fMRI BOLD Signal Research Articles

Concurrent TMS/fMRI with high sensitivity and accelerated image acquisition

Abstract Functional magnetic resonance imaging (fMRI) has become the prime technique for non-invasive brain mapping as it yields high-resolution activation maps throughout the brain. Combined with TMS and applied simultaneously, concurrent TMS/fMRI offers the unique opportunity for directly assessing TMS-caused brain activity. Such, it is possible to study TMS effects across varying stimulation amplitude, protocol and targets. As changes in the BOLD fMRI signal are typically in range of only a few percent, high sensitivity acquisition is a prerequisite for successful TMS/fMRI. Standard concurrent TMS/fMRI setups are based on the use of single-channel, large-diameter birdcage coils that enable positioning of the TMS coil within the birdcage coil. In addition to their intrinsically low sensitivity, birdcage coils cannot be used with any of the various accelerated image acquisition methods used in MRI today, including parallel imaging (SENSE, GRAPPA) and multiband imaging (MB; simultaneous multi-slice, SMS). A different setup, based on thin, 7-channel receive arrays was recently proposed to overcome the shortcomings typical to birdcage coil setups [1,2]. Here we quantitatively assess the sensitivity of this new setup across a range of acquisition protocols. We also measure the effects of combining multiple receive-arrays for extended coverage. In particular, we show for the first time that it is possible to use three sets of 7-channel receive-arrays simultaneously to obtain unprecedentedly high sensitivity across the brain.

Selective auditory attention within naturalistic scenes modulates reactivity to speech sounds.

Rapid recognition and categorization of sounds are essential for humans and animals alike, both for understanding and reacting to our surroundings and for daily communication and social interaction. For humans, perception of speech sounds is of crucial importance. In real life, this task is complicated by the presence of a multitude of meaningful non‐speech sounds. The present behavioural, magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) study was set out to address how attention to speech versus attention to natural non‐speech sounds within complex auditory scenes influences cortical processing. The stimuli were superimpositions of spoken words and environmental sounds, with parametric variation of the speech‐to‐environmental sound intensity ratio. The participants' task was to detect a repetition in either the speech or the environmental sound. We found that specifically when participants attended to speech within the superimposed stimuli, higher speech‐to‐environmental sound ratios resulted in shorter sustained MEG responses and stronger BOLD fMRI signals especially in the left supratemporal auditory cortex and in improved behavioural performance. No such effects of speech‐to‐environmental sound ratio were observed when participants attended to the environmental sound part within the exact same stimuli. These findings suggest stronger saliency of speech compared with other meaningful sounds during processing of natural auditory scenes, likely linked to speech‐specific top‐down and bottom‐up mechanisms activated during speech perception that are needed for tracking speech in real‐life‐like auditory environments.

Ultra-High-Field Neuroimaging Reveals Fine-Scale Processing for 3D Perception.

Binocular disparity provides critical information about three-dimensional (3D) structures to support perception and action. In the past decade significant progress has been made in uncovering human brain areas engaged in the processing of binocular disparity signals. Yet, the fine-scale brain processing underlying 3D perception remains unknown. Here, we use ultra-high-field (7T) functional imaging at submillimeter resolution to examine fine-scale BOLD fMRI signals involved in 3D perception. In particular, we sought to interrogate the local circuitry involved in disparity processing by sampling fMRI responses at different positions relative to the cortical surface (i.e., across cortical depths corresponding to layers). We tested for representations related to 3D perception by presenting participants (male and female, N = 8) with stimuli that enable stable stereoscopic perception [i.e., correlated random dot stereograms (RDS)] versus those that do not (i.e., anticorrelated RDS). Using multivoxel pattern analysis (MVPA), we demonstrate cortical depth-specific representations in areas V3A and V7 as indicated by stronger pattern responses for correlated than for anticorrelated stimuli in upper rather than deeper layers. Examining informational connectivity, we find higher feedforward layer-to-layer connectivity for correlated than anticorrelated stimuli between V3A and V7. Further, we observe disparity-specific feedback from V3A to V1 and from V7 to V3A. Our findings provide evidence for the role of V3A as a key nexus for disparity processing, which is implicated in feedforward and feedback signals related to the perceptual estimation of 3D structures.SIGNIFICANCE STATEMENT Binocular vision plays a significant role in supporting our interactions with the surrounding environment. The fine-scale neural mechanisms that underlie the brain's skill in extracting 3D structures from binocular signals are poorly understood. Here, we capitalize on recent advances in ultra-high-field functional imaging to interrogate human brain circuits involved in 3D perception at submillimeter resolution. We provide evidence for the role of area V3A as a key nexus for disparity processing, which is implicated in feedforward and feedback signals related to the perceptual estimation of 3D structures from binocular signals. These fine-scale measurements help bridge the gap between animal neurophysiology and human fMRI studies investigating cross-scale circuits, from micro circuits to global brain networks for 3D perception.

