A High-Resolution LED Stimulator for Steady-State Visual Stimulation: Customizable, Affordable, and Open Source.

Visually evoked steady-state potentials (SSVEPs) are neural responses elicited by visual stimuli oscillating at specific frequencies. In this study, we introduce a novel LED stimulator system explicitly designed for steady-state visual stimulation, offering precise control over visual stimulus parameters, including frequency resolution, luminance, and the ability to control the phase at the end of the stimulation. The LED stimulator provides a personalized, modular, and affordable option for experimental setups. Based on the Teensy 3.2 board, the stimulator utilizes direct digital synthesis and pulse width modulation techniques to control the LEDs. We validated its performance through four experiments: the first two measured LED light intensities directly, while the last two assessed the stimulator's impact on EEG recordings. The results demonstrate that the stimulator can deliver a stimulus suitable for generating SSVEPs with the desired frequency and phase resolution. As an open source resource, we provide comprehensive documentation, including all necessary codes and electrical diagrams, which facilitates the system's replication and adaptation for specific experimental requirements, enhancing its potential for widespread use in the field of neuroscience setups.

A mean-field model of glutamate and GABA synaptic dynamics for functional MRS.

Advances in functional magnetic resonance spectroscopy (fMRS) have enabled the quantification of activity-dependent changes in neurotransmitter concentrations in vivo. However, the physiological basis of the large changes in GABA and glutamate observed by fMRS (>10%) over short time scales of less than a minute remain unclear as such changes cannot be accounted for by known synthesis or degradation metabolic pathways. Instead, it has been hypothesized that fMRS detects shifts in neurotransmitter concentrations as they cycle from presynaptic vesicles, where they are largely invisible, to extracellular and cytosolic pools, where they are detectable. The present paper uses a computational modelling approach to demonstrate the viability of this hypothesis. A new mean-field model of the neural mechanisms generating the fMRS signal in a cortical voxel is derived. The proposed macroscopic mean-field model is based on a microscopic description of the neurotransmitter dynamics at the level of the synapse. Specifically, GABA and glutamate are assumed to cycle between three metabolic pools: packaged in the vesicles; active in the synaptic cleft; and undergoing recycling and repackaging in the astrocytic or neuronal cytosol. Computational simulations from the model are used to generate predicted changes in GABA and glutamate concentrations in response to different types of stimuli including pain, vision, and electric current stimulation. The predicted changes in the extracellular and cytosolic pools corresponded to those reported in empirical fMRS data. Furthermore, the model predicts a selective control mechanism of the GABA/glutamate relationship, whereby inhibitory stimulation reduces both neurotransmitters, whereas excitatory stimulation increases glutamate and decreases GABA. The proposed model bridges between neural dynamics and fMRS and provides a mechanistic account for the activity-dependent changes in the glutamate and GABA fMRS signals. Lastly, these results indicate that echo-time may be an important timing parameter that can be leveraged to maximise fMRS experimental outcomes.

Uncovering hidden resting state dynamics: A new perspective on auditory verbal hallucinations.

In the absence of sensory stimulation, the brain transits between distinct functional networks. Network dynamics such as transition patterns and the time the brain stays in each network link to cognition and behavior and are subject to much investigation. Auditory verbal hallucinations (AVH), the temporally fluctuating unprovoked experience of hearing voices, are associated with aberrant resting state network activity. However, we lack a clear understanding of how different networks contribute to aberrant activity over time. An accurate characterization of latent network dynamics and their relation to neurocognitive changes necessitates methods that capture the sub-second temporal fluctuations of the networks' functional connectivity signatures. Here, we critically evaluate the assumptions and sensitivity of several approaches commonly used to assess temporal dynamics of brain connectivity states in M/EEG and fMRI research, highlighting methodological constraints and their clinical relevance to AVH. Identifying altered brain connectivity states linked to AVH can facilitate the detection of predictive disease markers and ultimately be valuable for generating individual risk profiles, differential diagnosis, targeted intervention, and treatment strategies.

C-low threshold mechanoafferent targeted dynamic touch modulates stress resilience in rats exposed to chronic mild stress.

