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Masaya Misaki

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YNICL Journal 2021 Journal Article

Real-time fMRI neurofeedback amygdala training may influence kynurenine pathway metabolism in major depressive disorder

  • Aki Tsuchiyagaito
  • Jared L. Smith
  • Nour El-Sabbagh
  • Vadim Zotev
  • Masaya Misaki
  • Obada Al Zoubi
  • T. Kent Teague
  • Martin P. Paulus

Real-time fMRI neurofeedback (rtfMRI-nf) left amygdala (LA) training is a promising intervention for major depressive disorder (MDD). We have previously proposed that rtfMRI-nf LA training may reverse depression-associated regional impairments in neuroplasticity and restore information flow within emotion-regulating neural circuits. Inflammatory cytokines as well as the neuroactive metabolites of an immunoregulatory pathway, i.e. the kynurenine pathway (KP), have previously been implicated in neuroplasticity. Therefore, in this proof-of-principle study, we investigated the association between rtfMRI-nf LA training and circulating inflammatory mediators and KP metabolites. Based on our previous work, the primary variable of interest was the ratio of the NMDA-receptor antagonist, kynurenic acid to the NMDA receptor agonist, quinolinic acid (KynA/QA), a putative neuroprotective index. We tested two main hypotheses. i. Whether rtfMRI-nf acutely modulates KynA/QA, and ii. whether baseline KynA/QA predicts response to rtfMRI-nf. Twenty-nine unmedicated participants who met DSM-5 criteria for MDD based on the Mini-International Neuropsychiatric Interview and had current depressive symptoms (Montgomery-Åsberg Depression Rating Scale (MADRS) score > 6) completed two rtfMRI-nf sessions to upregulate LA activity (Visit1 and 2), as well as a follow-up (Visit3) without rtfMRI-nf. All visits occurred at two-week intervals. At all three visits, the MADRS was administered to participants and serum samples for the quantification of inflammatory cytokines and KP metabolites were obtained. First, the longitudinal changes in the MADRS score and immune markers were tested by linear mixed effect model analysis. Further, utilizing a linear regression model, we investigated the relationship between rtfMRI-nf performance and immune markers. After two sessions of rtfMRI-nf, MADRS scores were significantly reduced (t[58] = -4.07, p = 0.009, d = 0.56). Thirteen participants showed a ≥ 25% reduction in the MADRS score (the partial responder group). There was a significant effect of visit (F[2,58] = 3.17, p = 0.05) for the neuroprotective index, KynA to 3-hydroxykynurenine (3-HK), that was driven by a significant increase in KynA/3-HK between Visit1 and Visit3 (t[58] = 2.50, p = 0.03, d = 0.38). A higher baseline level of KynA/QA (β = 5.23, p = 0.06; rho = 0.49, p = 0.02) was associated with greater ability to upregulate the LA. Finally, for exploratory purposes correlation analyses were performed between the partial responder and the non-responder groups as well as in the whole sample including all KP metabolites and cytokines. In the partial responder group, greater ability to upregulate the LA was correlated with an increase in KynA/QA after rtfMRI-nf (rho = 0.75, p = 0.03). The results are consistent with the possibility that rtfMRI-nf decreases metabolism down the so-called neurotoxic branch of the KP. Nevertheless, non-specific effects cannot be ruled out due to the lack of a sham control. Future, controlled studies are needed to determine whether the increase in KynA/3HK and KynA/QA is specific to rtfMRI-nf or whether it is a non-specific correlate of the resolution of depressive symptoms. Similarly, replication studies are needed to determine whether KynA/QA has clinical utility as a treatment response biomarker.

