Temporal imprecision of phase coherence in schizophrenia and psychosis-dynamic mechanisms and diagnostic marker.

Schizophrenia (SCZ) is a complex disorder in which various pathophysiological models have been postulated. Brain imaging studies using EEG/MEG and fMRI show altered amplitude and, more recently, decrease in phase coherence in response to external stimuli. What are the dynamic mechanisms of such phase incoherence, and can it serve as a differential-diagnostic marker? Addressing this gap in our knowledge, we uniquely combine a review of previous findings, novel empirical data, and computational-dynamic simulation. The main findings are: (i) the review shows decreased phase coherence in SCZ across a variety of different tasks and frequencies, e.g., task- and frequency-unspecific, which is further supported by our own novel data; (ii) our own data demonstrate diagnostic specificity of decreased phase coherence for SCZ as distinguished from major depressive disorder; (iii) simulation data exhibit increased phase offset in SCZ leading to a precision index, in the millisecond range, of the phase coherence relative to the timing of the external stimulus. Together, we demonstrate the key role of temporal imprecision in phase coherence of SCZ, including its mechanisms (phase offsets, precision index) on the basis of which we propose a phase-based temporal imprecision model of psychosis (PTP). The PTP targets a deeper dynamic layer of a basic disturbance. This converges well with other models of psychosis like the basic self-disturbance and time-space experience changes, as discussed in phenomenological and spatiotemporal psychopathology, as well as with the models of aberrant predictive coding and disconnection as in computational psychiatry. Finally, our results show that temporal imprecision as manifest in decreased phase coherence is a promising candidate biomarker for clinical differential diagnosis of SCZ, and more broadly, psychosis.

Increased noise relates to abnormal excitation-inhibition balance in schizophrenia: a combined empirical and computational study.

Electroencephalography studies link sensory processing issues in schizophrenia to increased noise level-noise here is background spontaneous activity-as measured by the signal-to-noise ratio. The mechanism, however, of such increased noise is unknown. We investigate if this relates to changes in cortical excitation-inhibition balance, which has been observed to be atypical in schizophrenia, by combining electroencephalography and computational modeling. Our electroencephalography task results, for which the local field potentials can be used as a proxy, show lower signal-to-noise ratio due to higher noise in schizophrenia. Both electroencephalography rest and task states exhibit higher levels of excitation in the functional excitation-inhibition (as a proxy of excitation-inhibition balance). This suggests a relationship between increased noise and atypical excitation in schizophrenia, which was addressed by using computational modeling. A Leaky Integrate-and-Fire model was used to simulate the effects of varying degrees of noise on excitation-inhibition balance, local field potential, NMDA current, and . Results show a noise-related increase in the local field potential, excitation in excitation-inhibition balance, pyramidal NMDA current, and spike rate. Mutual information and mediation analysis were used to explore a cross-level relationship, showing that the cortical local field potential plays a key role in transferring the effect of noise to the cellular population level of NMDA.

Intrinsic neural timescales mediate the cognitive bias of self - temporal integration as key mechanism.

Our perceptions and decisions are not always objectively correct as they are featured by a bias related to our self. What are the behavioral, neural, and computational mechanisms of such cognitive bias? Addressing this yet unresolved question, we here investigate whether the cognitive bias is related to temporal integration and segregation as mediated by the brain's Intrinsic neural timescales (INT). Using Signal Detection Theory (SDT), we operationalize the cognitive bias by the Criterion C as distinguished from the sensitivity index d'. This was probed in a self-task based on morphed self- and other faces. Behavioral data demonstrate clear cognitive bias, i.e., Criterion C. That was related to the EEG-based INT as measured by the autocorrelation window (ACW) in especially the transmodal regions dorsolateral prefrontal cortex (dlPFC) and default-mode network (DMN) as distinct from unimodal visual cortex. Finally, simulation of the same paradigm in a large-scale network model shows high degrees of temporal integration of temporally distinct inputs in CMS/DMN and dlPFC while temporal segregation predominates in visual cortex. Together, we demonstrate a key role of INT-based temporal integration in CMS/DMN and dlPFC including its relation to the brain's uni-transmodal topographical organization in mediating the cognitive bias of our self.