Prefrontal and striatal dopamine D2/D3 receptors correlate with fMRI BOLD activation during stopping

D2-like dopamine receptors in animals and humans have been shown to be linked to impulsive behaviors that are highly relevant for several psychiatric disorders. Here, we investigate the relationship between the fronto-striatal D2/D3 dopamine receptor availability and response inhibition in a selected population of healthy OPRM1 G-allele carriers. Twenty-two participants successively underwent blood-oxygen level dependent functional magnetic resonance imaging (fMRI) while performing a stop-signal task and a separate positron emission tomography (PET) scan. Striatal and extrastriatal D2/D3 dopamine receptor availability was measured using the radiotracer [18F]fallypride. Caudate D2/D3 dopamine receptor availability positively correlated with stopping-related fronto-striatal fMRI activation. In addition, right prefrontal D2/D3 dopamine receptor availability correlated positively with stopping-related striatal fMRI BOLD signal. Our study partially replicates previous findings on correlations between striatal D2/D3 dopamine receptor availability and response inhibition in a population selected for its genetic determination of dopamine response to alcohol and as a modulator of impulse control via the endogenous opioid system. We confirm the important role of D2/D3 dopamine receptor availability in the fronto-striatal neural circuit for response inhibition. Moreover, we extend previous findings suggesting that dopamine receptor availability in the right inferior frontal cortex, a crucial region of the stopping network, is also strongly associated with stopping-related striatal fMRI activity in healthy OPRM1 G-allele carriers.

A practical modification to a resting state fMRI protocol for improved characterization of cerebrovascular function

Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO2 pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemodynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural activity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting-state data segments, and in data segments which added a 2–3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO2 pressure: a breath-hold task to induce hypercapnia (CO2 increase) and a cued deep breathing task to induce hypocapnia (CO2 decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO2 by systematically shifting the CO2 regressor in time to optimize the model fit. This optimization inherently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO2 and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemodynamic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation.

Exploring the relation between BOLD fMRI and cognitive performance using a computer-based quantitative systems pharmacology model: Applications to the COMTVAL158MET genotype and ketamine

BOLD fMRI is increasingly used mostly in an observational way to probe the effect of genotypes or therapeutic intervention in normal and diseased subjects. We use a mechanism-based quantitative systems pharmacology computer model of a human cortical microcircuit, previously calibrated for the 2-back working memory paradigm, adding established biophysical principles, of glucose metabolism, oxygen consumption, neurovascular effects and the paramagnetic impact on blood oxygen levels to calculate a readout for the voxel-based BOLD fMRI signal. The objective was to study the effect of the Catechol-O-methyl Transferase Val158Met (COMT) genotype on performance and BOLD fMRI. While the simulation suggests that on average virtual COMTVV genotype subjects perform worse, subjects with lower GABA, lower 5-HT3 and higher 5-HT1A activation can improve cognitive performance to the level of COMTMM subjects but at the expense of higher BOLD fMRI signal. In a schizophrenia condition, increased NMDA, GABA tone and noise levels, and lower D1R activity can improve cognitive outcome with greater BOLD fMRI signal in COMT Val-carriers. We further generate hypotheses about why ketamine in healthy controls increases the BOLD fMRI signal but reduces cognitive performance. These simulations suggest a strong non-linear relationship between BOLD fMRI signal and cognitive performance. When validated, this mechanistic approach can be useful for moving beyond the descriptive nature of BOLD fMRI imaging and supporting the proper interpretation of imaging biomarkers in CNS disorders.

