Cortical fMRI Research Articles

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions.

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing.

The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.

Cortical Patterns Shift from Sequence Feature Separation during Planning to Integration during Motor Execution.

Performing sequences of movements from memory and adapting them to changing task demands is a hallmark of skilled human behavior, from handwriting to playing a musical instrument. Prior studies showed a fine-grained tuning of cortical primary motor, premotor, and parietal regions to motor sequences: from the low-level specification of individual movements to high-level sequence features, such as sequence order and timing. However, it is not known how tuning in these regions unfolds dynamically across planning and execution. To address this, we trained 24 healthy right-handed human participants (14 females, 10 males) to produce four five-element finger press sequences with a particular finger order and timing structure in a delayed sequence production paradigm entirely from memory. Local cortical fMRI patterns during preparation and production phases were extracted from separate No-Go and Go trials, respectively, to tease out activity related to these perimovement phases. During sequence planning, premotor and parietal areas increased tuning to movement order or timing, regardless of their combinations. In contrast, patterns reflecting the unique integration of sequence features emerged in these regions during execution only, alongside timing-specific tuning in the ventral premotor, supplementary motor, and superior parietal areas. This was in line with the participants' behavioral transfer of trained timing, but not of order to new sequence feature combinations. Our findings suggest a general informational state shift from high-level feature separation to low-level feature integration within cortical regions for movement execution. Recompiling sequence features trial-by-trial during planning may enable flexible last-minute adjustment before movement initiation.SIGNIFICANCE STATEMENT Musicians and athletes can modify the timing and order of movements in a sequence trial-by-trial, allowing for a vast repertoire of flexible behaviors. How does the brain put together these high-level sequence features into an integrated whole? We found that, trial-by-trial, the control of sequence features undergoes a state shift from separation during planning to integration during execution across a network of motor-related cortical areas. These findings have implications for understanding the hierarchical control of skilled movement sequences, as well as how information in brain areas unfolds across planning and execution.

Decreased but diverse activity of cortical and thalamic neurons in consciousness-impairing rodent absence seizures

Absence seizures are brief episodes of impaired consciousness, behavioral arrest, and unresponsiveness, with yet-unknown neuronal mechanisms. Here we report that an awake female rat model recapitulates the behavioral, electroencephalographic, and cortical functional magnetic resonance imaging characteristics of human absence seizures. Neuronally, seizures feature overall decreased but rhythmic firing of neurons in cortex and thalamus. Individual cortical and thalamic neurons express one of four distinct patterns of seizure-associated activity, one of which causes a transient initial peak in overall firing at seizure onset, and another which drives sustained decreases in overall firing. 40–60 s before seizure onset there begins a decline in low frequency electroencephalographic activity, neuronal firing, and behavior, but an increase in higher frequency electroencephalography and rhythmicity of neuronal firing. Our findings demonstrate that prolonged brain state changes precede consciousness-impairing seizures, and that during seizures distinct functional groups of cortical and thalamic neurons produce an overall transient firing increase followed by a sustained firing decrease, and increased rhythmicity.

Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress

Background: Posttraumatic stress disorder (PTSD) is a significant burden among combat Veterans returning from the wars in Iraq and Afghanistan. While empirically supported treatments have demonstrated reductions in PTSD symptomatology, there remains a need to improve treatment effectiveness. Functional magnetic resonance imaging (fMRI) neurofeedback has emerged as a possible treatment to ameliorate PTSD symptom severity. Virtual reality (VR) approaches have also shown promise in increasing treatment compliance and outcomes. To facilitate fMRI neurofeedback-associated therapies, it would be advantageous to accurately classify internal brain stress levels while Veterans are exposed to trauma-associated VR imagery. Methods: Across 2 sessions, we used fMRI to collect neural responses to trauma-associated VR-like stimuli among male combat Veterans with PTSD symptoms (N = 8). Veterans reported their self-perceived stress level on a scale from 1 to 8 every 15 s throughout the fMRI sessions. In our proposed framework, we precisely sample the fMRI data on cortical gray matter, blurring the data along the gray-matter manifold to reduce noise and dimensionality while preserving maximum neural information. Then, we independently applied 3 machine learning (ML) algorithms to this fMRI data collected across 2 sessions, separately for each Veteran, to build individualized ML models that predicted their internal brain states (self-reported stress responses). Results: We accurately classified the 8-class self-reported stress responses with a mean (± standard error) root mean square error of 0.6 (± 0.1) across all Veterans using the best ML approach. Conclusions: The findings demonstrate the predictive ability of ML algorithms applied to whole-brain cortical fMRI data collected during individual Veteran sessions. The framework we have developed to preprocess whole-brain cortical fMRI data and train ML models across sessions would provide a valuable tool to enable individualized real-time fMRI neurofeedback during VR-like exposure therapy for PTSD.