YNICL Journal 2020 Journal Article

Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention

  • Masaya Misaki
  • Aki Tsuchiyagaito
  • Obada Al Zoubi
  • Martin Paulus
  • Jerzy Bodurka

Real-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N = 223) and healthy controls (N = 45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (-6, -54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, -49, 23 mm) was positively correlated with RRS scores (depressive, p < 0.001; brooding, p < 0.001; reflective, p = 0.002) in the mood and anxiety disorder group. We then performed a rtfMRI processing simulation to optimize the online computation of the precuneus-RTPJ connectivity. We determined that the two-point method without a control region was appropriate as a functional connectivity neurofeedback signal with less dependence on signal history and its accommodation of head motion. The present study offers a discovery framework for the precise location of functional connectivity targets for rtfMRI-nf intervention, which could help directly translate neuroimaging findings into clinical rtfMRI-nf interventions.

YNICL Journal 2020 Journal Article

Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback

  • Vadim Zotev
  • Ahmad Mayeli
  • Masaya Misaki
  • Jerzy Bodurka

Simultaneous real-time fMRI and EEG neurofeedback (rtfMRI-EEG-nf) is an emerging neuromodulation approach, that enables simultaneous volitional regulation of both hemodynamic (BOLD fMRI) and electrophysiological (EEG) brain activities. Here we report the first application of rtfMRI-EEG-nf for emotion self-regulation training in patients with major depressive disorder (MDD). In this proof-of-concept study, MDD patients in the experimental group (n = 16) used rtfMRI-EEG-nf during a happy emotion induction task to simultaneously upregulate two fMRI and two EEG activity measures relevant to MDD. The target measures included BOLD activities of the left amygdala (LA) and left rostral anterior cingulate cortex (rACC), and frontal EEG asymmetries in the alpha band (FAA, [7.5-12.5] Hz) and high-beta band (FBA, [21-30] Hz). MDD patients in the control group (n = 8) were provided with sham feedback signals. An advanced procedure for improved real-time EEG-fMRI artifact correction was implemented. The experimental group participants demonstrated significant upregulation of the LA BOLD activity, FAA, and FBA during the rtfMRI-EEG-nf task, as well as significant enhancement in fMRI connectivity between the LA and left rACC. Average individual FAA changes during the rtfMRI-EEG-nf task positively correlated with depression and anhedonia severities, and negatively correlated with after-vs-before changes in depressed mood ratings. Temporal correlations between the FAA and FBA time courses and the LA BOLD activity were significantly enhanced during the rtfMRI-EEG-nf task. The experimental group participants reported significant mood improvements after the training. Our results suggest that the rtfMRI-EEG-nf may have potential for treatment of MDD.

YNICL Journal 2019 Journal Article

Brain activity mediators of PTSD symptom reduction during real-time fMRI amygdala neurofeedback emotional training

  • Masaya Misaki
  • Raquel Phillips
  • Vadim Zotev
  • Chung-Ki Wong
  • Brent E. Wurfel
  • Frank Krueger
  • Matthew Feldner
  • Jerzy Bodurka

Self-regulation of brain activation with real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) is emerging as a promising treatment for psychiatric disorders. The association between the regulation and symptom reduction, however, has not been consistent, and the mechanisms underlying the symptom reduction remain poorly understood. The present study investigated brain activity mediators of the amygdala rtfMRI-nf training effect on combat veterans' PTSD symptom reduction. The training was designed to increase a neurofeedback signal either from the left amygdala (experimental group; EG) or from a control region not implicated in emotion regulation (control group; CG) during positive autobiographical memory recall. We employed a structural equation model mapping analysis to identify brain regions that mediated the effects of the rtfMRI-nf training on PTSD symptoms. Symptom reduction was mediated by low activation in the dorsomedial prefrontal cortex (DMPFC) and the middle cingulate cortex. There was a trend toward less activation in these regions for the EG compared to the CG. Low activation in the precuneus, the right superior parietal, the right insula, and the right cerebellum also mediated symptom reduction while their effects were moderated by the neurofeedback signal; a higher signal was linked to less effect on symptom reduction. This moderation was not specific to the EG. MDD comorbidity was associated with high DMPFC activation, which resulted in less effective regulation of the feedback signal. These results indicated that symptom reduction due to the neurofeedback training was not specifically mediated by the neurofeedback target activity, but broad regions were involved in the process.

