Large-scale Cortical Networks Research Articles

Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain

According to predictive-coding theory, cortical areas continuously generate and update predictions of sensory inputs at different hierarchical levels and emit prediction errors when the predicted and actual inputs differ. However, predictions and prediction errors are simultaneous and interdependent processes, making it difficult to disentangle their constituent neural network organization. Here, we test the theory by using high-density electrocorticography (ECoG) in monkeys during an auditory "local-global" paradigm in which the temporal regularities of the stimuli were controlled at two hierarchical levels. We decomposed the broadband data and identified lower- and higher-level prediction-error signals in early auditory cortex and anterior temporal cortex, respectively, and a prediction-update signal sent from prefrontal cortex back to temporal cortex. The prediction-error and prediction-update signals were transmitted via γ (>40Hz) and α/β (<30Hz) oscillations, respectively. Our findings provide strong support for hierarchical predictive coding and outline how it is dynamically implemented using distinct cortical areas and frequencies.

Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks

Frequency-specific oscillations and phase-coupling of neuronal populations are essential mechanisms for the coordination of activity between brain areas during cognitive tasks. Therefore, the ongoing activity ascribed to the different functional brain networks should also be able to reorganise and coordinate via similar mechanisms. We develop a novel method for identifying large-scale phase-coupled network dynamics and show that resting networks in magnetoencephalography are well characterised by visits to short-lived transient brain states, with spatially distinct patterns of oscillatory power and coherence in specific frequency bands. Brain states are identified for sensory, motor networks and higher-order cognitive networks. The cognitive networks include a posterior alpha (8–12 Hz) and an anterior delta/theta range (1–7 Hz) network, both exhibiting high power and coherence in areas that correspond to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks have characteristic signatures in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales.

T166. SPATIAL INCOHERENCE OF LARGE-SCALE CORTICAL NETWORKS RELATES TO FORMAL THOUGHT DISORDER IN SCHIZOPHRENIA: A 7T MRI-BASED THICKNESS STUDY

BackgroundThe thickness of cerebral cortex varies across individuals as well as across different regions within an individual. Shared trophic or plastic influences such as repeated task-related recruitment of extant brain regions results in morphological covariance within large-scale brain networks. Pathological processes disrupting functional co-activation can result in higher than expected degree of variability within large-scale networks in an individual level, resulting in spatial incoherence. We studied spatial incoherence of cortical thickness in 17 cortical networks identified on the basis of well-known patterns of intrinsic connectivity, to identify the spatially incoherent networks and relate them to differences in severity of thought disorder among patients with schizophrenia.MethodsUltra-high field 7T anatomical MRI scans (MPRAGE) were obtained from 20 subjects in a clinically stable, medicated early stage of schizophrenia, and 19 sex, parental socioeconomic-status and age matched healthy controls. Cortical thickness was estimated using Freesurfer v5.0, across 17 networks based on the parcellation scheme of Yeo et al. We computed within-network coefficient of variation in thickness (CVT) across vertices that constitute each network. Higher CVT of a network in a subject indicates higher spatial incoherence within the network for that individual. Independent 2-tailed t-tests were used to compare CVT of 17 networks between the 2 groups with FDR-corrected p=0.05 considered as statistically significant. We related CVT of affected networks to the scores of positive and negative Formal Thought Disorder measured using Thought and Language Index in patients.ResultsSalience Network (aka Ventral Attention Network as per Yeo atlas), Default Mode Network and Central Executive Network (aka dorsal Attention Network in Yeo atlas) showed most significant reduction in MRI-derived cortical thickness (networks #8, #12, #15 as well as #16 of Yeo atlas). Only the Salience and Executive Networks (network #8 and #12) showed higher coefficient of variation in patients compared to controls, indicating either a failure of coordinated maturation or co-ordinated function. Higher spatial incoherence of Salience Network related to reduced mean thickness of Central Executive Network in patients with schizophrenia; this relationship was not seen in healthy controls (Fisher’s z test, p=0.02). Both higher coefficient of variation in Salience Network and lower mean thickness in Central Executive Network predicted the severity of positive but not negative thought disorder scores.DiscussionOur results indicate that (1) large-scale cortical networks involved in information processing (Salience and Executive Networks) show spatial incoherence in schizophrenia (2) the degree of spatial incoherence relates to the severity of disorganisation of thoughts and language in patients. Spatial incoherence may be the result of a dysmaturational or functional dysplastic effect reflecting inefficient cortical recruitment in schizophrenia. Within-subject morphological variability carries useful information that can potentially explain the elusive neural basis of complex symptoms such as formal thought disorder.

Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance

Dopamine underlies important aspects of cognition, and has been suggested to boost cognitive performance. However, how dopamine modulates the large-scale cortical dynamics during cognitive performance has remained elusive. Using functional MRI during a working memory task in healthy young human listeners, we investigated the effect of levodopa (l-dopa) on two aspects of cortical dynamics, blood oxygen-level-dependent (BOLD) signal variability and the functional connectome of large-scale cortical networks. We here show that enhanced dopaminergic signaling modulates the two potentially interrelated aspects of large-scale cortical dynamics during cognitive performance, and the degree of these modulations is able to explain inter-individual differences in l-dopa-induced behavioral benefits. Relative to placebo, l-dopa increased BOLD signal variability in task-relevant temporal, inferior frontal, parietal and cingulate regions. On the connectome level, however, l-dopa diminished functional integration across temporal and cingulo-opercular regions. This hypo-integration was expressed as a reduction in network efficiency and modularity in more than two thirds of the participants and to different degrees. Hypo-integration co-occurred with relative hyper-connectivity in paracentral lobule and precuneus, as well as posterior putamen. Both, l-dopa-induced BOLD signal variability modulation and functional connectome modulations proved predictive of an individual's l-dopa-induced benefits in behavioral performance, namely response speed and perceptual sensitivity. Lastly, l-dopa-induced modulations of BOLD signal variability were correlated with l-dopa-induced modulation of nodal connectivity and network efficiency. Our findings underline the role of dopamine in maintaining the dynamic range of, and communication between, cortical systems, and their explanatory power for inter-individual differences in benefits from dopamine during cognitive performance.

Hyperedge bundling: A practical solution to spurious interactions in MEG/EEG source connectivity analyses

Inter-areal functional connectivity (FC), neuronal synchronization in particular, is thought to constitute a key systems-level mechanism for coordination of neuronal processing and communication between brain regions. Evidence to support this hypothesis has been gained largely using invasive electrophysiological approaches. In humans, neuronal activity can be non-invasively recorded only with magneto- and electroencephalography (MEG/EEG), which have been used to assess FC networks with high temporal resolution and whole-scalp coverage. However, even in source-reconstructed MEG/EEG data, signal mixing, or “source leakage”, is a significant confounder for FC analyses and network localization.Signal mixing leads to two distinct kinds of false-positive observations: artificial interactions (AI) caused directly by mixing and spurious interactions (SI) arising indirectly from the spread of signals from true interacting sources to nearby false loci. To date, several interaction metrics have been developed to solve the AI problem, but the SI problem has remained largely intractable in MEG/EEG all-to-all source connectivity studies. Here, we advance a novel approach for correcting SIs in FC analyses using source-reconstructed MEG/EEG data.Our approach is to bundle observed FC connections into hyperedges by their adjacency in signal mixing. Using realistic simulations, we show here that bundling yields hyperedges with good separability of true positives and little loss in the true positive rate. Hyperedge bundling thus significantly decreases graph noise by minimizing the false-positive to true-positive ratio. Finally, we demonstrate the advantage of edge bundling in the visualization of large-scale cortical networks with real MEG data. We propose that hypergraphs yielded by bundling represent well the set of true cortical interactions that are detectable and dissociable in MEG/EEG connectivity analysis.

The Construction of Large-Scale Cortical Networks for P300 From Scalp EEG

Multiple studies, which studied cortical generators and cerebral networks of P300 based on electroencephalogram (EEG) and functional magnetic resonance imaging, have investigated the possible mechanism underlying the inter-subject variability of P300 component. However, few studies have investigated the large-scale networks of P300 as well as the potential relationships between large-scale networks and P300, particularly in EEG signal. When using group independent component analysis (gICA) to investigate the large-scale EEG networks, some resting-state networks, such as the default mode network, have been observed, while the related task-state networks (TSNs) of P300 are still unknown. In this paper, we estimated the time courses of cortical voxels following EEG source localization and then adopted gICA at the group level to identify the related P300-TSNs. Thereafter, we constructed and further investigated the potential relationships between the properties of functional network connectivity (FNC) and P300 amplitude. Our findings revealed six best-fit common functional networks that participated in the generation of P300, namely, the primary visual network, visual network, central executive network, left parietal network, attention network, and sensorimotor network. Specifically, the FNC properties significantly related to the P300 amplitude, as well as some FNC connections. In addition, when using FNC properties to classify the high- and low-amplitude groups, a relatively high-classification accuracy of 87.26% for linear discriminant analysis and of 86.31% for support vector machine were achieved. These findings confirmed the validation of gICA in mining large-scale networks in cortical EEG and may help us better understand the inter-subject P300 variability.

Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain

The predictive-coding theory proposes that cortical areas continuously generate and update predictions of sensory inputs, and emit predictionerror signals when the predicted and actual sensory inputs differ. According to the theory, the computations of predictions and prediction errors are carried out by hierarchically organized neuronal populations and transmitted between hierarchies via specific frequency channels. However, these multilevel processes are simultaneous and interdependent, making it difficult to disentangle their constituent neural network organization. Here, we test the theory by using hemisphere-wide, high-density electrocorticography (ECoG) to provide a large-scale characterization of the cortical networks for hierarchical auditory prediction and prediction-error processing in macaque monkeys. Broadband neuronal signals were collected during an auditory “local-global” paradigm in which the temporal regularities of the stimuli and their violations were controlled at two hierarchical levels. Using an automatized decomposition method for cortical activations and corticocortical interactions, we identified three distinct structures in the violation responses and further evaluated their functional interactions within and across trials. Structure 1, representing bottom-up processing of lower-level prediction errors, was reflected in γ oscillations (>40Hz) in the auditory cortex. Structure 2, representing the subsequent bottom-up processing of higher-level prediction errors, was reflected in γ oscillations in the anterior temporal cortex. Lastly, structure 3, representing a top-down updating of those predictions, was reflected in α/β-band interactions (<30Hz) from the prefrontal cortex back to the temporal cortex. Our findings provide strong support for hierarchical predictive coding and outline how it is dynamically implemented in signals using distinct areas and frequency bands.

Revisiting the Functional and Structural Connectivity of Large-Scale Cortical Networks.

Multimodal neuroimaging research has become increasingly popular, and structure-function correspondence is tacitly assumed. Researchers have not yet adequately assessed whether the functional connectivity (FC) and structural connectivity (SC) of large-scale cortical networks are in agreement. Structural magnetic resonance imaging (sMRI), resting-state functional MRI (rfMRI), and diffusion-weighted imaging (DWI) data sets from 36 healthy subjects (age 27.4) were selected from a Rockland sample (Enhanced Nathan Kline Institute). The cerebral cortex was parcellated into 62 regions according to the Desikan-Killiany atlas for FC and SC analyses. Thresholded correlations in rfMRI and tractography derived from DWI were used to construct FC and SC maps, respectively. A community detection algorithm was applied to reveal the underlying organization, and modular consistency was quantified to bridge cross-modal comparisons. The distributions of correlation coefficients in FC and SC maps were significantly different. Approximately one-fourth of the connections in the SC map were located at a correlation level below 0.2 (df 253). The index of modular consistency in the within-modality interindividual condition (either FC or SC) was considerably greater than that in the between-modality intraindividual analog. In addition, the SC-FC differential map (SC connections with lower correlations) revealed reliable modular structures. Based on these results, the hypothesized FC-SC agreement is partially valid. Contingent on extant neuroimaging tools and analytical conventions, the neural informatics of FC and SC should be regarded as complementary rather than concordant. Furthermore, the results verify the physiological significance of moderately (or mildly) correlated brain signals in rfMRI, which are often discarded by stringent thresholding.

Patterns of altered neural synchrony in the default mode network in autism spectrum disorder revealed with magnetoencephalography (MEG): Relationship to clinical symptomatology.

Communication between different areas of the brain was observed in children with ASD and neurotypical children while awake, but not working on a task. Magnetoencephalography was used to measure tiny magnetic fields naturally generated via brain activity. The brains of children with ASD showed less communication between areas that are important for social information processing compared to the brains of neurotypical children. The amount of communication between these areas was associated with social and social communication difficulties.

