumatic brain injury (pmTBI) affects these cognitions immediately post-injury, but the role of vascular versus neural injury in cognitive dysfunction remains debated. This study consecutively recruited 214 sub-acute pmTBI (8-18 years) and age/sex-matched healthy controls (HC; N = 186), with high retention rates (>80%) at four months post-injury. Multimodal imaging (functional MRI during response inhibition, cerebral blood flow and cerebrovascular reactivity) assessed for pathologies within the neurovascular unit. Patients exhibited increased errors of commission and hypoactivation of motor circuitry during processing of probes. Evidence of increased/delayed cerebrovascular reactivity within motor circuitry during hypercapnia was present along with normal perfusion. Neither age-at-injury nor post-concussive symptom load were strongly associated with imaging abnormalities. Collectively, mild cognitive impairments and clinical symptoms may continue up to four months post-injury. Prolonged dysfunction within the neurovascular unit was observed during proactive response inhibition, with preliminary evidence that neural and pure vascular trauma are statistically independent. These findings suggest pmTBI is characterized by multifaceted pathologies during the sub-acute injury stage that persist several months post-injury.

Correlated slow fluctuations in respiration, EEG, and BOLD fMRI.

Low-frequency temporal fluctuations of physiological signals (<0.1 Hz), such as the respiration and cardiac pulse rate, occur naturally during rest and have been shown to be correlated with blood-oxygenation-level-dependent (BOLD) signal fluctuation. Such physiological signal modulations have been considered as sources of noise and their effects on BOLD signal are commonly removed in functional magnetic resonance imaging (fMRI) studies. However, possible neural correlates of the physiological fluctuations have not been considered nor examined in detail. In the present study we investigated this possibility by simultaneously acquiring electroencephalogram (EEG) with BOLD fMRI data, respiratory and cardiac waveforms in healthy human subjects at eyes-closed and eyes-open resting. We quantified the concurrent changes of the EEG power in the alpha frequency band, the respiration volume, and the cardiac pulse rate, then assessed the temporal correlations between alpha EEG power and physiological signal fluctuations. In addition, time-shifted time courses of alpha EEG power or physiological data were included as regressors to examine their correlations with the whole-brain BOLD fMRI signals. We observed a significant correlation between alpha EEG global field power and respiration, particularly at eyes-closed resting condition. Similar spatial patterns were observed between the correlation maps of BOLD with alpha EEG power and respiration, with negative correlations coinciding in the visual cortex, superior/middle temporal gyrus, inferior frontal gyrus, and inferior parietal lobule and positive correlations in the thalamus and caudate. Regressing out the physiological variations in the BOLD signal resulted in reduced correlation between BOLD and alpha EEG power. These results suggest a mutual link of neuronal origin between alpha EEG power, respiration, and BOLD signals.

The propensity to sign-track is associated with externalizing behavior and distinct patterns of reward-related brain activation in youth.

Externalizing behaviors in childhood often predict impulse control disorders in adulthood; however, the underlying bio-behavioral risk factors are incompletely understood. In animals, the propensity to sign-track, or the degree to which incentive motivational value is attributed to reward cues, is associated with externalizing-type behaviors and deficits in executive control. Using a Pavlovian conditioned approach paradigm, we quantified sign-tracking in 40 healthy 9-12-year-olds. We also measured parent-reported externalizing behaviors and anticipatory neural activations to outcome-predicting cues using the monetary incentive delay fMRI task. Sign-tracking was associated with attentional and inhibitory control deficits and the degree of amygdala, but not cortical, activation during reward anticipation. These findings support the hypothesis that youth with a propensity to sign-track are prone to externalizing tendencies, with an over-reliance on subcortical cue-reactive brain systems. This research highlights sign-tracking as a promising experimental approach delineating the behavioral and neural circuitry of individuals at risk for externalizing disorders.