Stem cell restores thalamocortical plasticity to rescue cognitive deficit in neonatal intraventricular hemorrhage

Severe neonatal intraventricular hemorrhage (IVH) patients incur long-term neurologic deficits such as cognitive disabilities. Recently, the intraventricular transplantation of allogeneic human umbilical cord blood-derived mesenchymal stem cells (MSCs) has drawn attention as a therapeutic potential to treat severe IVH. However, its pathological synaptic mechanism is still elusive. We here demonstrated that the integration of the somatosensory input was significantly distorted by suppressing feed-forward inhibition (FFI) at the thalamocortical (TC) inputs in the barrel cortices of neonatal rats with IVH by using BOLD-fMRI signal and brain slice patch-clamp technique. This is induced by the suppression of Hebbian plasticity via an increase in tumor necrosis factor-α expression during the critical period, which can be effectively reversed by the transplantation of MSCs. Furthermore, we showed that MSC transplantation successfully rescued IVH-induced learning deficits in the sensory-guided decision-making in correlation with TC FFI in the layer 4 barrel cortex.

Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?

Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ4 band and low β1 band) demonstrated significant negative linear relationship (pFWE < 0.05) to the frequent stimulus and three patterns (two low δ2 and δ3 bands, and narrow θ1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ4, β1, and θ1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.

Contribution of Excitatory and Inhibitory Neuronal Activity to BOLD fMRI.

The BOLD fMRI response in the cortex is often assumed to reflect changes in excitatory neural activity. However, the contribution of inhibitory neurons to BOLD fMRI is unclear. Here, the role of inhibitory and excitatory activity was examined using multimodal approaches: electrophysiological recording, 15.2 T fMRI, optical intrinsic signal imaging, and modeling. Inhibitory and excitatory neuronal activity in the somatosensory cortex were selectively modulated by 20-s optogenetic stimulation of VGAT-ChR2 and CaMKII-ChR2 mice, respectively. Somatosensory stimulation and optogenetic stimulation of excitatory neurons induced positive BOLD responses in the somatosensory network, whereas stimulation of inhibitory neurons produced biphasic responses at the stimulation site, initial positive and later negative BOLD signals, and negative BOLD responses at downstream sites. When the stimulation duration was reduced to 5 s, the hemodynamic response of VGAT-ChR2 mice to optogenetic stimulation was only positive. Lastly, modeling performed from neuronal and hemodynamic data shows that the hemodynamic response function (HRF) of excitatory neurons is similar across different conditions, whereas the HRF of inhibitory neurons is highly sensitive to stimulation frequency and peaks earlier than that of excitatory neurons. Our study provides insights into the neurovascular coupling of excitatory and inhibitory neurons and the interpretation of BOLD fMRI signals.

Brain dynamics: Synchronous peaks, functional connectivity, and its temporal variability.

We describe advances in the understanding of brain dynamics that are important for understanding the operation of the cerebral cortex in health and disease. Peaks in the resting state fMRI BOLD signal in many different brain areas can become synchronized. In data from 1,017 participants from the Human Connectome Project, we show that early visual and connected areas have the highest probability of synchronized peaks. We show that these cortical areas also have low temporal variability of their functional connectivity. We show that there is an approximately reciprocal relation between the probability that a brain region will be involved in synchronized peaks and the temporal variability of the connectivity of a brain region. We show that a high probability of synchronized peaks and a low temporal variability of the connectivity of cortical areas are related to high mean functional connectivity, and provide an account of how these dynamics with some of the properties of avalanches arise. These discoveries help to advance our understanding of cortical operation in health, and in some mental disorders including schizophrenia.

