Cortico-cortical Connections Research Articles

Cortical drive may facilitate enhanced use of the paretic leg induced by random constraint force to the non-paretic leg during walking in chronic stroke.

The goal of this study was to determine the effects of applying random vs. constant constraint force to the non-paretic leg during walking on enhanced use of the paretic leg in individuals post-stroke, and examine the underlying brain mechanisms. Twelve individuals with chronic stroke were tested under two conditions while walking on a treadmill: random vs. constant magnitude of constraint force applied to the non-paretic leg during swing phase of gait using a custom designed robotic system. Leg kinematics, muscle activity of the paretic leg, and electroencephalography (EEG) were recorded during treadmill walking. Paretic step length and muscle activity of the paretic ankle plantarflexors significantly increased after walking with random and constant constraint forces. Cortico-cortical connectivity between motor cortices and cortico-muscular connectivity from the lesioned motor cortex to the paretic ankle plantarflexors significantly increased for the random force condition but not for the constant force condition. In addition, individuals post-stroke with greater baseline gait variability showed greater improvements in the paretic step length after walking with random force condition but not with the constant force condition. In conclusion, application of random constraint force to the non-paretic leg may enhance the use of the paretic leg during walking by facilitating cortical drive from the lesioned motor cortex to the paretic ankle plantarflexors. Results from this study may be used for the development of constraint induced locomotor intervention approaches aimed at improving locomotor function in individuals after stroke.

Atrophy of cerebellar lobule VI and primary motor cortex in cervical dystonia - a region of interest-based study.

Recently, a network model of cervical dystonia (CD) has been adopted that implicates nodes and pathways involving cerebellar, basal-ganglia and cortico-cortical connections. Although functional changes in the cerebello-thalamo-cortical network in dystonia have been reported in several studies, structural information of this network remain sparse. To characterize the structural properties of the cerebellar motor network in isolated CD patients. This includes cerebellar lobules involved in motor processing, the dentate nucleus (DN), the thalamus, and the primary motor cortex (M1). Magnetic resonance imaging data of 18 CD patients and 18 healthy control subjects were acquired. In CD patients, the motor part of the Toronto Western Spasmodic Torticollis Rating Scale was assessed to evaluate motor symptom severity. The volume of cerebellar lobules I-VI and VIII, the DN and thalamus, and the cortical thickness (CT) of M1 were determined for a region of interest (ROI)-based quantitative analysis. Volumes/CT of these ROIs were compared between groups and associated with motor symptom severity in patients. The volume of lobule VI and the CT of M1 were reduced in CD patients. The volumes of the other ROIs were not different between groups. No association was identified between the structural properties of lobule VI or M1 and the severity of CD motor symptoms. Atrophy within the cerebellum and M1 contributes to CD's complex motor network pathology. Further investigations are needed to ascertain the mechanisms underlying the local volume loss.

Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.

People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual ("visual2"), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients-both affective and non-affective-exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged and normalized the visual2 and somatomotor network connections, and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p = 2e-10, Hedges' g = 1.05). This "somato-visual" biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, anxiety, or demographics. It had moderate test-retest reliability (ICC = 0.62) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC = 0.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.

Corticocortical and corticomuscular connectivity dynamics in standing posture: electroencephalography study.

Cortical mechanism is necessary for human standing control. Previous research has demonstrated that cortical oscillations and corticospinal excitability respond flexibly to postural demands. However, it is unclear how corticocortical and corticomuscular connectivity changes dynamically during standing with spontaneous postural sway and over time. This study investigated the dynamics of sway- and time-varying connectivity using electroencephalography and electromyography. Electroencephalography and electromyography were recorded in sitting position and 3 standing postures with varying base-of-support: normal standing, one-leg standing, and standing on a piece of wood. For sway-varying connectivity, corticomuscular connectivity was calculated based on the timing of peak velocity in anteroposterior sway. For time-varying connectivity, corticocortical connectivity was measured using the sliding-window approach. This study found that corticomuscular connectivity was strengthened at the peak velocity of postural sway in the γ- and β-frequency bands. For time-varying corticocortical connectivity, the θ-connectivity in all time-epoch was classified into 7 clusters including posture-relevant component. In one of the 7 clusters, strong connectivity pairs were concentrated in the mid-central region, and the proportion of epochs under narrow-base standing conditions was significantly higher, indicating a functional role for posture balance. These findings shed light on the connectivity dynamics and cortical oscillation that govern standing balance.

Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI.

The superficial white matter (SWM) consists of numerous short-range association fibers connecting adjacent and nearby gyri and plays an important role in brain function, development, aging, and various neurological disorders. Diffusion MRI (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the SWM. However, detailed imaging of the small, highly-curved fibers of the SWM is a challenge for current clinical and research dMRI acquisitions. This work investigates the efficacy of mapping the SWM using in vivo ultra-high-resolution dMRI data. We compare the SWM mapping performance from two dMRI acquisitions: a high-resolution 0.76-mm isotropic acquisition using the generalized slice-dithered enhanced resolution (gSlider) protocol and a lower resolution 1.25-mm isotropic acquisition obtained from the Human Connectome Project Young Adult (HCP-YA) database. Our results demonstrate significant differences in the cortico-cortical anatomical connectivity that is depicted by these two acquisitions. We perform a detailed assessment of the anatomical plausibility of these results with respect to the nonhuman primate (macaque) tract-tracing literature. We find that the high-resolution gSlider dataset is more successful at depicting a large number of true positive anatomical connections in the SWM. An additional cortical coverage analysis demonstrates significantly higher cortical coverage in the gSlider dataset for SWM streamlines under 40 mm in length. Overall, we conclude that the spatial resolution of the dMRI data is one important factor that can significantly affect the mapping of SWM. Considering the relatively long acquisition time, the application of dMRI tractography for SWM mapping in future work should consider the balance of data acquisition efforts and the efficacy of SWM depiction.

REM sleep remains paradoxical: sub-states determined by thalamo-cortical and cortico-cortical functional connectivity.

During paradoxical sleep (PS, aka REM sleep) the cerebral cortex displays rapid electroencephalographic activity similar to that of wakefulness, whereas in the posterior associative thalamus, rapid activity is interrupted by frequent periods of slow-wave (delta) oscillations at 2-3Hz, thereby dissociating the intrinsic frequency in thalamus and cortex. Here we studied the functional consequences of such a dissociation using intrathalamic and intracortical recordings in 21 epileptic patients, applying coherence analysis to examine changes in functional connectivity between the posterior thalamus (mainly medial pulvinar) and six cortical functional networks, and also between each cortical network with respect to the others. Periods of slow-wave thalamic activity ('delta PS') were more prevalent than phases of 'rapid PS,' and the delta/rapid thalamic alternance did not overlap with the classical tonic/phasic dichotomy based on rapid eye movements. Thalamo-cortical and cortico-cortical functional connectivity significantly decreased during delta PS, relative to both rapid PS periods and to wakefulness. The fact that delta thalamic activity and low thalamo-cortical binding coincided with a suppression of cortico-cortical connectivity supports a crucial role for the posterior associative thalamus, and particularly the medial pulvinar, in ensuring trans-thalamic communication between distant cortical areas. Disruption of such a trans-thalamic communication during delta PS compromises the functional binding between cortical areas, and consequently might contribute to the alteration of perceptual experiences commonly reported during dreams. KEY POINTS: During paradoxical, or REM, sleep (PS), rapid thalamic activity is interrupted by frequent periods of slow delta waves at 2-3Hz. During these periods of thalamic delta activity there was a drastic drop of functional connectivity between associative thalamus and cortex, and also among different cortical networks. The delta/rapid alternance did not overlap with the classically defined 'tonic/phasic' periods and therefore suggests a distinct dichotomy of functional states in PS. Recurrent decrease in thalamo-cortical and cortico-cortical functional connectivity during PS may compromise the spatio-temporal binding between cortical areas, which in turn could hinder the formation of coherent mental content during dreams.

Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.

People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual ("visual2"), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients- both affective and non-affective-exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged and normalized the visual2 and somatomotor network connections, and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p=2e-10, Hedges' g=1.05). This "somato-visual" biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, anxiety, or demographics. It had moderate test-retest reliability (ICC=.61) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC=.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.

High-resolution diffusion MRI of the cortico-cortical connections between lower visual areas reveals divergence of connections and enhanced connectivity of the central visual field representation

High-resolution diffusion MRI of the cortico-cortical connections between lower visual areas reveals divergence of connections and enhanced connectivity of the central visual field representation

Structural connectivity matures along a sensorimotor-association connectional axis in youth.

Childhood and adolescence are associated with protracted developmental remodeling of cortico-cortical structural connectivity. However, how heterochronous development in white matter structural connectivity spatially and temporally unfolds across the macroscale human connectome remains unknown. Leveraging non-invasive diffusion MRI data from both cross-sectional (N = 590) and longitudinal (baseline: N = 3,949; two-year follow-up: N = 3,155) developmental datasets, we found that structural connectivity development diverges along a pre-defined sensorimotor-association (S-A) connectional axis from ages 8.1 to 21.9 years. Specifically, we observed a continuum of developmental profiles that spans from an early childhood increase in connectivity strength in sensorimotor-sensorimotor connections to a late adolescent increase in association-association connectional strength. The S-A connectional axis also captured spatial variations in associations between structural connectivity and both higher-order cognition and general psychopathology. Together, our findings reveal a hierarchical axis in the development of structural connectivity across the human connectome.

Premotor cortical beta synchronization and the network neuromodulation of externally paced finger tapping in Parkinson's disease

Parkinson's disease (PD) is characterized by the disruption of repetitive, concurrent and sequential motor actions due to compromised timing-functions principally located in cortex-basal ganglia (BG) circuits. Increasing evidence suggests that motor impairments in untreated PD patients are linked to an excessive synchronization of cortex-BG activity at beta frequencies (13–30 Hz). Levodopa and subthalamic nucleus deep brain stimulation (STN-DBS) suppress pathological beta-band reverberation and improve the motor symptoms in PD. Yet a dynamic tuning of beta oscillations in BG-cortical loops is fundamental for movement-timing and synchronization, and the impact of PD therapies on sensorimotor functions relying on neural transmission in the beta frequency-range remains controversial. Here, we set out to determine the differential effects of network neuromodulation through dopaminergic medication (ON and OFF levodopa) and STN-DBS (ON-DBS, OFF-DBS) on tapping synchronization and accompanying cortical activities. To this end, we conducted a rhythmic finger-tapping study with high-density EEG-recordings in 12 PD patients before and after surgery for STN-DBS and in 12 healthy controls. STN-DBS significantly ameliorated tapping parameters as frequency, amplitude and synchrony to the given auditory rhythms. Aberrant neurophysiologic signatures of sensorimotor feedback in the beta-range were found in PD patients: their neural modulation was weaker, temporally sluggish and less distributed over the right cortex in comparison to controls. Levodopa and STN-DBS boosted the dynamics of beta-band modulation over the right hemisphere, hinting to an improved timing of movements relying on tactile feedback. The strength of the post-event beta rebound over the supplementary motor area correlated significantly with the tapping asynchrony in patients, thus indexing the sensorimotor match between the external auditory pacing signals and the performed taps. PD patients showed an excessive interhemispheric coherence in the beta-frequency range during the finger-tapping task, while under DBS-ON the cortico-cortical connectivity in the beta-band was normalized. Ultimately, therapeutic DBS significantly ameliorated the auditory-motor coupling of PD patients, enhancing the electrophysiological processing of sensorimotor feedback-information related to beta-band activity, and thus allowing a more precise cued-tapping performance.