It's in the Timing: Reduced Temporal Precision in Neural Activity of Schizophrenia.

Studies of perception and cognition in schizophrenia (SCZ) show neuronal background noise (ongoing activity) to intermittently overwhelm the processing of external stimuli. This increased noise, relative to the activity evoked by the stimulus, results in temporal imprecision and higher variability of behavioral responses. What, however, are the neural correlates of temporal imprecision in SCZ behavior? We first report a decrease in electroencephalography signal-to-noise ratio (SNR) in two SCZ datasets and tasks in the broadband (1-80 Hz), theta (4-8 Hz), and alpha (8-13 Hz) bands. SCZ participants also show lower inter-trial phase coherence (ITPC)-consistency over trials in the phase of the signal-in theta. From these ITPC results, we varied phase offsets in a computational simulation, which illustrated phase-based temporal desynchronization. This modeling also provided a necessary link to our results and showed decreased neural synchrony in SCZ in both datasets and tasks when compared with healthy controls. Finally, we showed that reduced SNR and ITPC are related and showed a relationship to temporal precision on the behavioral level, namely reaction times. In conclusion, we demonstrate how temporal imprecision in SCZ neural activity-reduced relative signal strength and phase coherence-mediates temporal imprecision on the behavioral level.

Variability and task-responsiveness of electrophysiological dynamics: Scale-free stability and oscillatory flexibility.

Cortical oscillations and scale-free neural activity are thought to influence a variety of cognitive functions, but their differential relationships to neural stability and flexibility has never been investigated. Based on the existing literature, we hypothesize that scale-free and oscillatory processes in the brain exhibit different trade-offs between stability and flexibility; specifically, cortical oscillations may reflect variable, task-responsive aspects of brain activity, while scale-free activity is proposed to reflect a more stable and task-unresponsive aspect. We test this hypothesis using data from two large-scale MEG studies (HCP: n = 89; CamCAN: n = 195), operationalizing stability and flexibility by task-responsiveness and spontaneous intra-subject variability in resting state. We demonstrate that the power-law exponent of scale-free activity is a highly stable parameter, which responds little to external cognitive demands and shows minimal spontaneous fluctuations over time. In contrast, oscillatory power, particularly in the alpha range (8-13 Hz), responds strongly to tasks and exhibits comparatively large spontaneous fluctuations over time. In sum, our data support differential roles for oscillatory and scale-free activity in the brain with respect to neural stability and flexibility. This result carries implications for criticality-based theories of scale-free activity, state-trait models of variability, and homeostatic views of the brain with regulated variables vs. effectors.

Temporal continuity of self: Long autocorrelation windows mediate self-specificity.

The self is characterized by an intrinsic temporal component consisting in continuity across time. On the neural level, this temporal continuity manifests in the brain's intrinsic neural timescales (INT) that can be measured by the autocorrelation window (ACW). Recent EEG studies reveal a relationship between resting state ACW and self-consciousness. However, it remains unclear whether ACW exhibits different degrees of task-related changes during self-specific compared to non-self-specific activities. To this end, participants in our study initially recorded an eight-minute autobiographical narrative. Following a resting-state session, participants were presented with their own narrative and the narrative of a stranger while undergoing concurrent EEG recording. Behaviorally, subjects evaluated both of the narratives and indicated their perceptions of positivity or negativity on a moment-to-moment basis by positioning a cursor relative to the center of the computer screen. Our results indicate: (a) greater spatial extension and velocity in the behavioral cursor movement during the self narrative assessment compared to the non-self narrative assessment; and (b) longer neural ACWs in response to the self- compared to the non-self narrative and rest. These findings demonstrate the importance of longer temporal windows in neural activity measured by ACW for self-specificity. More broadly, the results highlight the relevance of temporal continuity for the self on the neural level. Such temporal continuity may, correspondingly, also manifest on the psychological level as a "common currency" between brain and self.

Prestimulus dynamics blend with the stimulus in neural variability quenching.