A probabilistic atlas of the human ventral tegmental area (VTA) based on 7\xa0Tesla MRI data

Functional magnetic resonance imaging (fMRI) BOLD signal is commonly localized by using neuroanatomical atlases, which can also serve for region of interest analyses. Yet, the available MRI atlases have serious limitations when it comes to imaging subcortical structures: only 7% of the 455 subcortical nuclei are captured by current atlases. This highlights the general difficulty in mapping smaller nuclei deep in the brain, which can be addressed using ultra-high field 7 Tesla (T) MRI. The ventral tegmental area (VTA) is a subcortical structure that plays a pivotal role in reward processing, learning and memory. Despite the significant interest in this nucleus in cognitive neuroscience, there are currently no available, anatomically precise VTA atlases derived from 7 T MRI data that cover the full region of the VTA. Here, we first provide a protocol for multimodal VTA imaging and delineation. We then provide a data description of a probabilistic VTA atlas based on in vivo 7 T MRI data.

Blockade of Indoleamine 2, 3-dioxygenase 1 ameliorates hippocampal neurogenesis and BOLD-fMRI signals in chronic stress precipitated depression.

Indoleamine 2, 3-dioxygenase 1 (IDO1) has been implicated in the pathogenesis of depression, though its molecular mechanism is still poorly understood. We investigated the molecular mechanism of IDO1 in depression by using the chronic unpredictable mild stress (CUMS) model in Ido1-/- mice and WT mice. The brain blood oxygen level dependent (BOLD) signals in mice were collected by functional magnetic resonance imaging (fMRI) technology. IDO1 inhibitor INCB024360 was intervened in dorsal raphe nucleus (DRN) through stereotactic injection. We found an elevation of serum IDO1 activity and decreased 5-HT in CUMS mice, and the serum IDO1 activity was negatively correlated with 5-HT level. Consistently, IDO1 was increased in hippocampus and DRN regions, accompanied by a reduction of hippocampal BDNF levels in mice with CUMS. Specifically, pharmacological inhibition of IDO1 activity in the DRN alleviated depressive-like behaviour with improving hippocampal BDNF expression and neurogenesis in CUMS mice. Furthermore, ablation of Ido1 exerted stress resistance and decreased the sensitivity of depression in CUMS mice with the stable BOLD signals, BDNF expression and neurogenesis in hippocampus. Thus, IDO1 hyperactivity played crucial roles in modulating 5-HT metabolism and BDNF function thereby impacting outcomes of hippocampal neurogenesis and BOLD signals in depressive disorder.

Increased sensitivity to strong perturbations in a whole-brain model of LSD

Lysergic acid diethylamide (LSD) is a potent psychedelic drug, which has seen a revival in clinical and pharmacological research within recent years. Human neuroimaging studies have shown fundamental changes in brain-wide functional connectivity and an expansion of dynamical brain states, thus raising the question about a mechanistic explanation of the dynamics underlying these alterations. Here, we applied a novel perturbational approach based on a whole-brain computational model, which opens up the possibility to externally perturb different brain regions in silico and investigate differences in dynamical stability of different brain states, i.e. the dynamical response of a certain brain region to an external perturbation. After adjusting the whole-brain model parameters to reflect the dynamics of functional magnetic resonance imaging (fMRI) BOLD signals recorded under the influence of LSD or placebo, perturbations of different brain areas were simulated by either promoting or disrupting synchronization in the regarding brain region. After perturbation offset, we quantified the recovery characteristics of the brain area to its basal dynamical state with the Perturbational Integration Latency Index (PILI) and used this measure to distinguish between the two brain states. We found significant changes in dynamical complexity with consistently higher PILI values after LSD intake on a global level, which indicates a shift of the brain's global working point further away from a stable equilibrium as compared to normal conditions. On a local level, we found that the largest differences were measured within the limbic network, the visual network and the default mode network. Additionally, we found a higher variability of PILI values across different brain regions after LSD intake, indicating higher response diversity under LSD after an external perturbation. Our results provide important new insights into the brain-wide dynamical changes underlying the psychedelic state - here provoked by LSD intake - and underline possible future clinical applications of psychedelic drugs in particular psychiatric disorders.