YNICL Journal 2018 Journal Article

Altered task-based and resting-state amygdala functional connectivity following real-time fMRI amygdala neurofeedback training in major depressive disorder

  • Kymberly D. Young
  • Greg J. Siegle
  • Masaya Misaki
  • Vadim Zotev
  • Raquel Phillips
  • Wayne C. Drevets
  • Jerzy Bodurka

Background: We have previously shown that in participants with major depressive disorder (MDD) trained to upregulate their amygdala hemodynamic response during positive autobiographical memory (AM) recall with real-time fMRI neurofeedback (rtfMRI-nf) training, depressive symptoms diminish. Here, we assessed the effect of rtfMRI-nf on amygdala functional connectivity during both positive AM recall and rest. Method: The current manuscript consists of a secondary analysis on data from our published clinical trial of neurofeedback. Patients with MDD completed two rtfMRI-nf sessions (18 received amygdala rtfMRI-nf, 16 received control parietal rtfMRI-nf). One-week prior-to and following training participants also completed a resting-state fMRI scan. A GLM-based functional connectivity analysis was applied using a seed ROI in the left amygdala. We compared amygdala functional connectivity changes while recalling positive AMs from the baseline run to the final transfer run during rtfMRI-nf training, as well during rest from the baseline to the one-week follow-up visit. Finally, we assessed the correlation between change in depression scores and change in amygdala connectivity, as well as correlations between amygdala regulation success and connectivity changes. Results: Following training, amygdala connectivity during positive AM recall increased with widespread regions in the frontal and limbic network. During rest, amygdala connectivity increased following training within the fronto-temporal-limbic network. During both task and resting-state analyses, amygdala-temporal pole connectivity decreased. We identified increased amygdala-precuneus and amygdala-inferior frontal gyrus connectivity during positive memory recall and increased amygdala-precuneus and amygdala-thalamus connectivity during rest as functional connectivity changes that explained significant variance in symptom improvement. Amygdala-precuneus connectivity changes also explain a significant amount of variance in neurofeedback regulation success. Conclusions: Neurofeedback training to increase amygdala hemodynamic activity during positive AM recall increased amygdala connectivity with regions involved in self-referential, salience, and reward processing. Results suggest future targets for neurofeedback interventions, particularly interventions involving the precuneus.

YNICL Journal 2018 Journal Article

Connectome-wide investigation of altered resting-state functional connectivity in war veterans with and without posttraumatic stress disorder

  • Masaya Misaki
  • Raquel Phillips
  • Vadim Zotev
  • Chung-Ki Wong
  • Brent E. Wurfel
  • Frank Krueger
  • Matthew Feldner
  • Jerzy Bodurka

Altered resting-state functional connectivity in posttraumatic stress disorder (PTSD) suggests neuropathology of the disorder. While seed-based fMRI connectivity analysis is often used for the studies, such analysis requires defining a seed location a priori, which restricts search scope and could bias findings toward presupposed areas. Recently, a comprehensive exploratory voxel-wise connectivity analysis, the connectome-wide association approach, has been introduced using multivariate distance matrix regression (MDMR) for resting-state functional connectivity analysis. The current study performed a connectome-wide investigation of resting-state functional connectivity for war veterans with and without PTSD compared to non-trauma-exposed healthy controls using MDMR. Thirty-five male combat veterans with PTSD (unmedicated), 18 male combat veterans without PTSD (veterans control, VC), and 28 age-matched non-trauma-exposed healthy males (NC) participated in a resting-state fMRI scan. MDMR analysis was used to identify between-groups differences in regions with altered connectivity. The identified regions were used as a seed for post-hoc functional connectivity analysis. The analysis revealed that PTSD patients had hypoconnectivity between the left lateral prefrontal regions and the salience network regions as well as hypoconnectivity between the parahippocampal gyrus and the visual cortex areas. Connectivity between the ventromedial prefrontal cortex and the middle frontal gyrus and between the parahippocampal gyrus and the anterior insula were negatively correlated with PTSD symptom severity. VC subjects also had altered functional connectivity compared to NC, including increased connectivity between the posterior insula and several brain regions and decreased connectivity between the precuneus region and several other brain areas. The decreased connectivity between the lateral prefrontal regions and the salience network regions in PTSD was consistent with previous reports that indicated lowered emotion-regulation function in these regions. The decreased connectivity between the parahippocampal gyrus and visual cortex supported the dual representation theory of PTSD, which suggests dissociation between sensory and contextual memory representations in PTSD. The theory also supposes that the precuneus is a region that triggers retrieval of sensory memory of traumatic events. The decreased connectivity at the precuneus for VC might be associated with suppressing such a process.