Affiliative tactile interactions buffer social mammals against neurobiological and behavioral effects of stress. The aim of this study was to investigate the cutaneous mechanisms underlying such beneficial consequences of touch by determining whether daily stroking, specifically targeted to activate a velocity/force tuned class of low-threshold c-fiber mechanoreceptor (CLTM), confers resilience against established markers of chronic unpredictable mild stress (CMS). Adult male Sprague Dawley rats were exposed to 2 weeks of CMS. Throughout the CMS protocol, some rats were stroked daily, either at CLTM optimal velocity (5 cm/s) or outside the CLTM optimal range (30 cm/s). A third CMS exposed group did not receive any tactile stimulation. The effect of CMS on serum corticosterone levels, anxiety- and depressive-like behaviors in these three groups was assessed in comparison to a control group of non-CMS exposed rats. While stroking did not mitigate the effects of CMS on body weight gain, CLTM optimal velocity stroking did significantly reduce CMS-induced elevations in corticosterone following an acute forced-swim. Rats receiving CLTM optimal stroking also showed significantly fewer anxiety-like behaviors (elevated plus-maze) than the other CMS exposed rats. In terms of depressive-like behavior, whereas the same velocity-specific resilience was observed in a forced-swim test and social interaction test both groups of stroked rats spent significantly less time interacting than control rats, though they also spent significantly less time in the corner than non-stroked CMS rats. Together, these findings support the theory CLTMs play a functional role in regulating the physiological condition of the body.

Cortical ignition dynamics is tightly linked to the core organisation of the human connectome.

The capability of cortical regions to flexibly sustain an "ignited" state of activity has been discussed in relation to conscious perception or hierarchical information processing. Here, we investigate how the intrinsic propensity of different regions to get ignited is determined by the specific topological organisation of the structural connectome. More specifically, we simulated the resting-state dynamics of mean-field whole-brain models and assessed how dynamic multistability and ignition differ between a reference model embedding a realistic human connectome, and alternative models based on a variety of randomised connectome ensembles. We found that the strength of global excitation needed to first trigger ignition in a subset of regions is substantially smaller for the model embedding the empirical human connectome. Furthermore, when increasing the strength of excitation, the propagation of ignition outside of this initial core-which is able to self-sustain its high activity-is way more gradual than for any of the randomised connectomes, allowing for graded control of the number of ignited regions. We explain both these assets in terms of the exceptional weighted core-shell organisation of the empirical connectome, speculating that this topology of human structural connectivity may be attuned to support enhanced ignition dynamics.

Evidence for frequency-dependent cortical plasticity in the human brain.

Frequency-dependent plasticity (FDP) describes adaptation at the synapse in response to stimulation at different frequencies. Its consequence on the structure and function of cortical networks is unknown. We tested whether cortical "resonance," favorable stimulation frequencies at which the sensory cortices respond maximally, influenced the impact of FDP on perception, functional topography, and connectivity of the primary somatosensory cortex using psychophysics and functional imaging (fMRI). We costimulated two digits on the hand synchronously at, above, or below the resonance frequency of the somatosensory cortex, and tested subjects' accuracy and speed on tactile localization before and after costimulation. More errors and slower response times followed costimulation at above- or below-resonance, respectively. Response times were faster after at-resonance costimulation. In the fMRI, the cortical representations of the two digits costimulated above-resonance shifted closer, potentially accounting for the poorer performance. Costimulation at-resonance did not shift the digit regions, but increased the functional coupling between them, potentially accounting for the improved response time. To relate these results to synaptic plasticity, we simulated a network of oscillators incorporating Hebbian learning. Two neighboring patches embedded in a cortical sheet, mimicking the two digit regions, were costimulated at different frequencies. Network activation outside the stimulated patches was greatest at above-resonance frequencies, reproducing the spread of digit representations seen with fMRI. Connection strengths within the patches increased following at-resonance costimulation, reproducing the increased fMRI connectivity. We show that FDP extends to the cortical level and is influenced by cortical resonance.

Improving EEG Muscle Artifact Removal With an EMG Array.