Resting-State functional networks of different topographic representations in the somatosensory cortex of macaque monkeys and humans

Information processing in the brain is mediated through a complex functional network architecture whose comprising nodes integrate and segregate themselves on different timescales. To gain an understanding of the network function it is imperative to identify and understand the network structure with respect to the underlying anatomical connectivity and the topographic organization. Here we show that the previously described resting-state network for the somatosensory area 3b comprises of distinct networks that are characteristic for different topographic representations. Seed-based resting-state functional connectivity analysis in macaque monkeys and humans using BOLD-fMRI signals from the face, the hand and rest of the medial somatosensory representations of area 3b revealed different correlation patterns. Both monkeys and humans have many similarities in the connectivity networks, although the networks are more complex in humans with many more nodes. In both the species face area network has the highest ipsilateral and contralateral connectivity, which included areas 3b and 4, and ventral premotor area. The area 3b hand network included ipsilateral hand representation in area 4.The emergent functional network structures largely reflect the known anatomical connectivity. Our results show that different body part representations in area 3b have independent functional networks perhaps reflecting differences in the behavioral use of different body parts. The results also show that large cortical areas if considered together, do not give a complete and accurate picture of the network architecture.

Altered sensorimotor fMRI directed connectivity in Parkinson's disease patients.

Dopamine depletion in the axons of Parkinson's disease (PD) patients precedes depletion in cell bodies thus proposing that macroscopic connectivity can be used to understand disease mechanism. A novel multivariate functional connectivity analysis, based on high order coherence among four fMRI BOLD signals was applied on resting-state fMRI data of controls and PD patients (OFF and ON medication states) and unidirectional multiple-region pathways in the sensorimotor system were identified. Pathways were classified as "preserved" (unaffected by the disease), "damaged" (not observed in patients) and "corrected" (observed in controls and in PD-ON state). The majority of all pathways were feedforward, most of them with the pattern "S1→M1→SMA." Of these pathways, 67% were "damaged," 28% "preserved," and 5% "corrected." Prefrontal cortex (PFC) afferent and efferent pathways that corresponded to goal directed and habitual activities corresponded to recurrent circuits. Eighty-one percent of habitual afferent had internal cue (i.e., M1→S1→), of them 79% were "damaged" and the rest "preserved." All goal-directed afferent had external cue (i.e., S1→M1→) with third "damaged," third "preserved," and third "corrected." Corrected pathways were initiated in the dorsolateral PFC. Reduced connectivity of the SMA and PFC resulted from reduced sensorimotor afferent to these regions. Reduced sensorimotor internal cues to the PFC resulted with reduced habitual processes. Levodopa effects were for pathways that started in region reach with dopamine receptors. This methodology can enrich understudying of PD mechanisms in other (e.g., the default mode network) systems.

NREM sleep stages specifically alter dynamical integration of large-scale brain networks.

SummaryFunctional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.

Repeatability of Neural and Autonomic Responses to Acute Psychosocial Stress.

FMRI Montreal Imaging Stress Tasks (MIST) have been shown to activate endocrine and autonomic stress responses that are mediated by a prefrontal cortex (PFC)-hippocampus-amygdala circuit. However, the stability of the neurobehavioral responses over time and the ability to monitor response to clinical interventions has yet to be validated. The objective of this study was to compare the fMRI and physiologic responses to acute psychosocial stress in healthy volunteers during initial and follow-up visits approximately 13 weeks later, simulating a typical duration of clinical intervention. We hypothesized that responses to stress would remain highly conserved across the 2 visits in the absence of an intervention. 15 healthy volunteers completed a variant of control math task (CMT) and stress math task (SMT) conditions based on MIST. Neural responses were modeled using an event-related design with estimates for math performance and auditory feedback for each task condition. For each visit, measures of stress reactivity included differential fMRI and heart rate (SMT-CMT), as well as salivary alpha-amylase before and after scanning sessions. The results revealed that differential fMRI, as well as increased heart rate and salivary alpha-amylase from before and after scanning remained similar between visits. Intraclass correlation coefficient (ICC) values revealed areas of reliable task-dependent BOLD fMRI signal response across visits for peaks of clusters for the main effect of condition (SMT vs CMT) within dorsal anterior cingulate cortex (ACC), insula, and hippocampus regions during math performance and within subgenual ACC, posterior cingulate cortex, dorsolateral PFC regions during auditory feedback. Given that the neurobehavioral response to acute stress remained highly conserved across visits in the absence of an intervention, this study confirms the utility for MIST for assessing longitudinal changes in controlled trials that can identify underlying neurobiological mechanisms involved in mediating the efficacy of stress-reduction interventions.