YNICL Journal 2018 Journal Article

Real-time fMRI amygdala neurofeedback positive emotional training normalized resting-state functional connectivity in combat veterans with and without PTSD: a connectome-wide investigation

  • Masaya Misaki
  • Raquel Phillips
  • Vadim Zotev
  • Chung-Ki Wong
  • Brent E. Wurfel
  • Frank Krueger
  • Matthew Feldner
  • Jerzy Bodurka

Self-regulation of brain activation using real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) is an emerging approach for treating mood and anxiety disorders. The effect of neurofeedback training on resting-state functional connectivity warrants investigation as changes in spontaneous brain activation could reflect the association between sustained symptom relief and brain alteration. We investigated the effect of amygdala-focused rtfMRI-nf training on resting-state functional connectivity in combat veterans with and without posttraumatic stress disorder (PTSD) who were trained to increase a feedback signal reflecting left amygdala activity while recalling positive autobiographical memories (Zotev et al., 2018). The analysis was performed in three stages: i) first, we investigated the connectivity in the left amygdala region; ii) next, we focused on the abnormal resting-state functional connectivity identified in our previous analysis of this data (Misaki et al., 2018); and iii) finally, we performed a novel data-driven longitudinal connectome-wide analysis. We introduced a longitudinal multivariate distance matrix regression (MDMR) analysis to comprehensively examine neurofeedback training effects beyond those associated with abnormal baseline connectivity. These comprehensive exploratory analyses suggested that abnormal resting-state connectivity for combat veterans with PTSD was partly normalized after the training. This included hypoconnectivities between the left amygdala and the left ventrolateral prefrontal cortex (vlPFC) and between the supplementary motor area (SMA) and the dorsal anterior cingulate cortex (dACC). The increase of SMA-dACC connectivity was associated with PTSD symptom reduction. Longitudinal MDMR analysis found a connectivity change between the precuneus and the left superior frontal cortex. The connectivity increase was associated with a decrease in hyperarousal symptoms. The abnormal connectivity for combat veterans without PTSD - such as hypoconnectivity in the precuneus with a superior frontal region and hyperconnectivity in the posterior insula with several regions - could also be normalized after the training. These results suggested that the rtfMRI-nf training effect was not limited to a feedback target region and symptom relief could be mediated by brain modulation in several regions other than in a feedback target area. While further confirmatory research is needed, the results may provide valuable insight into treatment effects on the whole brain resting-state connectivity.

YNICL Journal 2018 Journal Article

Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD

  • Vadim Zotev
  • Raquel Phillips
  • Masaya Misaki
  • Chung Ki Wong
  • Brent E. Wurfel
  • Frank Krueger
  • Matthew Feldner
  • Jerzy Bodurka