A simulation study: comparing independent component analysis and signal-space projection - source-informed reconstruction for rejecting muscle artifacts evoked by transcranial magnetic stimulation.

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore cortico-cortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection-source-informed reconstruction approach (SSP-SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP-SIR. We simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS-EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP-SIR, and subsequent comparisons were made with the ground truth data. ICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP-SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP-SIR being better when difference between topographies was larger. Overall, SSP-SIR performed better than ICA across the tested conditions. Based on these simulations, SSP-SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials. Selecting between ICA and SSP-SIR should be guided by the characteristics of the artifacts. SSP-SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP-SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease.

Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI). We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population. The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier. Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

Hyperphosphorylated tau in Alzheimer's disease disseminates along pathways predicted by the Structural Model for Cortico-cortical Connections.

In Alzheimer´s disease (AD), hyperphosphorylated tau spreads along the cerebral cortex in a stereotypical pattern that parallels cognitive deterioration. Tau seems to spread transsynaptically along cortico-cotical pathways that, according to synaptic tract-tracing studies in nonhuman primates, have specific laminar patterns related to the cortical type of the connected areas. This relation is described in the Structural Model. In the present article, we study the laminar distribution of hyperphosphorylated tau, labeled with the antibody AT8, along temporal cortical types in postmortem human brains with different AD stages to test the predictions of the Structural Model. Brains from donors without dementia had scant AT8-immunorreactive (AT8-ir) neurons in allo-, meso-, and isocortical types. In early AD stages, the mesocortical dysgranular type, including part of the transentorhinal cortex, had the highest AT8 immunostaining and AT8-ir neurons density. In advanced AD stages, AT8 immunostaining increased along the isocortical types until reaching the auditory koniocortex. Regarding laminar patterns, in early AD stages there were more AT8-ir neurons in supragranular layers in each de novo involved neocortical type; in advanced AD stages, AT8-ir neurons were equally distributed in supra- and infragranular layers. These AT8-ir laminar patterns are compatible with the predictions of the Structural Model. In summary, we show that hyperphosphorylated tau initially accumulates in allo-, meso-, and isocortical types, offer a proof of concept for the validity of the Structural Model to predict synaptic pathway organization in the human cerebral cortex, and highlight the relevance of nonhuman primate tract-tracing studies to understand human neuropathology.

Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.

Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinations are equivocal. Such individuals can benefit from novel accurate assessments that complement clinical diagnostics. We introduce a Bayesian machine learning classifier to identify concussion through cortico-cortical connectome mapping from magnetic resonance imaging in persons with quasi-normal cognition and without neuroradiological findings. Classifier features are generated from connectivity matrices specifying the mean fractional anisotropy of white matter connections linking brain structures. Each connection's saliency to classification was quantified by training individual classifier instantiations using a single feature type. The classifier was tested on a discovery sample of 92 healthy controls (HCs; 26 females, age μ ± σ: 39.8 ± 15.5 years) and 471 adult mTBI patients (158 females, age μ ± σ: 38.4 ± 5.9 years). Results were replicated in an independent validation sample of 256 HCs (149 females, age μ ± σ: 55.3 ± 12.1 years) and 126 patients with concussion (46 females, age μ ± σ: 39.0 ± 17.7 years). Classifier accuracy exceeds 99% in both samples, suggesting robust generalizability to new samples. Notably, 13 bilateral cortico-cortical connection pairs predict diagnostic status with accuracy exceeding 99% in both discovery and validation samples. Many such connection pairs are between prefrontal cortex structures, fronto-limbic and fronto-subcortical structures, and occipito-temporal structures in the ventral ("what") visual stream. This and related connectivity form a highly salient network of brain connections that is particularly vulnerable to concussion. Because these connections are important in mediating cognitive control, memory, and attention, our findings explain the high frequency of cognitive disturbances after concussion. Our classifier was trained and validated on concussed participants with cognitive profiles very similar to those of HCs. This suggests that the classifier can complement current diagnostics by providing independent information in clinical contexts where patients have quasi-normal cognition but where concussion diagnosis stands to benefit from additional evidence.