New class of reduced computationally efficient neuronal models for large-scale simulations of brain dynamics

During slow-wave sleep, brain electrical activity is dominated by the slow (< 1Hz) electroencephalogram (EEG) oscillations characterized by the periodic transitions between active (or Up) and silent (or Down) states in the membrane voltage of the cortical and thalamic neurons. Sleep slow oscillation is believed to play critical role in consolidation of recent memories. Past computational studies, based on the Hodgkin-Huxley type neuronal models, revealed possible intracellular and network mechanisms of the neuronal activity during sleep, however, they failed to explore the large-scale cortical network dynamics depending on collective behavior in the large populations of neurons. In this new study, we developed a novel class of reduced discrete time spiking neuron models for large-scale network simulations of wake and sleep dynamics. In addition to the spiking mechanism, the new model implemented nonlinearities capturing effects of the leak current, the Ca2+ dependent K+ current and the persistent Na+ current that were found to be critical for transitions between Up and Down states of the slow oscillation. We applied the new model to study large-scale two-dimensional cortical network activity during slow-wave sleep. Our study explained traveling wave dynamics and characteristic synchronization properties of transitions between Up and Down states of the slow oscillation as observed in vivo in recordings from cats. We further predict a critical role of synaptic noise and slow adaptive currents for spike sequence replay as found during sleep related memory consolidation.

Large-scale cortical volume correlation networks reveal disrupted small world patterns in Parkinson's disease.

To date, the most frequently reported neuroimaging biomarkers in Parkinson's disease (PD) are direct brain imaging measurements focusing on local disrupted regions. However, the notion that PD is related to abnormal functional and structural connectivity has received support in the past few years. Here, we employed graph theory to analyze the structural co-variance networks derived from 50 PD patients and 48 normal controls (NC). Then, the small world properties of brain networks were assessed in the structural networks that were constructed based on cortical volume data. Our results showed that both the PD and NC groups had a small world architecture in brain structural networks. However, the PD patients had a higher characteristic path length and clustering coefficients compared with the NC group. With regard to the nodal centrality, 11 regions, including 3 association cortices, 5 paralimbic cortices, and 3 subcortical regions were identified as hubs in the PD group. In contrast, 10 regions, including 7 association cortical regions, 2 paralimbic cortical regions, and the primary motor cortex region, were identified as hubs. Moreover, the regional centrality was profoundly affected in PD patients, including decreased nodal centrality in the right inferior occipital gyrus and the middle temporal gyrus and increased nodal centrality in the right amygdala, the left caudate and the superior temporal gyrus. In addition, the structural cortical network of PD showed reduced topological stability for targeted attacks. Together, this study shows that the coordinated patterns of cortical volume network are widely altered in PD patients with a decrease in the efficiency of parallel information processing. These changes provide structural evidence to support the concept that the core pathophysiology of PD is associated with disruptive alterations in the coordination of large-scale brain networks that underlie high-level cognition.

Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism

Although evidence is accumulating that autism spectrum disorder (ASD) is associated with disruption of functional connections between and within brain networks, it remains largely unknown whether these abnormalities are related to specific frequency bands. To address this question, network contingency analysis was performed on brain functional connectomes obtained from 213 adolescent participants across nine sites in the Autism Brain Imaging Data Exchange (ABIDE) multisite sample, to determine the disrupted connections between and within seven major cortical networks in adolescents with ASD at Slow-5, Slow-4 and Slow-3 frequency bands and further assess whether the aberrant intra- and inter-network connectivity varied as a function of ASD symptoms. Overall under-connectivity within and between large-scale intrinsic networks in ASD was revealed across the three frequency bands. Specifically, decreased connectivity strength within the default mode network (DMN), between DMN and visual network (VN), ventral attention network (VAN), and between dorsal attention network (DAN) and VAN was observed in the lower frequency band (slow-5, slow-4), while decreased connectivity between limbic network (LN) and frontal-parietal network (FPN) was observed in the higher frequency band (slow-3). Furthermore, weaker connectivity within and between specific networks correlated with poorer communication and social interaction skills in the slow-5 band, uniquely. These results demonstrate intrinsic under-connectivity within and between multiple brain networks within predefined frequency bands in ASD, suggesting that frequency-related properties underlie abnormal brain network organization in the disorder.

Mapping original thinking by differential modulation of large-scale cortical networks: functional preservation of creativity in LGG patients

Mapping original thinking by differential modulation of large-scale cortical networks: functional preservation of creativity in LGG patients