Towards assessing subcortical "deep brain" biomarkers of PTSD with functional near-infrared spectroscopy.

Assessment of brain function with functional near-infrared spectroscopy (fNIRS) is limited to the outer regions of the cortex. Previously, we demonstrated the feasibility of inferring activity in subcortical "deep brain" regions using cortical functional magnetic resonance imaging (fMRI) and fNIRS activity in healthy adults. Access to subcortical regions subserving emotion and arousal using affordable and portable fNIRS is likely to be transformative for clinical diagnostic and treatment planning. Here, we validate the feasibility of inferring activity in subcortical regions that are central to the pathophysiology of posttraumatic stress disorder (PTSD; i.e. amygdala and hippocampus) using cortical fMRI and simulated fNIRS activity in a sample of adolescents diagnosed with PTSD (N = 20, mean age = 15.3 ± 1.9years) and age-matched healthy controls (N = 20, mean age = 14.5 ± 2.0years) as they performed a facial expression task. We tested different prediction models, including linear regression, a multilayer perceptron neural network, and a k-nearest neighbors model. Inference of subcortical fMRI activity with cortical fMRI showed high prediction performance for the amygdala (r >0.91) and hippocampus (r >0.95) in both groups. Using fNIRS simulated data, relatively high prediction performance for deep brain regions was maintained in healthy controls (r >0.79), as well as in youths with PTSD (r >0.75). The linear regression and neural network models provided the best predictions.

NAS-optimized topology-preserving transfer learning for differentiating cortical folding patterns

Increasing evidence suggests that cortical folding patterns of human cerebral cortex manifest overt structural and functional differences. However, for interpretability, few studies leverage advanced techniques (e.g., deep learning) to investigate the difference among cortical folds, resulting in more differences yet to be extensively explored. To this end, we proposed an effective topology-preserving transfer learning framework to differentiate cortical fMRI time series extracted from cortical folds. Our framework consists of three main parts: (1) Neural architecture search (NAS), which is used to devise a well-performing network structure based on an initialized hand-designed super-graph in an image dataset; (2) Topology-preserving transfer, which takes the model searched by NAS as the source network, keeping the topological connectivity in the network unchanged, while transforming all 2D operations including convolution and pooling into 1D, therefore resulting in a topology-preserving network, named TPNAS-Net; (3) Classification and correlation analysis, which involves using the TPNAS-Net to classify 1D cortical fMRI time series for each individual brain, and performing a group difference analysis between autism spectrum disorder (ASD) and healthy control (HC) and correlation analysis with clinical information (i.e., age). Extensive experiments on two ASD datasets obtain consistent results, demonstrating that the TPNAS-Net not only discriminates cortical folding patterns at high classification accuracy, but also captures subtle differences between ASD and HC (p-value=0.042). In addition, we discover that there is a positive correlation between the classification accuracy and age in ASD (r=0.39, p-value=0.04). These findings together suggest that structural and functional differences in cortical folding patterns between ASD and HC may provide a potentially useful biomarker for the diagnosis of ASD.

Complexity Assessment of Chronic Pain in Elderly Knee Osteoarthritis Based on Neuroimaging Recognition Techniques.