Manganese-enhanced MRI reveals changes within brain anxiety and aversion circuitry in rats with chronic neuropathic pain- and anxiety-like behaviors

Chronic pain often predicts the onset of psychological distress. Symptoms including anxiety and depression after pain chronification reportedly are caused by brain remodeling/recruitment of the limbic and reward/aversion circuitries. Pain is the primary precipitating factor that has caused opioid overprescribing and continued overuse of opioids leading to the current opioid epidemic. Yet experimental pain therapies often fail in clinical trials. Better understanding of underlying pathologies contributing to pain chronification is needed to address these chronic pain related issues. In the present study, a chronic neuropathic pain model persisting 10 weeks was studied. The model develops both anxiety- and pain-related behavioral measures to mimic clinical pain. The manganese-enhanced magnetic resonance imaging (MEMRI) utilized improved MRI signal contrast in brain regions with higher neuronal activity in the rodent chronic constriction trigeminal nerve injury (CCI-ION) model. T1-weighted MEMRI signal intensity was increased compared to controls in supraspinal regions of the anxiety and aversion circuitry, including anterior cingulate gyrus (ACC), amygdala, habenula, caudate, ventrolateral and dorsomedial periaqueductal gray (PAG). Despite continuing mechanical hypersensitivity, MEMRI T1 signal intensity as the neuronal activity measure, was not significantly different in thalamus and decreased in somatosensory cortex (S1BF) of CCI-ION rats compared to naïve controls. This is consistent with decreased fMRI BOLD signal intensity in thalamus and cortex of patients with longstanding trigeminal neuropathic pain reportedly associated with gray matter volume decrease in these regions. Significant increase in MEMRI T2 signal intensity in thalamus of CCI-ION animals was indication of tissue water content, cell dysfunction and/or reactive astrogliosis. Decreased T2 signal intensity in S1BF cortex of rats with CCI-ION was similar to findings of reduced T2 signals in clinical patients with chronic orofacial pain indicating prolonged astrocyte activation. These findings support use of MEMRI and chronic rodent models for preclinical studies and therapeutic trials to reveal brain sites activated only after neuropathic pain has persisted in timeframes relevant to clinical pain and to observe treatment effects not possible in short-term models which do not have evidence of anxiety-like behaviors. Potential improvement is predicted in the success rate of preclinical drug trials in future studies with this model.

Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women.

Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently.

How much of the BOLD-fMRI signal can be approximated from simultaneous EEG data: relevance for the transfer and dissemination of neurofeedback interventions

Objective. fMRI-based neurofeedback (NF) interventions represent the method of choice for the neuromodulation of localized brain areas. Although we have already validated an fMRI-NF protocol targeting the facial expressions processing network (FEPN), its dissemination is hampered by the economical and logistical constraints of fMRI-NF interventions, which may be however surpassed by transferring it to EEG setups, due to their low cost and portability. One of the major challenges of this procedure is then to reconstruct the BOLD-fMRI signal measured at the FEPN using only EEG signals. Because these types of approaches have been poorly explored so far, here we systematically investigated the extent at which the BOLD-fMRI signal recorded from the FEPN during a fMRI-NF protocol could be reconstructed from the simultaneously recorded EEG signal. Approach. Several features from both scalp and source spaces (the latter estimated using continuous EEG source imaging) were extracted and used as predictors in a regression problem using random forests. Furthermore, three different approaches to deal with the hemodynamic delay of the BOLD signal were tested. The resulting models were compared with the only approach already proposed in the literature that uses spectral features and considers different time delays. Main results. The combination of linear and non-linear features (particularly the largest Lyapunov exponent and entropy measures) projected into the source space, spatially filtered by independent component analysis (ICA) and convolved with multiple HRF functions peaking at different latencies, increases significantly the reconstruction accuracy (defined as the correlation between the measured and approximated BOLD signal) from 20% (direct comparison with the method used in the current literature) to 56%. Significance. With this pipeline, a more accurate reconstruction of the BOLD signal can be obtained, which will positively impact the transfer of fMRI-based neurofeedback interventions to EEG setups, and more importantly, their dissemination and efficacy in modulating the activity of the desired brain areas.