Removal of artifacts induced by muscle activity is crucial for analysis of the electroencephalogram (EEG), and continues to be a challenge in experiments where the subject may speak, change facial expressions, or move. Ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) has been proven to be an efficient method for denoising of EEG contaminated with muscle artifacts. EEMD-CCA, likewise the majority of algorithms, does not incorporate any statistical information of the artifact, namely, electromyogram (EMG) recorded over the muscles actively contaminating the EEG. In this paper, we propose to extend EEMD-CCA in order to include an EMG array as information to aid the removal of artifacts, assessing the performance gain achieved when the number of EMG channels grow. By filtering adaptively (recursive least squares, EMG array as reference) each component resulting from CCA, we aim to ameliorate the distortion of brain signals induced by artifacts and denoising methods. We simulated several noise scenarios based on a linear contamination model, between real and synthetic EEG and EMG signals, and varied the number of EMG channels available to the filter. Our results exhibit a substantial improvement in the performance as the number of EMG electrodes increase from 2 to 16. Further increasing the number of EMG channels up to 128 did not have a significant impact on the performance. We conclude by recommending the use of EMG electrodes to filter components, as it is a computationally inexpensive enhancement that impacts significantly on performance using only a few electrodes.

Cued Reactivation of Motor Learning during Sleep Leads to Overnight Changes in Functional Brain Activity and Connectivity.

Sleep plays a role in memory consolidation. This is demonstrated by improved performance and neural plasticity underlying that improvement after sleep. Targeted memory reactivation (TMR) allows the manipulation of sleep-dependent consolidation through intentionally biasing the replay of specific memories in sleep, but the underlying neural basis of these altered memories remains unclear. We use functional magnetic resonance imaging (fMRI) to show a change in the neural representation of a motor memory after targeted reactivation in slow-wave sleep (SWS). Participants learned two serial reaction time task (SRTT) sequences associated with different auditory tones (high or low pitch). During subsequent SWS, one sequence was reactivated by replaying the associated tones. Participants were retested on both sequences the following day during fMRI. As predicted, they showed faster reaction times for the cued sequence after targeted memory reactivation. Furthermore, increased activity in bilateral caudate nucleus and hippocampus for the cued relative to uncued sequence was associated with time in SWS, while increased cerebellar and cortical motor activity was related to time in rapid eye movement (REM) sleep. Functional connectivity between the caudate nucleus and hippocampus was also increased after targeted memory reactivation. These findings suggest that the offline performance gains associated with memory reactivation are supported by altered functional activity in key cognitive and motor networks, and that this consolidation is differentially mediated by both REM sleep and SWS.

Identification of memory reactivation during sleep by EEG classification.

Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Neural reactivation was classified above chance in all participants when TMR was applied in SWS, and in 5 of the 14 participants to whom TMR was applied in N2. Classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be valuable for future investigations of memory consolidation.

Cortical Resonance Frequencies Emerge from Network Size and Connectivity.

Neural oscillations occur within a wide frequency range with different brain regions exhibiting resonance-like characteristics at specific points in the spectrum. At the microscopic scale, single neurons possess intrinsic oscillatory properties, such that is not yet known whether cortical resonance is consequential to neural oscillations or an emergent property of the networks that interconnect them. Using a network model of loosely-coupled Wilson-Cowan oscillators to simulate a patch of cortical sheet, we demonstrate that the size of the activated network is inversely related to its resonance frequency. Further analysis of the parameter space indicated that the number of excitatory and inhibitory connections, as well as the average transmission delay between units, determined the resonance frequency. The model predicted that if an activated network within the visual cortex increased in size, the resonance frequency of the network would decrease. We tested this prediction experimentally using the steady-state visual evoked potential where we stimulated the visual cortex with different size stimuli at a range of driving frequencies. We demonstrate that the frequency corresponding to peak steady-state response inversely correlated with the size of the network. We conclude that although individual neurons possess resonance properties, oscillatory activity at the macroscopic level is strongly influenced by network interactions, and that the steady-state response can be used to investigate functional networks.

A Systematic Review of Efficacy of the Attention Training Technique in Clinical and Nonclinical Samples.