The chronic pain of knee osteoarthritis in the elderly is investigated in detail in this paper, as well as the complexity of chronic pain utilising neuroimaging recognition techniques. Chronic pain in knee osteoarthritis (KOA) has a major effect on patients' quality of life and functional activities; therefore, understanding the causes of KOA pain and the analgesic advantages of different therapies is important. In recent years, neuroimaging techniques have become increasingly important in basic and clinical pain research. Thanks to the application and development of neuroimaging techniques in the study of chronic pain in KOA, researchers have found that chronic pain in KOA contains both injury-receptive and neuropathic pain components. The neuropathic pain mechanism that causes KOA pain is complicated, and it may be produced by peripheral or central sensitization, but it has not gotten enough attention in clinical practice, and there is no agreement on how to treat combination neuropathic pain KOA. As a result, using neuroimaging techniques such as magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS), this review examines the changes in brain pathophysiology-related regions caused by KOA pain, compares the latest results in pain assessment and prediction, and clarifies the central brain analgesic mechanistic. The capsule network model is introduced in this paper from the perspective of deep learning network structure to construct an information-complete and reversible image low-level feature bridge using isotropic representation, predict the corresponding capsule features from MRI voxel responses, and then, complete the accurate reconstruction of simple images using inverse transformation. The proposed model improves the structural similarity index by about 10%, improves the reconstruction performance of low-level feature content in simple images by about 10%, and achieves feature interpretation and analysis of low-level visual cortical fMRI voxels by visualising capsule features, according to the experimental results.

Changes in working memory-related cortical responses following pediatric mild traumatic brain injury: A longitudinal fMRI study

Persistent post-concussion symptoms (PPCS) lasting longer than 4 weeks affect 25% of children with mild traumatic brain injury (mTBI) or concussion. Working memory (WM) problems are a common complaint in children with PPCS. Despite normal function on traditional neuropsychological tests, these children exhibit aberrant cortical responses within the dorsolateral prefrontal cortex (dlPFC) and default mode network (DMN) regions – both of which are implicated in WM. Using a prospective, longitudinal cohort study design, we investigated changes in cortical fMRI responses within the dlPFC and DMN during an nback WM task at two timepoints: one and two months post-injury. Across these timepoints, the primary outcome was change in cortical activations (increase in BOLD) and deactivations (decrease in BOLD) of both dlPFC and DMN. Twenty-nine children (mean age 15.49 ± 2.15; 48.3% male) with fMRI scans at both timepoints were included, following data quality control. Student’s t-tests were used to examine cortical activations across time and task difficulty. ANCOVA F-tests examined cortical responses after removal of baseline across time, task difficulty and recovery. Volumes of interest (5 mm sphere) were placed in peak voxel regions of the DMN and dlPFC to compare cortical responses between recovered and unrecovered participants over time (one-way ANOVA). Between one and two months post-injury, we found significant increases in dlPFC activations and significant activations and deactivations in the DMN with increasing task difficulty, alongside improved task performance. Cortical responses of the DMN and bilateral dlPFC displayed increased intensity in recovered participants, together with improved attention and behavioural symptoms. Overall, our findings suggest evidence of neural compensation and ongoing cognitive recovery from pediatric TBI over time between one and two months post injury in children with PPCS. These results highlight the wider and persisting implications of mTBI in children, whose maturing brains are particularly vulnerable to TBI.

Slow Endogenous Fluctuations in Cortical fMRI Signals Correlate with Reduced Performance in a Visual Detection Task and Are Suppressed by Spatial Attention.

Spatial attention improves performance on visual tasks, increases neural responses to attended stimuli, and reduces correlated noise in visual cortical neurons. In addition to being visually responsive, many retinotopic visual cortical areas exhibit very slow (<0.1 Hz) endogenous fluctuations in functional magnetic resonance imaging signals. To test whether these fluctuations degrade stimulus representations, thereby impairing visual detection, we recorded functional magnetic resonance imaging responses while human participants performed a target detection task that required them to allocate spatial attention to either a rotating wedge stimulus or a central fixation point. We then measured the effects of spatial attention on response amplitude at the frequency of wedge rotation and on the amplitude of endogenous fluctuations at nonstimulus frequencies. We found that, in addition to enhancing stimulus-evoked responses, attending to the wedge also suppressed slow endogenous fluctuations that were unrelated to the visual stimulus in topographically defined areas in early visual cortex, posterior parietal cortex, and lateral occipital cortex, but not in a nonvisual cortical control region. Moreover, attentional enhancement of response amplitude and suppression of endogenous fluctuations were dissociable across cortical areas and across time. Finally, we found that the amplitude of the stimulus-evoked response was not correlated with a perceptual measure of visual target detection. Instead, perceptual performance was accounted for by the amount of suppression of slow endogenous fluctuations. Our results indicate that the amplitude of slow fluctuations of cortical activity is influenced by spatial attention and suggest that these endogenous fluctuations may impair perceptual processing in topographically organized visual cortical areas.