P296 Spike timing dependent plasticity in the human parietal-prefrontal network

Question Learning may occur through associative changes in the synaptic strength of neural connections that can be induced by coupling of activity of interconnected neurons with precise timing (spike-timing dependent plasticity, STDP). It is debated if this model can be applied in the context of large-scale cortical networks in humans. Methods We combined transcranial magnetic stimulation with concurrent electroencephalography to directly investigate if precise repeated activation of pre and post-synaptic large-scale cortical inputs produces STDP effects within the parietal-prefrontal network of 15 healthy volunteers. Results We found direct evidence for bidirectional STDP after effects over the DLPFC ruled by the PPC pre-synaptic inputs. Specifically, when the pre-synaptic input (PPC) precedes repeatedly the post-synaptic activation (DLPFC) by 10 ms a LTD-like effect of TMS-evoked activity is induced within the DLPFC. When the temporal order of the two stimulated areas is reverted, i.e. the pre-synaptic input (PPC) follows the post-synpatic input (DLPFC), we observed an LTP-like effect. Interestingly, such changes were accompained by PAS-depedendent modulations of the natural frequency of oscillation of the DLPFC, i.e. gamma activity. Conclusions We provide novel evidence that paired associative stimulation of posterior parietal cortex (PPC) and dorsolateral prefrontal cortex (DLPFC) induces bidirectional STDP effects in the post synaptic area (DLPFC) both in the time and in the time/frequency domain. Download : Download high-res image (811KB) Download : Download full-size image Download : Download high-res image (407KB) Download : Download full-size image

How Do We Keep Information ‘Online’?

New magnetoencephalography (MEG) results indicate that a putative marker of conscious processes - namely, the global broadcasting of information across large-scale cortical networks - can also operate during the maintenance of non-conscious input. I discuss the implications for the theoretical linkage between conscious awareness and working memory functions.

Large Scale Cortical Functional Networks Associated with Slow-Wave and Spindle-Burst-Related Spontaneous Activity.

Cortical sensory systems are active with rich patterns of activity during sleep and under light anesthesia. Remarkably, this activity shares many characteristics with those present when the awake brain responds to sensory stimuli. We review two specific forms of such activity: slow-wave activity (SWA) in the adult brain and spindle bursts in developing brain. SWA is composed of 0.5–4 Hz resting potential fluctuations. Although these fluctuations synchronize wide regions of cortex, recent large-scale imaging has shown spatial details of their distribution that reflect underlying cortical structural projections and networks. These networks are regulated, as prior awake experiences alter both the spatial and temporal features of SWA in subsequent sleep. Activity patterns of the immature brain, however, are very different from those of the adult. SWA is absent, and the dominant pattern is spindle bursts, intermittent high frequency oscillations superimposed on slower depolarizations within sensory cortices. These bursts are driven by intrinsic brain activity, which act to generate peripheral inputs, for example via limb twitches. They are present within developing sensory cortex before they are mature enough to exhibit directed movements and respond to external stimuli. Like in the adult, these patterns resemble those evoked by sensory stimulation when awake. It is suggested that spindle-burst activity is generated purposefully by the developing nervous system as a proxy for true external stimuli. While the sleep-related functions of both slow-wave and spindle-burst activity may not be entirely clear, they reflect robust regulated phenomena which can engage select wide-spread cortical circuits. These circuits are similar to those activated during sensory processing and volitional events. We highlight these two patterns of brain activity because both are prominent and well-studied forms of spontaneous activity that will yield valuable insights into brain function in the coming years.

Large Scale Functional Brain Networks Underlying Temporal Integration of Audio-Visual Speech Perception: An EEG Study.

Observable lip movements of the speaker influence perception of auditory speech. A classical example of this influence is reported by listeners who perceive an illusory (cross-modal) speech sound (McGurk-effect) when presented with incongruent audio-visual (AV) speech stimuli. Recent neuroimaging studies of AV speech perception accentuate the role of frontal, parietal, and the integrative brain sites in the vicinity of the superior temporal sulcus (STS) for multisensory speech perception. However, if and how does the network across the whole brain participates during multisensory perception processing remains an open question. We posit that a large-scale functional connectivity among the neural population situated in distributed brain sites may provide valuable insights involved in processing and fusing of AV speech. Varying the psychophysical parameters in tandem with electroencephalogram (EEG) recordings, we exploited the trial-by-trial perceptual variability of incongruent audio-visual (AV) speech stimuli to identify the characteristics of the large-scale cortical network that facilitates multisensory perception during synchronous and asynchronous AV speech. We evaluated the spectral landscape of EEG signals during multisensory speech perception at varying AV lags. Functional connectivity dynamics for all sensor pairs was computed using the time-frequency global coherence, the vector sum of pairwise coherence changes over time. During synchronous AV speech, we observed enhanced global gamma-band coherence and decreased alpha and beta-band coherence underlying cross-modal (illusory) perception compared to unisensory perception around a temporal window of 300–600 ms following onset of stimuli. During asynchronous speech stimuli, a global broadband coherence was observed during cross-modal perception at earlier times along with pre-stimulus decreases of lower frequency power, e.g., alpha rhythms for positive AV lags and theta rhythms for negative AV lags. Thus, our study indicates that the temporal integration underlying multisensory speech perception requires to be understood in the framework of large-scale functional brain network mechanisms in addition to the established cortical loci of multisensory speech perception.