Neural responses to the same stimulus show significant variability over trials, with this variability typically reduced (quenched) after a stimulus is presented. This trial-to-trial variability (TTV) has been much studied, however how this neural variability quenching is influenced by the ongoing dynamics of the prestimulus period is unknown. Utilizing a human intracranial stereo-electroencephalography (sEEG) data set, we investigate how prestimulus dynamics, as operationalized by standard deviation (SD), shapes poststimulus activity through trial-to-trial variability (TTV). We first observed greater poststimulus variability quenching in those real trials exhibiting high prestimulus variability as observed in all frequency bands. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. Lastly, we replicate our findings in a separate EEG dataset and extend them by finding that trials with high prestimulus variability in the theta and alpha bands had faster reaction times. Together, our results demonstrate that stimulus-related activity, including its variability, is a blend of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period - the state at stimulus onset - with the second dwarfing the influence of the first.

Childhood trauma mediates repetitive transcranial magnetic stimulation efficacy in major depressive disorder.

Childhood trauma is one of the most prominent risk factors in developing major depressive disorder (MDD) and may lead to unfavorable outcomes of pharmacotherapy and psychotherapy in MDD. While how it modulates the treatment outcome of the repetitive transcranial magnetic stimulation (rTMS) and how sex difference may play a role in mediating this relationship remain unknown. To evaluate this question, 51 (37 women) MDD patients were treated with 10 Hz rTMS to the left dorsolateral prefrontal cortex (lDLPFC). The experience of childhood trauma was quantified by the Childhood Traumatic Questionnaire (CTQ). The depressive severity was assessed by Hamilton Depression Scale (HAMD) and Beck Depression Inventory (BDI) as the primary and secondary assessments. Beck Hopelessness Scale (BHS) and Hamilton Anxiety Scale (HAMA) were also assessed for further confirmation. Thirty-six (70.6%) participants showed a response including 17 (33.3%) achieving remission to the rTMS treatment. The alleviation of depressive symptoms was negatively correlated with the CTQ scores, specifically in women but not men, in subjective BDI and BHS, but not objective HAMD or HAMA. We demonstrate that childhood trauma negatively affects the subjective perception of rTMS-lDLPFC treatment outcomes in female MDD patients. This highlights the importance of measuring childhood trauma-related symptoms in routine clinical rTMS treatment, as they may impact perceived efficacy.

Temporal integration as "common currency" of brain and self-scale-free activity in resting-state EEG correlates with temporal delay effects on self-relatedness.

The self is a multifaceted phenomenon that integrates information and experience across multiple time scales. How temporal integration on the psychological level of the self is related to temporal integration on the neuronal level remains unclear. To investigate temporal integration on the psychological level, we modified a well-established self-matching paradigm by inserting temporal delays. On the neuronal level, we indexed temporal integration in resting-state EEG by two related measures of scale-free dynamics, the power law exponent and autocorrelation window. We hypothesized that the previously established self-prioritization effect, measured as decreased response times or increased accuracy for self-related stimuli, would change with the insertion of different temporal delays between the paired stimuli, and that these changes would be related to temporal integration on the neuronal level. We found a significant self-prioritization effect on accuracy in all conditions with delays, indicating stronger temporal integration of self-related stimuli. Further, we observed a relationship between temporal integration on psychological and neuronal levels: higher degrees of neuronal integration, that is, higher power-law exponent and longer autocorrelation window, during resting-state EEG were related to a stronger increase in the self-prioritization effect across longer temporal delays. We conclude that temporal integration on the neuronal level serves as a template for temporal integration of the self on the psychological level. Temporal integration can thus be conceived as the "common currency" of neuronal and psychological levels of self.

Neural variability quenching during decision-making: Neural individuality and its prestimulus complexity.