The association between subcortical and cortical fMRI and lifetime noise exposure in listeners with normal hearing thresholds

In animal models, exposure to high noise levels can cause permanent damage to hair-cell synapses (cochlear synaptopathy) for high-threshold auditory nerve fibers without affecting sensitivity to quiet sounds. This has been confirmed in several mammalian species, but the hypothesis that lifetime noise exposure affects auditory function in humans with normal audiometric thresholds remains unconfirmed and current evidence from human electrophysiology is contradictory. Here we report the auditory brainstem response (ABR), and both transient (stimulus onset and offset) and sustained functional magnetic resonance imaging (fMRI) responses throughout the human central auditory pathway across lifetime noise exposure. Healthy young individuals aged 25–40 years were recruited into high (n = 32) and low (n = 30) lifetime noise exposure groups, stratified for age, and balanced for audiometric threshold up to 16 kHz fMRI demonstrated robust broadband noise-related activity throughout the auditory pathway (cochlear nucleus, superior olivary complex, nucleus of the lateral lemniscus, inferior colliculus, medial geniculate body and auditory cortex). fMRI responses in the auditory pathway to broadband noise onset were significantly enhanced in the high noise exposure group relative to the low exposure group, differences in sustained fMRI responses did not reach significance, and no significant group differences were found in the click-evoked ABR. Exploratory analyses found no significant relationships between the neural responses and self-reported tinnitus or reduced sound-level tolerance (symptoms associated with synaptopathy). In summary, although a small effect, these fMRI results suggest that lifetime noise exposure may be associated with central hyperactivity in young adults with normal hearing thresholds.

Sub-millimetre quantitative T1 mapping using inversion-recovery EPI and application for cortical depth-dependent fMRI at 7 Tesla

Sub-millimetre quantitative T1 mapping using inversion-recovery EPI and application for cortical depth-dependent fMRI at 7 Tesla

Neither Cholinergic Nor Dopaminergic Enhancement Improve Spatial Working Memory Precision in Humans.

Acetylcholine and dopamine are neurotransmitters that play multiple important roles in perception and cognition. Pharmacological cholinergic enhancement reduces excitatory receptive field size of neurons in marmoset primary visual cortex and sharpens the spatial tuning of visual perception and visual cortical fMRI responses in humans. Moreover, previous studies show that manipulation of cholinergic or dopaminergic signaling alters the spatial tuning of macaque prefrontal cortical neurons during the delay period of a spatial working memory (SWM) task and can improve SWM performance in macaque monkeys and human subjects. Here, we investigated the effects of systemic cholinergic and dopaminergic enhancement on the precision of SWM, as measured behaviorally in human subjects. Cholinergic transmission was increased by oral administration of 5 mg of the cholinesterase inhibitor donepezil, and dopaminergic signaling was enhanced with 100 mg levodopa/10 mg carbidopa. Each neurotransmitter system was separately investigated in double-blind placebo-controlled studies. On each trial of the SWM task, a square was presented for 150 ms at a random location along an invisible circle with a radius of 12 degrees of visual angle, followed by a 900 ms delay period with no stimulus shown on the screen. Then, the square was presented at new location, displaced in either a clockwise (CW) or counterclockwise (CCW) direction along the circle. Subjects used their memory of the location of the original square to report the direction of displacement. SWM precision was defined as the amount of displacement corresponding to 75% correct performance. We observed no significant effect on SWM precision for either donepezil or levodopa/carbidopa. There was also no significant effect on performance on the SWM task (percent correct across all trials) for either donepezil or levodopa/carbidopa. Thus, despite evidence that acetylcholine and dopamine regulate spatial tuning of individual neurons and can improve performance of SWM tasks, pharmacological enhancement of signaling of these neurotransmitters does not substantially affect a behavioral measure of the precision of SWM in humans.

Individual Movement Variability Magnitudes Are Explained by Cortical Neural Variability.