Executive control network connectivity strength protects against relapse to cocaine use.

Cocaine addiction is characterized by notoriously high relapse rates following treatment. Recent efforts to address poor treatment outcomes have turned to potential neural markers of relapse risk. Accordingly, the present study examined resting state functional connectivity (rsFC) within and between three large-scale cortical networks: the default mode network (DMN), salience network (SN) and executive control network (ECN). All three have been implicated in relapse-related phenomena including craving, withdrawal and executive control deficits. Forty-five cocaine-dependent individuals and 22 healthy controls completed 6-min resting fMRI scans, The Wisconsin Card Sorting Task, Continuous Performance Test and Cocaine Craving Questionnaire. Cocaine-dependent individuals completed all measures in the final week of a residential treatment episode. Ten control and 9 abstinent cocaine-dependent individuals returned for 3-6 month follow-up scan visits. A group-level independent component analysis was employed to generate ECN, DMN and SN components. For individuals abstinent up to day 30 post-treatment (n = 21), we found enhanced pre-discharge rsFC between the left ECN and both the right ECN and SN as well as between the right ECN and left ECN. Left ECN rsFC effects remained elevated 3-6 months later among abstinent cocaine-dependent individuals. Relapse was related to fewer years of education and more years smoking but no other demographic, clinical, treatment and neurocognitive characteristics. Findings suggest that interhemispheric ECN and ECN-SN connectivity strength may protect against relapse to cocaine use following treatment. These patterns of enhanced interhemispheric network connectivity may reflect a greater capacity to engage executive control processes when faced with opportunities to use cocaine post-treatment.

Spike-timing-dependent plasticity in the human dorso-lateral prefrontal cortex

Changes in the synaptic strength of neural connections are induced by repeated coupling of activity of interconnected neurons with precise timing, a phenomenon known as spike-timing-dependent plasticity (STDP). It is debated if this mechanism exists in large-scale cortical networks in humans. We combined transcranial magnetic stimulation (TMS) with concurrent electroencephalography (EEG) to directly investigate the effects of two paired associative stimulation (PAS) protocols (fronto-parietal and parieto-frontal) of pre and post-synaptic inputs within the human fronto-parietal network. We found evidence that the dorsolateral prefrontal cortex (DLPFC) has the potential to form robust STDP. Long-term potentiation/depression of TMS-evoked cortical activity is prompted after that DLPFC stimulation is followed/preceded by posterior parietal stimulation. Such bidirectional changes are paralleled by sustained increase/decrease of high-frequency oscillatory activity, likely reflecting STDP responsivity. The current findings could be important to drive plasticity of damaged cortical circuits in patients with cognitive or psychiatric disorders.

In vivo imaging reveals impaired connectivity across cortical and subcortical networks in a mouse model of DYT1 dystonia

Developing in vivo functional and structural neuroimaging assays in Dyt1 ΔGAG heterozygous knock-in (Dyt1 KI) mice provide insight into the pathophysiology underlying DYT1 dystonia. In the current study, we examined in vivo functional connectivity of large-scale cortical and subcortical networks in Dyt1 KI mice and wild-type (WT) controls using resting-state functional magnetic resonance imaging (MRI) and an independent component analysis. In addition, using diffusion MRI we examined how structural integrity across the basal ganglia and cerebellum directly relates to impairments in functional connectivity. Compared to WT mice, Dyt1 KI mice revealed increased functional connectivity across the striatum, thalamus, and somatosensory cortex; and reduced functional connectivity in the motor and cerebellar cortices. Further, Dyt1 KI mice demonstrated elevated free-water (FW) in the striatum and cerebellum compared to WT mice, and increased FW was correlated with impairments in functional connectivity across basal ganglia, cerebellum, and sensorimotor cortex. The current study provides the first in vivo MRI-based evidence in support of the hypothesis that the deletion of a 3-base pair (ΔGAG) sequence in the Dyt1 gene encoding torsinA has network level effects on in vivo functional connectivity and microstructural integrity across the sensorimotor cortex, basal ganglia, and cerebellum.