The spontaneous activity of the brain interacts with stimulus-induced activity which is manifested in event-related amplitude and its trial-to-trial variability (TTV). TTV describes the variability in the amplitude of the stimulus-evoked response across trials, and it is generally observed to be reduced, or quenched. While such TTV quenching has been observed on both the cellular and regional levels, its exact behavioral relevance and neuronal basis remains unclear. Applying a novel paradigm for testing neural markers of individuality in internally-guided decision-making, we here investigated whether TTV (i) represents an individually specific response by comparing individualized vs shared stimuli; and (ii) is mediated by the complexity of prestimulus activity as measured by the Lempel-Ziv Complexity index (LZC). We observed that TTV - and other electrophysiological markers such as ERP, ERSP, and ITC - showed first significant differences between individualized and shared stimuli (while controlling for task-related effects) specifically in the alpha and beta frequency bands, and secondly that TTV in the beta band correlated significantly with reaction time and eLORETA activity. Moreover, we demonstrate that the complexity (LZC) of neuronal activity is higher in the prestimulus period while it decreases during the poststimulus period, with the former also correlating specifically with poststimulus individualized TTV in alpha (but not with shared TTV). Together, our results show that the TTV represents a marker of 'neural individualization' which, being related to internal processes on both neural and psychological levels, is mediated by the information complexity of prestimulus activity. More generally, our results inform the pre-post-stimulus dynamics of rest-stimulus interaction, which is a basic and ubiquitous neural phenomenon in the brain and highly relevant for mental features including their individuality.

From neuronal to psychological noise - Long-range temporal correlations in EEG intrinsic activity reduce noise in internally-guided decision making.

Our personal internal preferences while making decisions are usually consistent. Recent psychological studies, however, show observable variability of internal criteria occurs by random noise. The neural correlates of said random noise - an instance of 'psychological noise' - yet remain unclear. Combining simulation, behavioral, and neural approaches, our study investigated the psychological and neural correlates of such random noise in our internal criteria during decision making. We applied well-established decision-making tasks which relied on either internal criteria - occupation choice task as internally-guided decision making (IDM) - or external criteria - salary judgment task as externally-guided decision making (EDM). Subjects underwent EEG for resting state and task-evoked activity during IDM and EDM. We measured resting state long-range temporal correlation (LRTC) in the alpha frequency range as the index of neuronal noise. Based on our simulation, we identified a measure of psychological noise (as distinguished from true preference change) in IDM. The main finding shows that the indices for psychological noise are directly related to frontocentral LRTC in the alpha range. Higher degrees of frontocentral LRTC, which index lower neuronal noise, were related to lower degrees of psychological noise during IDM. This was not found during EDM. Resting state LRTC was also related to task-evoked activity, such as conflict-related negativity, during IDM only. Taken together, our data demonstrate, for the first time, the direct relationship between neuronal noise in the brain's intrinsic activity and psychological noise in the internal criteria of our decision making.

The temporal signature of self: Temporal measures of resting-state EEG predict self-consciousness.

The self is the core of our mental life. Previous investigations have demonstrated a strong neural overlap between self-related activity and resting state activity. This suggests that information about self-relatedness is encoded in our brain's spontaneous activity. The exact neuronal mechanisms of such "rest-self containment," however, remain unclear. The present EEG study investigated temporal measures of resting state EEG to relate them to self-consciousness. This was obtained with the self-consciousness scale (SCS) which measures Private, Public, and Social dimensions of self. We demonstrate positive correlations between Private self-consciousness and three temporal measures of resting state activity: scale-free activity as indexed by the power-law exponent (PLE), the auto-correlation window (ACW), and modulation index (MI). Specifically, higher PLE, longer ACW, and stronger MI were related to higher degrees of Private self-consciousness. Finally, conducting eLORETA for spatial tomography, we found significant correlation of Private self-consciousness with activity in cortical midline structures such as the perigenual anterior cingulate cortex and posterior cingulate cortex. These results were reinforced with a data-driven analysis; a machine learning algorithm accurately predicted an individual as having a "high" or "low" Private self-consciousness score based on these measures of the brain's spatiotemporal structure. In conclusion, our results demonstrate that Private self-consciousness is related to the temporal structure of resting state activity as featured by temporal nestedness (PLE), temporal continuity (ACW), and temporal integration (MI). Our results support the hypothesis that self-related information is temporally contained in the brain's resting state. "Rest-self containment" can thus be featured by a temporal signature.

Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania.