OBJECTIVE: The Attention Training Technique (ATT; Wells, 1990) is a brief metacognitive treatment strategy aimed at remediating self-focused processing and increasing attention flexibility in psychological disorder. METHOD: We systematically reviewed and examined the efficacy of ATT in clinical and nonclinical samples. Scientific databases were searched from 1990 to 2014 and 10 studies (total N = 295) met inclusion criteria. Single-case data were meta-analyzed using the improvement rate difference, and standardized between and within-group effect sizes (ESs) were examined across 4 analogue randomized controlled trials (RCTs). RESULTS: Single-case outcomes indicated that ATT yields large ES estimates (pooled ES range: 0.74-1.00) for anxiety and depressive disorders. Standardized ESs across the RCTs indicated that ATT yields greater treatment gains than reference groups across majority outcomes (adjusted Cohen's d range: 0.40-1.23). CONCLUSIONS: These preliminary results suggest ATT may be effective in treating anxiety and depressive disorders and help remediate some symptoms of schizophrenia. Although a limited number of studies with small sample sizes warrants caution of interpretation, ATT appears promising and future studies will benefit from adequately powered RCTs.

Cued memory reactivation during slow-wave sleep promotes explicit knowledge of a motor sequence.

Memories are gradually consolidated after initial encoding, and this can sometimes lead to a transition from implicit to explicit knowledge. The exact physiological processes underlying this reorganization remain unclear. Here, we used a serial reaction time task to determine whether targeted memory reactivation (TMR) of specific memory traces during slow-wave sleep promotes the emergence of explicit knowledge. Human participants learned two 12-item sequences of button presses (A and B). These differed in both cue order and in the auditory tones associated with each of the four fingers (one sequence had four higher-pitched tones). Subsequent overnight sleep was monitored, and the tones associated with one learned sequence were replayed during slow-wave sleep. After waking, participants demonstrated greater explicit knowledge (p = 0.005) and more improved procedural skill (p = 0.04) for the cued sequence relative to the uncued sequence. Furthermore, fast spindles (13.5-15 Hz) at task-related motor regions predicted overnight enhancement in procedural skill (r = 0.71, p = 0.01). Auditory cues had no effect on post-sleep memory performance in a control group who received TMR before sleep. These findings suggest that TMR during sleep can alter memory representations and promote the emergence of explicit knowledge, supporting the notion that reactivation during sleep is a key mechanism in this process.

Differential Phonological and Semantic Modulation of Neurophysiological Responses to Visual Word Recognition.

BACKGROUND: Reading words for meaning relies on orthographic, phonological and semantic processing. The triangle model implicates a direct orthography-to-semantics pathway and a phonologically mediated orthography-to-semantics pathway, which interact with each other. The temporal evolution of processing in these routes is not well understood, although theoretical evidence predicts early phonological processing followed by interactive phonological and semantic processing. METHOD: This study used electroencephalography-event-related potential (ERP) analysis and magnetoencephalography (MEG) source localisation to identify temporal markers and the corresponding neural generators of these processes in early (∼200 ms) and late (∼400 ms) neurophysiological responses to visual words, pseudowords and consonant strings. RESULTS: ERP showed an effect of phonology but not semantics in both time windows, although at ∼400 ms there was an effect of stimulus familiarity. Phonological processing at ~200 ms was localised to the left occipitotemporal cortex and the inferior frontal gyrus. At 400 ms, there was continued phonological processing in the inferior frontal gyrus and additional semantic processing in the anterior temporal cortex. There was also an area in the left temporoparietal junction which was implicated in both phonological and semantic processing. In ERP, the semantic response at ∼400 ms appeared to be masked by concurrent processes relating to familiarity, while MEG successfully differentiated these processes. DISCUSSION: The results support the prediction of early phonological processing followed by an interaction of phonological and semantic processing during word recognition. Neuroanatomical loci of these processes are consistent with previous neuropsychological and functional magnetic resonance imaging studies. The results also have implications for the classical interpretation of N400-like responses as markers for semantic processing.

Decision-making and trait impulsivity in bipolar disorder are associated with reduced prefrontal regulation of striatal reward valuation.