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior-parietal cortex of individual subjects explained their movement-extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities.SIGNIFICANCE STATEMENT Neural activity and movement kinematics are remarkably variable. Although intertrial variability is rarely studied, here, we demonstrate that individual human subjects exhibit distinct magnitudes of neural and kinematic variability that are reproducible across movements to different targets and when performing these movements with either arm. Furthermore, when examining the relationship between cortical variability and movement variability, we find that cortical fMRI variability in parietal cortex of individual subjects explained their movement extent variability. This enabled us to explain why some subjects performed more variable movements than others based on their cortical variability magnitudes.

Cortical depth-dependent fMRI: heterogeneity across tasks, across participants, across days and along the cortical ribbon

Cortical depth-dependent fMRI: heterogeneity across tasks, across participants, across days and along the cortical ribbon

Distortion products in auditory fMRI research: Measurements and solutions

Nonlinearities in the cochlea can introduce audio frequencies that are not present in the sound signal entering the ear. Known as distortion products (DPs), these added frequencies complicate the interpretation of auditory experiments. Sound production systems also introduce distortion via nonlinearities, a particular concern for fMRI research because the Sensimetrics earphones widely used for sound presentation are less linear than most high-end audio devices (due to design constraints). Here we describe the acoustic and neural effects of cochlear and earphone distortion in the context of fMRI studies of pitch perception, and discuss how their effects can be minimized with appropriate stimuli and masking noise. The amplitude of cochlear and Sensimetrics earphone DPs were measured for a large collection of harmonic stimuli to assess effects of level, frequency, and waveform amplitude. Cochlear DP amplitudes were highly sensitive to the absolute frequency of the DP, and were most prominent at frequencies below 300Hz. Cochlear DPs could thus be effectively masked by low-frequency noise, as expected. Earphone DP amplitudes, in contrast, were highly sensitive to both stimulus and DP frequency (due to prominent resonances in the earphone's transfer function), and their levels grew more rapidly with increasing stimulus level than did cochlear DP amplitudes. As a result, earphone DP amplitudes often exceeded those of cochlear DPs. Using fMRI, we found that earphone DPs had a substantial effect on the response of pitch-sensitive cortical regions. In contrast, cochlear DPs had a small effect on cortical fMRI responses that did not reach statistical significance, consistent with their lower amplitudes. Based on these findings, we designed a set of pitch stimuli optimized for identifying pitch-responsive brain regions using fMRI. These stimuli robustly drive pitch-responsive brain regions while producing minimal cochlear and earphone distortion, and will hopefully aid fMRI researchers in avoiding distortion confounds.

Developmental refinement of cortical systems for speech and voice processing

Development typically leads to optimized and adaptive neural mechanisms for the processing of voice and speech. In this fMRI study we investigated how this adaptive processing reaches its mature efficiency by examining the effects of task, age and phonological skills on cortical responses to voice and speech in children (8–9years), adolescents (14–15years) and adults. Participants listened to vowels (/a/, /i/, /u/) spoken by different speakers (boy, girl, man) and performed delayed-match-to-sample tasks on vowel and speaker identity. Across age groups, similar behavioral accuracy and comparable sound evoked auditory cortical fMRI responses were observed. Analysis of task-related modulations indicated a developmental enhancement of responses in the (right) superior temporal cortex during the processing of speaker information. This effect was most evident through an analysis based on individually determined voice sensitive regions. Analysis of age effects indicated that the recruitment of regions in the temporal–parietal cortex and posterior cingulate/cingulate gyrus decreased with development. Beyond age-related changes, the strength of speech-evoked activity in left posterior and right middle superior temporal regions significantly scaled with individual differences in phonological skills. Together, these findings suggest a prolonged development of the cortical functional network for speech and voice processing. This development includes a progressive refinement of the neural mechanisms for the selection and analysis of auditory information relevant to the ongoing behavioral task.

Neural basis of identity information extraction from noisy face images.