Depressive and manic phases in bipolar disorder show opposite constellations of affective, cognitive, and psychomotor symptoms. At a neural level, these may be related to topographical disbalance between large-scale networks, such as the default mode network (DMN) and sensorimotor network (SMN). We investigated topographical patterns of variability in the resting-state signal-measured by fractional SD (fSD) of the BOLD signal-of the DMN and SMN (and other networks) in two frequency bands (Slow5 and Slow4) with their ratio and clinical correlations in depressed (n = 20), manic (n = 20), euthymic (n = 20) patients, and healthy controls (n = 40). After controlling for global signal changes, the topographical balance between the DMN and SMN, specifically in the lowest frequency band, as calculated by the Slow5 fSD DMN/SMN ratio, was significantly increased in depression, whereas the same ratio was significantly decreased in mania. Additionally, Slow5 variability was increased in the DMN and decreased in the SMN in depressed patients, whereas the opposite topographical pattern was observed in mania. Finally, the Slow5 fSD DMN/SMN ratio correlated positively with clinical scores of depressive symptoms and negatively with those of mania. Results were replicated in a smaller independent bipolar disorder sample. We demonstrated topographical abnormalities in frequency-specific resting-state variability in the balance between DMN and SMN with opposing patterns in depression and mania. The Slow5 DMN/SMN ratio was tilted toward the DMN in depression but was shifted toward the SMN in mania. The Slow5 fSD DMN/SMN pattern could constitute a state-biomarker in diagnosis and therapy.

Thalamo-Sensorimotor Functional Connectivity Correlates with World Ranking of Olympic, Elite, and High Performance Athletes.

Brain plasticity studies have shown functional reorganization in participants with outstanding motor expertise. Little is known about neural plasticity associated with exceptionally long motor training or of its predictive value for motor performance excellence. The present study utilised resting-state functional magnetic resonance imaging (rs-fMRI) in a unique sample of world-class athletes: Olympic, elite, and internationally ranked swimmers (n = 30). Their world ranking ranged from 1st to 250th: each had prepared for participation in the Olympic Games. Combining rs-fMRI graph-theoretical and seed-based functional connectivity analyses, it was discovered that the thalamus has its strongest connections with the sensorimotor network in elite swimmers with the highest world rankings (career best rank: 1-35). Strikingly, thalamo-sensorimotor functional connections were highly correlated with the swimmers' motor performance excellence, that is, accounting for 41% of the individual variance in best world ranking. Our findings shed light on neural correlates of long-term athletic performance involving thalamo-sensorimotor functional circuits.

Decoupled temporal variability and signal synchronization of spontaneous brain activity in loss of consciousness: An fMRI study in anesthesia.

Two aspects of the low frequency fluctuations of spontaneous brain activity have been proposed which reflect the complex and dynamic features of resting-state activity, namely temporal variability and signal synchronization. The relationship between them, especially its role in consciousness, nevertheless remains unclear. Our study examined the temporal variability and signal synchronization of spontaneous brain activity, as well as their relationship during loss of consciousness. We applied an intra-subject design of resting-state functional magnetic resonance imaging (rs-fMRI) in two conditions: during wakefulness, and under anesthesia with clinical unconsciousness. In addition, an independent group of patients with disorders of consciousness (DOC) was included in order to test the reliability of our findings. We observed a global reduction in the temporal variability, local and distant brain signal synchronization for subjects during anesthesia. Importantly, we found a link between temporal variability and both local and distant signal synchronizations during wakefulness: the higher the degree of temporal variability, the higher its intra-regional homogeneity and inter-regional functional connectivity. In contrast, this link was broken down under anesthesia, implying a decoupling between temporal variability and signal synchronization; this decoupling was reproduced in patients with DOC. Our results suggest that there exist some as yet unclear physiological mechanisms of consciousness which "couple" the two mathematically independent measures, temporal variability and signal synchronization of spontaneous brain activity. Our findings not only extend our current knowledge of the neural correlates of anesthetic-induced unconsciousness, but have implications for both computational neural modeling and clinical practice, such as in the diagnosis of loss of consciousness in patients with DOC.

How are different neural networks related to consciousness?