Bipolar disorder is characterized by impaired decision-making captured in impulsivity and risk-taking. We sought to determine whether this is driven by a failure to effectively weight the lower-order goal of obtaining a strongly desired reward in relation to higher-order goals, and how this relates to trait impulsivity and risk-taking. We hypothesized that in bipolar disorder the weighting of valuation signals converging on ventromedial prefrontal cortex are more heavily weighted towards ventral striatum inputs (lower-order), with less weighting of dorsolateral prefrontal cortex inputs (higher-order). Twenty euthymic patients with bipolar disorder not in receipt of antipsychotic medication and 20 case-matched controls performed a roulette task during functional magnetic resonance imaging. Activity in response to high-probability ('safe') and low-probability ('risky') prospects was measured during both anticipation, and outcome. In control subjects, anticipatory and outcome-locked activity in dorsolateral prefrontal cortex was greater for safe than risky reward prospects. The bipolar disorder group showed the opposite pattern with preferential response to risky rewards. This group also showed increased anticipatory and outcome-locked activity in ventral striatum in response to rewards. In control subjects, however, ventromedial prefrontal activation was positively associated with both ventral striatum and dorsolateral prefrontal activity; patients evidenced a strong positive association with ventral striatum, but a negative association with dorsolateral prefrontal cortex. Response to high-probability rewards in dorsolateral prefrontal cortex was inversely associated with trait impulsivity and risk-taking in the bipolar disorder group. Our findings suggest that clinically impulsive and risky decision-making are related to subjective valuation that is biased towards lower-order preference, with diminished integration of higher-order goals. The findings extend a functional neuroanatomical account of disorders characterized by clinically impulsive decision-making, and provide targets for evaluating interventions that foster self-control.

Electrode Selection and Speech Understanding in Patients With Auditory Brainstem Implants.

OBJECTIVES: The objective of this study was to evaluate whether speech understanding in auditory brainstem implant (ABI) users who have a tumor pathology could be improved by the selection of a subset of electrodes that were appropriately pitch ranked and distinguishable. It was hypothesized that disordered pitch or spectral percepts and channel interactions may contribute significantly to the poor outcomes in most ABI users. DESIGN: A single-subject design was used with five participants. Pitch ranking information for all electrodes in the patients' clinic maps was obtained using a pitch ranking task and previous pitch ranking information from clinic sessions. A multidimensional scaling task was used to evaluate the stimulus space evoked by stimuli on the same set of electrodes. From this information, a subset of four to six electrodes was chosen and a new map was created, using just this subset, that the subjects took home for 1 month's experience. Closed-set consonant and vowel perception and sentences in quiet were tested at three sessions: with the clinic map before the test map was given, after 1 month with the test map, and after an additional 2 weeks with their clinic map. RESULTS: The results of the pitch ranking and multidimensional scaling procedures confirmed that the ABI users did not have a well-ordered set of percepts related to electrode position, thus supporting the proposal that difficulty in processing of spectral information may contribute to poor speech understanding. However, none of the subjects benefited from a map that reduced the stimulation electrode set to a smaller number of electrodes that were well ordered in place pitch. CONCLUSIONS: Although poor spectral processing may contribute to poor understanding in ABI users, it is not likely to be the sole contributor to poor outcomes.

An empirical study of defensive avoidance in paranoia.

BACKGROUND: There is controversy as to whether psychological defensive avoidance is associated with paranoia. AIMS: To elucidate whether "Poor-me" paranoid patients, who believe that the persecution they perceive is undeserved, show more prominent avoidance of negative thoughts about themselves than healthy and clinical controls. METHOD: The act of avoidance of aversive mental contents was assessed in 27 healthy controls and 48 patients with poor-me, bad-me (perceived to be deserved) or no paranoia. Defensive avoidance was assessed via established questionnaires, a novel task based on self-discrepancy theory and research-clinician ratings. RESULTS: Participants in all groups showed substantial levels of verbal defensive avoidance. Paranoia was associated with reduced self-reported tolerance of negative mental contents (high Experiential Avoidance, EA). Contrary to our hypotheses, poor-me and bad-me patients showed similar EA. All participant groups showed similar levels of verbal defensive avoidance. CONCLUSION: The findings do not support an association of psychological avoidance with paranoia.

The feedback-related negativity signals salience prediction errors, not reward prediction errors.