Previous research has made significant progress in identifying the neural basis of the remarkably efficient and seemingly effortless face perception in humans. However, the neural processes that enable the extraction of facial information under challenging conditions when face images are noisy and deteriorated remains poorly understood. Here we investigated the neural processes underlying the extraction of identity information from noisy face images using fMRI. For each participant, we measured (1) face-identity discrimination performance outside the scanner, (2) visual cortical fMRI responses for intact and phase-randomized face stimuli, and (3) intrinsic functional connectivity using resting-state fMRI. Our whole-brain analysis showed that the presence of noise led to reduced and increased fMRI responses in the mid-fusiform gyrus and the lateral occipital cortex, respectively. Furthermore, the noise-induced modulation of the fMRI responses in the right face-selective fusiform face area (FFA) was closely associated with individual differences in the identity discrimination performance of noisy faces: smaller decrease of the fMRI responses was accompanied by better identity discrimination. The results also revealed that the strength of the intrinsic functional connectivity within the visual cortical network composed of bilateral FFA and bilateral object-selective lateral occipital cortex (LOC) predicted the participants' ability to discriminate the identity of noisy face images. These results imply that perception of facial identity in the case of noisy face images is subserved by neural computations within the right FFA as well as a re-entrant processing loop involving bilateral FFA and LOC.

Rhythmic 3–4 Hz discharge is insufficient to produce cortical BOLD fMRI decreases in generalized seizures

Absence seizures are transient episodes of impaired consciousness accompanied by 3–4Hz spike–wave discharge on electroencephalography (EEG). Human functional magnetic resonance imaging (fMRI) studies have demonstrated widespread cortical decreases in the blood oxygen-level dependent (BOLD) signal that may play an important role in the pathophysiology of these seizures. Animal models could provide an opportunity to investigate the fundamental mechanisms of these changes, however they have so far failed to consistently replicate the cortical fMRI decreases observed in human patients. This may be due to important differences between human seizures and animal models, including a lack of cortical development in rodents or differences in the frequencies of rodent (7–8Hz) and human (3–4Hz) spike–wave discharges. To examine the possible contributions of these differences, we developed a ferret model that exhibits 3–4Hz spike–wave seizures in the presence of a sulcated cortex. Measurements of BOLD fMRI and simultaneous EEG demonstrated cortical fMRI increases during and following spike–wave seizures in ferrets. However unlike human patients, significant fMRI decreases were not observed. The lack of fMRI decreases was consistent across seizures of different durations, discharge frequencies, and anesthetic regimes, and using fMRI analysis models similar to human patients. In contrast, generalized tonic–clonic seizures under the same conditions elicited sustained postictal fMRI decreases, verifying that the lack of fMRI decreases with spike–wave was not due to technical factors. These findings demonstrate that 3–4Hz spike–wave discharge in a sulcated animal model does not necessarily produce fMRI decreases, leaving the mechanism for this phenomenon open for further investigation.

Increased resting functional connectivity in spike-wave epilepsy in WAG/Rij rats.

Functional magnetic resonance imaging (fMRI)-based resting functional connectivity is well suited for measuring slow correlated activity throughout brain networks. Epilepsy involves chronic changes in normal brain networks, and recent work demonstrated enhanced resting fMRI connectivity between the hemispheres in childhood absence epilepsy. An animal model of this phenomenon would be valuable for investigating fundamental mechanisms and testing therapeutic interventions. We used fMRI-based resting functional connectivity for studying brain networks involved in absence epilepsy. Wistar Albino Glaxo rats from Rijswijk (WAG/Rij) exhibit spontaneous episodes of staring and unresponsiveness accompanied by spike-wave discharges (SWDs) resembling human absence seizures in behavior and electroencephalography (EEG). Simultaneous EEG-fMRI data in epileptic WAG/Rij rats in comparison to nonepileptic Wistar controls were acquired at 9.4 T. Regions showing cortical fMRI increases during SWDs were used to define reference regions for connectivity analysis to investigate whether chronic seizure activity is associated with changes in network resting functional connectivity. We observed high degrees of cortical-cortical correlations in all WAG/Rij rats at rest (when no SWDs were present), but not in nonepileptic controls. Strongest connectivity was seen between regions most intensely involved in seizures, mainly in the bilateral somatosensory and adjacent cortices. Group statistics revealed that resting interhemispheric cortical-cortical correlations were significantly higher in WAG/Rij rats compared to nonepileptic controls. These findings suggest that activity-dependent plasticity may lead to long-term changes in epileptic networks even at rest. The results show a marked difference between the epileptic and nonepileptic animals in cortical-cortical connectivity, indicating that this may be a useful interictal biomarker associated with the epileptic state.