Posttraumatic stress disorder (PTSD) is a chronic and disabling neuropsychiatric disorder characterized by insufficient top-down modulation of the amygdala activity by the prefrontal cortex. Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging method with potential for modifying the amygdala-prefrontal interactions. We report the first controlled emotion self-regulation study in veterans with combat-related PTSD utilizing rtfMRI-nf of the amygdala activity. PTSD patients in the experimental group (EG, n = 20) learned to upregulate blood‑oxygenation-level-dependent (BOLD) activity of the left amygdala (LA) using the rtfMRI-nf during a happy emotion induction task. PTSD patients in the control group (CG, n = 11) were provided with a sham rtfMRI-nf. The study included three rtfMRI-nf training sessions, and EEG recordings were performed simultaneously with fMRI. PTSD severity was assessed before and after the training using the Clinician-Administered PTSD Scale (CAPS). The EG participants who completed the study showed a significant reduction in total CAPS ratings, including significant reductions in avoidance and hyperarousal symptoms. They also exhibited a significant reduction in comorbid depression severity. Overall, 80% of the EG participants demonstrated clinically meaningful reductions in CAPS ratings, compared to 38% in the CG. No significant difference in the CAPS rating changes was observed between the groups. During the first rtfMRI-nf session, functional connectivity of the LA with the orbitofrontal cortex (OFC) and the dorsolateral prefrontal cortex (DLPFC) was progressively enhanced, and this enhancement significantly and positively correlated with the initial CAPS ratings. Left-lateralized enhancement in upper alpha EEG coherence also exhibited a significant positive correlation with the initial CAPS. Reduction in PTSD severity between the first and last rtfMRI-nf sessions significantly correlated with enhancement in functional connectivity between the LA and the left DLPFC. Our results demonstrate that the rtfMRI-nf of the amygdala activity has the potential to correct the amygdala-prefrontal functional connectivity deficiencies specific to PTSD.

YNICL Journal 2018 Journal Article

Tracking resting state connectivity dynamics in veterans with PTSD

  • Han Yuan
  • Raquel Phillips
  • Chung Ki Wong
  • Vadim Zotev
  • Masaya Misaki
  • Brent Wurfel
  • Frank Krueger
  • Matthew Feldner

Posttraumatic stress disorder (PTSD) is a trauma- and stressor-related disorder that may emerge following a traumatic event. Neuroimaging studies have shown evidence of functional abnormality in many brain regions and systems affected by PTSD. Exaggerated threat detection associated with abnormalities in the salience network, as well as abnormalities in executive functions involved in emotions regulations, self-referencing and context evaluation processing are broadly reported in PTSD. Here we aimed to investigate the behavior and dynamic properties of fMRI resting state networks in combat-related PTSD, using a novel, multimodal imaging approach. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) was employed to measure neurobiological brain activity among 36 veterans with combat-related PTSD and 20 combat-exposed veterans without PTSD. Based on the recently established method of measuring temporal-independent EEG microstates, we developed a novel strategy to integrate EEG and fMRI by quantifying the fast temporal dynamics associated with the resting state networks. We found distinctive occurrence rates of microstates associated with the dorsal default mode network and salience networks in the PTSD group as compared with control. Furthermore, the occurrence rate of the microstate for the dorsal default mode network was positively correlated with PTSD severity, whereas the occurrence rate of the microstate for the anterior salience network was negatively correlated with hedonic tone reported by participants with PTSD. Our findings reveal a novel aspect of abnormal network dynamics in combat-related PTSD and contribute to a better understanding of the pathophysiology of the disorder. Simultaneous EEG and fMRI will be a valuable tool in continuing to study the neurobiology underlying PTSD.

YNIMG Journal 2016 Journal Article

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

  • Chung-Ki Wong
  • Vadim Zotev
  • Masaya Misaki
  • Raquel Phillips
  • Qingfei Luo
  • Jerzy Bodurka

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.