Modulations of the feedback-related negativity (FRN) event-related potential (ERP) have been suggested as a potential biomarker in psychopathology. A dominant theory about this signal contends that it reflects the operation of the neural system underlying reinforcement learning in humans. The theory suggests that this frontocentral negative deflection in the ERP 230-270 ms after the delivery of a probabilistic reward expresses a prediction error signal derived from midbrain dopaminergic projections to the anterior cingulate cortex. We tested this theory by investigating whether FRN will also be observed for an inherently aversive outcome: physical pain. In another session, the outcome was monetary reward instead of pain. As predicted, unexpected reward omissions (a negative reward prediction error) yielded a more negative deflection relative to unexpected reward delivery. Surprisingly, unexpected pain omission (a positive reward prediction error) also yielded a negative deflection relative to unexpected pain delivery. Our data challenge the theory by showing that the FRN expresses aversive prediction errors with the same sign as reward prediction errors. Both FRNs were spatiotemporally and functionally equivalent. We suggest that FRN expresses salience prediction errors rather than reward prediction errors.

When the brain expects pain: common neural responses to pain anticipation are related to clinical pain and distress in fibromyalgia and osteoarthritis.

Supraspinal processes in humans can have a top-down enhancing effect on nociceptive processing in the brain and spinal cord. Studies have begun to suggest that such influences occur in conditions such as fibromyalgia (FM), but it is not clear whether this is unique to FM pain or common to other forms of chronic pain, such as that associated with osteoarthritis (OA). We assessed top-down processes by measuring anticipation-evoked potentials and their estimated sources, just prior (< 500 ms) to laser heat pain stimulation, in 16 patients with FM, 16 patients with OA and 15 healthy participants, by using whole-brain statistical parametric mapping. Clinical pain and psychological coping factors (pain catastrophizing, anxiety, and depression) were well matched between the patient groups, such that these did not confound our comparisons between FM and OA patients. For the same level of heat pain, insula activity was significantly higher in FM patients than in the other two groups during anticipation, and correlated with the intensity and extent of reported clinical pain. However, the same anticipatory insula activity also correlated with OA pain, and with the number of tender points across the two patient groups, suggesting common central mechanisms of tenderness. Activation in the dorsolateral prefrontal cortex was reduced during anticipation in both patient groups, and was related to less effective psychological coping. Our findings suggest common neural correlates of pain and tenderness in FM and OA that are enhanced in FM but not unique to this condition.

An ERP investigation of conditional reasoning with emotional and neutral contents.

In two experiments we investigate conditional reasoning using event-related potentials (ERPs). Our goal was to examine the time course of inference making in two conditional forms, one logically valid (Modus Ponens, MP) and one logically invalid (Affirming the Consequent, AC). We focus particularly on the involvement of semantically-based inferential processes potentially marked by modulations of the N400. We also compared reasoning about emotional and neutral contents with separate sets of stimuli of differing linguistic complexity across the two experiments. Both MP and AC modulated the N400 component, suggesting the involvement of a semantically-based inferential mechanism common across different logical forms, content types, and linguistic features of the problems. Emotion did not have an effect on early components, and did not interact with components related to inference making. There was a main effect of emotion in the 800-1050 ms time window, consistent with an effect on sustained attention. The results suggest that conditional reasoning is not a purely formal process but that it importantly implicates semantic processing, and that the effect of emotion on reasoning does not primarily operate through a modulation of early automatic stages of information processing.

Bayesian inferences about the self (and others): a review.

Viewing the brain as an organ of approximate Bayesian inference can help us understand how it represents the self. We suggest that inferred representations of the self have a normative function: to predict and optimise the likely outcomes of social interactions. Technically, we cast this predict-and-optimise as maximising the chance of favourable outcomes through active inference. Here the utility of outcomes can be conceptualised as prior beliefs about final states. Actions based on interpersonal representations can therefore be understood as minimising surprise - under the prior belief that one will end up in states with high utility. Interpersonal representations thus serve to render interactions more predictable, while the affective valence of interpersonal inference renders self-perception evaluative. Distortions of self-representation contribute to major psychiatric disorders such as depression, personality disorder and paranoia. The approach we review may therefore operationalise the study of interpersonal representations in pathological states.