OBJECTIVE: We aimed to investigate the roles of different resting-state networks in predicting both the actual level of consciousness and its recovery in brain injury patients. METHODS: We investigated resting-state functional connectivity within different networks in patients with varying levels of consciousness: unresponsive wakefulness syndrome (UWS; n = 56), minimally conscious state (MCS; n = 29), and patients with brain lesions but full consciousness (BL; n = 48). Considering the actual level of consciousness, we compared the strength of network connectivity among the patient groups. We then checked the presence of connections between specific regions in individual patients and calculated the frequency of this in the different patient groups. Considering the recovery of consciousness, we split the UWS group into 2 subgroups according to recovery: those who emerged from UWS (UWS-E) and those who remained in UWS (UWS-R). The above analyses were repeated on these 2 subgroups. RESULTS: Functional connectivity strength in salience network (SN), especially connectivity between the supragenual anterior cingulate cortex (SACC) and left anterior insula (LAI), was reduced in the unconscious state (UWS) compared to the conscious state (MCS and BL). Moreover, at the individual level, SACC-LAI connectivity was more present in MCS than in UWS. Default-mode network (DMN) connectivity strength, especially between the posterior cingulate cortex (PCC) and left lateral parietal cortex (LLPC), was reduced in UWS-R compared with UWS-E. Furthermore, PCC-LLPC connectivity was more present in UWS-E than in UWS-R. INTERPRETATION: Our findings show that SN (SACC-LAI) connectivity correlates with behavioral signs of consciousness, whereas DMN (PCC-LLPC) connectivity instead predicts recovery of consciousness.

Increase in glutamate/glutamine concentration in the medial prefrontal cortex during mental imagery: A combined functional mrs and fMRI study.

Recent functional magnetic resonance spectroscopy (fMRS) studies have shown changes in glutamate/glutamine (Glx) concentrations between resting-state and active-task conditions. However, the types of task used have been limited to sensory paradigms, and the regions from which Glx concentrations have been measured limited to sensory ones. This leaves open the question as to whether the same effect can be seen in higher-order brain regions during cognitive tasks. Cortical midline structures, especially the medial prefrontal cortex (MPFC), have been suggested to be involved in various such cognitive tasks. We, therefore set out to use fMRS to investigate the dynamics of Glx concentrations in the MPFC between resting-state and mental imagery task conditions. The auditory cortex was used as a control region. In addition, functional magnetic resonance imaging was used to explore task-related neural activity changes. The mental imagery task consisted of imagining swimming and was applied to a large sample of healthy participants (n = 46). The participants were all competitive swimmers, ensuring proficiency in mental-swimming. Glx concentrations in the MPFC increased during the imagery task, as compared to resting-state periods preceding and following the task. These increases mirror BOLD activity changes in the same region during the task. No changes in either Glx concentrations or BOLD activity were seen in the auditory cortex. These findings contribute to our understanding of the biochemical basis of generating or manipulating mental representations and the MPFC's role in this.

Group membership and social status modulate joint actions.

The ability to form shared task representations is considered a keystone of social cognition. It remains, however, contentious if, and to what extent, social categorization impacts on shared representations. In the present study, we address the possibility of the modulation of action co-representation by social categorization, such as group membership and social status, as indexed by the social Simon effect. Italian participants were requested to perform a social Simon task, along with either an Italian (high-status in-group) or an Albanian (low-status out-group) participant. Results show that Italian participants co-represented the action of their partner when paired with a high-status in-group participant. Conversely, this effect was absent when they performed the task with a low-status out-group participant. Furthermore, the Albanian participants co-represented the action of their partner when paired with an Italian participant. These results suggest that group membership modulates action co-representation through the varying of the groups' relative status. The impact of this issue is boundless given the increasing multicultural nature of our society. Indeed, if multiculturalism fails, modern society does likewise.

Dynamic mechanisms that couple the brain and breathing to the external environment.

Brain and breathing activities are closely related. However, the exact neurophysiological mechanisms that couple the brain and breathing to stimuli in the external environment are not yet agreed upon. Our data support that synchronization and dynamic attunement are two key mechanisms that couple local brain activity and breathing to external periodic stimuli. First, we review the existing literature, which provides strong evidence for the synchronization of brain and breathing in terms of coherence, cross-frequency coupling and phase-based entrainment. Second, using EEG and breathing data, we show that both the lungs and localized brain activity at the Cz channel attune the temporal structure of their power spectra to the periodic structure of external auditory inputs. We highlight the role of dynamic attunement in playing a key role in coordinating the tripartite temporal alignment of localized brain activity, breathing and input dynamics across longer timescales like minutes. Overall, this perspective sheds light on potential mechanisms of brain-breathing coupling and its alignment to stimuli in the external environment.