YNICL Journal 2016 Journal Article

Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression

  • Vadim Zotev
  • Han Yuan
  • Masaya Misaki
  • Raquel Phillips
  • Kymberly D. Young
  • Matthew T. Feldner
  • Jerzy Bodurka

Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging approach for studies and novel treatments of major depressive disorder (MDD). EEG performed simultaneously with an rtfMRI-nf procedure allows an independent evaluation of rtfMRI-nf brain modulation effects. Frontal EEG asymmetry in the alpha band is a widely used measure of emotion and motivation that shows profound changes in depression. However, it has never been directly related to simultaneously acquired fMRI data. We report the first study investigating electrophysiological correlates of the rtfMRI-nf procedure, by combining the rtfMRI-nf with simultaneous and passive EEG recordings. In this pilot study, MDD patients in the experimental group (n = 13) learned to upregulate BOLD activity of the left amygdala using an rtfMRI-nf during a happy emotion induction task. MDD patients in the control group (n = 11) were provided with a sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper alpha band and BOLD activity across the brain were examined. Average individual changes in frontal EEG asymmetry during the rtfMRI-nf task for the experimental group showed a significant positive correlation with the MDD patients' depression severity ratings, consistent with an inverse correlation between the depression severity and frontal EEG asymmetry at rest. The average asymmetry changes also significantly correlated with the amygdala BOLD laterality. Temporal correlations between frontal EEG asymmetry and BOLD activity were significantly enhanced, during the rtfMRI-nf task, for the amygdala and many regions associated with emotion regulation. Our findings demonstrate an important link between amygdala BOLD activity and frontal EEG asymmetry during emotion regulation. Our EEG asymmetry results indicate that the rtfMRI-nf training targeting the amygdala is beneficial to MDD patients. They further suggest that EEG-nf based on frontal EEG asymmetry in the alpha band would be compatible with the amygdala-based rtfMRI-nf. Combination of the two could enhance emotion regulation training and benefit MDD patients.

YNIMG Journal 2014 Journal Article

Network-dependent modulation of brain activity during sleep

  • Takamitsu Watanabe
  • Shigeyuki Kan
  • Takahiko Koike
  • Masaya Misaki
  • Seiki Konishi
  • Satoru Miyauchi
  • Yasushi Miyahsita
  • Naoki Masuda

Brain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy model to brain activity obtained by functional magnetic resonance imaging from sleeping healthy subjects. We found that the brain activity of individual brain regions and functional interactions between pairs of regions significantly increased in the default-mode network during SWS and decreased during REM sleep. In contrast, the network activity of the fronto-parietal and sensory-motor networks showed the opposite pattern. Furthermore, in the three networks, the amount of the activity changes throughout REM sleep was negatively correlated with that throughout SWS. The present findings suggest that the brain activity is dynamically modulated even in a sleep stage and that the pattern of modulation depends on the type of the large-scale brain networks.

YNIMG Journal 2014 Journal Article

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

  • Vadim Zotev
  • Raquel Phillips
  • Han Yuan
  • Masaya Misaki
  • Jerzy Bodurka

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.

YNIMG Journal 2013 Journal Article

Accurate decoding of sub-TR timing differences in stimulations of sub-voxel regions from multi-voxel response patterns

  • Masaya Misaki
  • Wen-Ming Luh
  • Peter A. Bandettini

We investigated the decoding of ocular dominance stimulations with millisecond-order timing difference from the blood oxygen level dependent (BOLD) signal in human functional magnetic resonance imaging (fMRI). In our experiment, ocular dominance columns were activated by monocular visual stimulation with 500- or 100- ms onset differences. We observed that the event-related hemodynamic response (HDR) in the human visual cortex was sensitive to the subtle onset difference. The HDR shapes were related to the stimulus timings in various manners: the timing difference was represented in either the amplitude of positive peak, amplitude of negative peak, delay of peak time, or response duration of HDR. These complex relationships were different across voxels and subjects. To find an informative feature of HDR for discriminating the subtle timing difference of ocular dominance stimulations, we examined various characteristics of HDR including response amplitude, time to peak, full width at half-maximum response, as inputs for decoding analysis. Using a canonical HDR function for estimating the voxel's response did not yield good decoding scores, suggesting that information may reside in the variability of HDR shapes. Using all the values from the deconvolved HDR also showed low performance, which could be due to an over-fitting problem with the large data dimensionality. When using either positive or negative peak amplitude of the deconvolved HDR, high decoding performance could be achieved for both the 500ms and the 100ms onset differences. The high accuracy even for the 100ms difference, given that the signal was sampled at a TR of 250ms and 2×2×3-mm voxels, implies a possibility of spatiotemporally hyper-resolution decoding. Furthermore, both down-sampling and smoothing did not affect the decoding accuracies very much. These results suggest a complex spatiotemporal relationship between the multi-voxel pattern of the BOLD response and the population activation of neuronal columns. The demonstrated possibility of decoding stimulations for columnar-level organization with 100-ms onset difference using lower resolution imaging data may broaden the scope of application of the BOLD fMRI.