Prospective Study of Gray Matter Atrophy Following Pediatric Mild Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: The clinical and physiologic time course for recovery following pediatric mild traumatic brain injury (pmTBI) remains actively debated. The primary objective of the current study was to prospectively examine structural brain changes (cortical thickness and subcortical volumes) and age-at-injury effects. A priori study hypotheses predicted reduced cortical thickness and hippocampal volumes up to 4 months postinjury, which would be inversely associated with age at injury. METHODS: Prospective cohort study design with consecutive recruitment. Study inclusion adapted from American Congress of Rehabilitation Medicine (upper threshold) and Zurich Concussion in Sport Group (minimal threshold) and diagnosed by Emergency Department and Urgent Care clinicians. Major neurologic, psychiatric, or developmental disorders were exclusionary. Clinical (Common Data Element) and structural (3 T MRI) evaluations within 11 days (subacute visit [SA]) and at 4 months (early chronic visit [EC]) postinjury. Age- and sex-matched healthy controls (HC) to control for repeat testing/neurodevelopment. Clinical outcomes based on self-report and cognitive testing. Structural images quantified with FreeSurfer (version 7.1.1). RESULTS: A total of 208 patients with pmTBI (age = 14.4 ± 2.9; 40.4% female) and 176 HC (age = 14.2 ± 2.9; 42.0% female) were included in the final analyses (>80% retention). Reduced cortical thickness (right rostral middle frontal gyrus; d = -0.49) and hippocampal volumes (d = -0.24) observed for pmTBI, but not associated with age at injury. Hippocampal volume recovery was mediated by loss of consciousness/posttraumatic amnesia. Significantly greater postconcussive symptoms and cognitive deficits were observed at SA and EC visits, but were not associated with the structural abnormalities. Structural abnormalities slightly improved balanced classification accuracy above and beyond clinical gold standards (∆+3.9%), with a greater increase in specificity (∆+7.5%) relative to sensitivity (∆+0.3%). DISCUSSION: Current findings indicate that structural brain abnormalities may persist up to 4 months post-pmTBI and are partially mediated by initial markers of injury severity. These results contribute to a growing body of evidence suggesting prolonged physiologic recovery post-pmTBI. In contrast, there was no evidence for age-at-injury effects or physiologic correlates of persistent symptoms in our sample.

EEG-assisted retrospective motion correction for fMRI: E-REMCOR.

We propose a method for retrospective motion correction of fMRI data in simultaneous EEG-fMRI that employs the EEG array as a sensitive motion detector. EEG motion artifacts are used to generate motion regressors describing rotational head movements with millisecond temporal resolution. These regressors are utilized for slice-specific motion correction of unprocessed fMRI data. Performance of the method is demonstrated by correction of fMRI data from five patients with major depressive disorder, who exhibited head movements by 1-3mm during a resting EEG-fMRI run. The fMRI datasets, corrected using eight to ten EEG-based motion regressors, show significant improvements in temporal SNR (TSNR) of fMRI time series, particularly in the frontal brain regions and near the surface of the brain. The TSNR improvements are as high as 50% for large brain areas in single-subject analysis and as high as 25% when the results are averaged across the subjects. Simultaneous application of the EEG-based motion correction and physiological noise correction by means of RETROICOR leads to average TSNR enhancements as high as 35% for extended brain regions. These TSNR improvements are largely preserved after the subsequent fMRI volume registration and regression of fMRI motion parameters. The proposed EEG-assisted method of retrospective fMRI motion correction (referred to as E-REMCOR) can be applied to improve quality of fMRI data with severe motion artifacts and to reduce spurious correlations between the EEG and fMRI data caused by head movements. It does not require any specialized equipment beyond the standard EEG-fMRI instrumentation and can be applied retrospectively to any existing EEG-fMRI data set.

Spatiotemporal dynamics of the brain at rest--exploring EEG microstates as electrophysiological signatures of BOLD resting state networks.

Neuroimaging research suggests that the resting cerebral physiology is characterized by complex patterns of neuronal activity in widely distributed functional networks. As studied using functional magnetic resonance imaging (fMRI) of the blood-oxygenation-level dependent (BOLD) signal, the resting brain activity is associated with slowly fluctuating hemodynamic signals (~10s). More recently, multimodal functional imaging studies involving simultaneous acquisition of BOLD-fMRI and electroencephalography (EEG) data have suggested that the relatively slow hemodynamic fluctuations of some resting state networks (RSNs) evinced in the BOLD data are related to much faster (~100 ms) transient brain states reflected in EEG signals, that are referred to as "microstates". To further elucidate the relationship between microstates and RSNs, we developed a fully data-driven approach that combines information from simultaneously recorded, high-density EEG and BOLD-fMRI data. Using independent component analysis (ICA) of the combined EEG and fMRI data, we identified thirteen microstates and ten RSNs that are organized independently in their temporal and spatial characteristics, respectively. We hypothesized that the intrinsic brain networks that are active at rest would be reflected in both the EEG data and the fMRI data. To test this hypothesis, the rapid fluctuations associated with each microstate were correlated with the BOLD-fMRI signal associated with each RSN. We found that each RSN was characterized further by a specific electrophysiological signature involving from one to a combination of several microstates. Moreover, by comparing the time course of EEG microstates to that of the whole-brain BOLD signal, on a multi-subject group level, we unraveled for the first time a set of microstate-associated networks that correspond to a range of previously described RSNs, including visual, sensorimotor, auditory, attention, frontal, visceromotor and default mode networks. These results extend our understanding of the electrophysiological signature of BOLD RSNs and demonstrate the intrinsic connection between the fast neuronal activity and slow hemodynamic fluctuations.

Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets.

Background/Introduction: Sex classification using functional connectivity from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results. This suggested that sex difference might also be embedded in the blood-oxygen-level-dependent properties such as the amplitude of low-frequency fluctuation (ALFF) and the fraction of ALFF (fALFF). This study comprehensively investigates sex differences using a reliable and explainable machine learning (ML) pipeline. Five independent cohorts of rs-fMRI with over than 5500 samples were used to assess sex classification performance and map the spatial distribution of the important brain regions. Methods: Five rs-fMRI samples were used to extract ALFF and fALFF features from predefined brain parcellations and then were fed into an unbiased and explainable ML pipeline with a wide range of methods. The pipeline comprehensively assessed unbiased performance for within-sample and across-sample validation. In addition, the parcellation effect, classifier selection, scanning length, spatial distribution, reproducibility, and feature importance were analyzed and evaluated thoroughly in the study. Results: The results demonstrated high sex classification accuracies from healthy adults (area under the curve >0.89), while degrading for nonhealthy subjects. Sex classification showed moderate to good intraclass correlation coefficient based on parcellation. Linear classifiers outperform nonlinear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 min). The spatial distribution of important features overlaps with previous results from studies. Discussion: Sex differences are consistent in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation. Features that discriminate males and females were found to be distributed across several different brain regions, suggesting a complex mosaic for sex differences in rs-fMRI. Impact statement The presented study unraveled that sex differences are embedded in the blood-oxygen-level dependent (BOLD) and can be predicted using unbiased and explainable machine learning pipeline. The study revealed that psychiatric disorders and demographics might influence the BOLD signal and interact with the classification of sex. The spatial distribution of the important features presented here supports the notion that the brain is a mosaic of male and female features. The findings emphasize the importance of controlling for sex when conducting brain imaging analysis. In addition, the presented framework can be adapted to classify other variables from resting-state BOLD signals.

Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal.

Objective.Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60-120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear.Approach. In a cohort of healthy subjects (n= 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches.Main results.Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa.Significance.Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.

Hippocampal volume recovery with real-time functional MRI amygdala neurofeedback emotional training for posttraumatic stress disorder.

BACKGROUND: Small hippocampal volume is a prevalent neurostructural abnormality in posttraumatic stress disorder (PTSD). However, whether the hippocampal atrophy is the cause of disease symptoms or a pre-existing risk factor and whether it is a reversible alteration or a permanent trait are unclear. The trait- or state-dependent alteration could also differ among the hippocampal subfields. METHODS: The study examined the longitudinal hippocampal volume changes due to positive emotional training with left amygdala (LA) real-time fMRI neurofeedback (rtfMRI-nf) in combat veterans with PTSD. The participants were trained to increase the neurofeedback signal from LA (experimental group, N = 20) or brain region not involved in emotion processing (control group, N = 9) by recalling a positive autobiographical memory. The pre- and post-training structural MRI brain images were processed with FreeSurfer to evaluate the hippocampal subfield volumes. Hippocampal volumes for healthy controls (N = 43) were also examined to evaluate the baseline abnormality in PTSD. RESULTS: A significant group difference in volume change was found in the left CA1 head region. This region had the most significant volume reduction at the baseline in PTSD. The experimental group showed a significant volume increase, while the control group showed a significant volume decrease in this region. The volume change in the control group negatively correlated with interval days between the scans. LIMITATIONS: A cognitive improvement due to the hippocampal volume increase could not be found with symptom scales. CONCLUSIONS: RtfMRI-nf positive emotional training increased the hippocampus volume among people with PTSD, suggesting that hippocampal atrophy in PTSD is modifiable.

Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI.

Objective.Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR).Approach.The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs);n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database.Main results.We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data.Significance.APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters' preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.