YNIMG Journal 2012 Journal Article

Characteristic cortical thickness patterns in adolescents with autism spectrum disorders: Interactions with age and intellectual ability revealed by canonical correlation analysis

  • Masaya Misaki
  • Gregory L. Wallace
  • Nathan Dankner
  • Alex Martin
  • Peter A. Bandettini

To investigate patterns and correlates of cortical thickness in adolescent males with autism spectrum disorders (ASD) versus matched typically developing controls, we applied kernel canonical correlation analysis to whole brain cortical thickness with the explaining variables of diagnosis, age, full-scale IQ, and their interactions. The analysis found that canonical variates (patterns of cortical thickness) correlated with each of these variables. The diagnosis- and age-by-diagnosis-related canonical variates showed thinner cortex for participants with ASD, which is consistent with previous studies using a univariate analysis. In addition, the multivariate statistics found larger affected regions with higher sensitivity than those found using univariate analysis. An IQ-related effect was also found with the multivariate analysis. The effects of IQ and age-by-IQ interaction on cortical thickness differed between the diagnostic groups. For typically developing adolescents, IQ was positively correlated with cortical thickness in orbitofrontal, postcentral and superior temporal regions, and greater thinning with age was seen in dorsal frontal areas in the superior IQ (>120) group. These associations between IQ and cortical thickness were not seen in the ASD group. Differing relationships between IQ and cortical thickness implies independent associations between measures of intelligence and brain structure in ASD versus typically developing controls. We discuss these findings vis-à-vis prior results obtained utilizing univariate methods.

YNIMG Journal 2010 Journal Article

Comparison of multivariate classifiers and response normalizations for pattern-information fMRI

  • Masaya Misaki
  • Youn Kim
  • Peter A. Bandettini
  • Nikolaus Kriegeskorte

A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to “decode” the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta- or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher's linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function kernel) support vector machines. We compared these classifiers' accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher's linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60min per subject with 3T fMRI.

YNIMG Journal 2009 Journal Article

Two temporal channels in human V1 identified using fMRI

  • Hiroshi Horiguchi
  • Satoshi Nakadomari
  • Masaya Misaki
  • Brian A. Wandell

Human visual sensitivity to a fairly broad class of dynamic stimuli can be modeled accurately using two temporal channels. Here, we analyze fMRI measurements of the temporal step response to spatially uniform stimuli to estimate these channels in human primary visual cortex (V1). In agreement with the psychophysical literature, the V1 fMRI temporal responses are modeled accurately as a mixture of two (transient and sustained) channels. We derive estimates of the relative contributions from these two channels at a range of eccentricities. We find that all portions of V1 contain a significant transient response. The central visual field representation includes a significant sustained response, but the amplitude of the sustained channel signal declines with eccentricity. The sustained signals may reflect the emphasis on pattern recognition and color in the central visual field; the dominant transient response in the visual periphery may reflect responses in the human visual attention system.

YNIMG Journal 2006 Journal Article

Application of artificial neural network to fMRI regression analysis

  • Masaya Misaki
  • Satoru Miyauchi

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i. e. , it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

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