Cortico-cortical connectivity is influenced by levodopa in tremor-dominant Parkinson's disease

Resting tremor is the most common presenting motor symptom in Parkinson's disease (PD). The supplementary motor area (SMA) is a main target of the basal-ganglia-thalamo-cortical circuit and has direct, facilitatory connections with the primary motor cortex (M1), which is important for the execution of voluntary movement. Dopamine potentially modulates SMA and M1 activity, and both regions have been implicated in resting tremor. This study investigated SMA-M1 connectivity in individuals with PD ON and OFF dopamine medication, and whether SMA-M1 connectivity is implicated in resting tremor. Dual-site transcranial magnetic stimulation was used to measure SMA-M1 connectivity in PD participants ON and OFF levodopa. Resting tremor was measured using electromyography and accelerometry. Stimulating SMA inhibited M1 excitability OFF levodopa, and facilitated M1 excitability ON levodopa. ON medication, SMA-M1 facilitation was significantly associated with smaller tremor than SMA-M1 inhibition. The current findings contribute to our understanding of the neural networks involved in PD which are altered by levodopa medication and provide a neurophysiological basis for the development of interventions to treat resting tremor.

Dopamine improves defective cortical and muscular connectivity during bilateral control of gait in Parkinson’s disease

Parkinson’s Disease (PD)-typical declines in gait coordination are possibly explained by weakness in bilateral cortical and muscular connectivity. Here, we seek to determine whether this weakness and consequent decline in gait coordination is affected by dopamine levels. To this end, we compare cortico-cortical, cortico-muscular, and intermuscular connectivity and gait outcomes between body sides in people with PD under ON and OFF medication states, and in older adults. In our study, participants walked back and forth along a 12 m corridor. Gait events (heel strikes and toe-offs) and electrical cortical and muscular activities were measured and used to compute cortico-cortical, cortico-muscular, and intermuscular connectivity (i.e., coherences in the alpha, beta, and gamma bands), as well as features characterizing gait performance (e.g., the step-timing coordination, length, and speed). We observe that people with PD, mainly during the OFF medication, walk with reduced step-timing coordination. Additionally, our results suggest that dopamine intake in PD increases the overall cortico-muscular connectivity during the stance and swing phases of gait. We thus conclude that dopamine corrects defective feedback caused by impaired sensory-information processing and sensory-motor integration, thus increasing cortico-muscular coherences in the alpha bands and improving gait.

Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers.

Psychosis is characterized by a diminished ability of the brain to distinguish externally driven activity patterns from self-generated activity patterns. Antipsychotic drugs are a class of small molecules with relatively broad binding affinity for a variety of neuromodulator receptors that, in humans, can prevent or ameliorate psychosis. How these drugs influence the function of cortical circuits, and in particular their ability to distinguish between externally and self-generated activity patterns, is still largely unclear. To have experimental control over self-generated sensory feedback, we used a virtual reality environment in which the coupling between movement and visual feedback can be altered. We then used widefield calcium imaging to determine the cell type-specific functional effects of antipsychotic drugs in mouse dorsal cortex under different conditions of visuomotor coupling. By comparing cell type-specific activation patterns between locomotion onsets that were experimentally coupled to self-generated visual feedback and locomotion onsets that were not coupled, we show that deep cortical layers were differentially activated in these two conditions. We then show that the antipsychotic drug clozapine disrupted visuomotor integration at locomotion onsets also primarily in deep cortical layers. Given that one of the key components of visuomotor integration in cortex is long-range cortico-cortical connections, we tested whether the effect of clozapine was detectable in the correlation structure of activity patterns across dorsal cortex. We found that clozapine as well as two other antipsychotic drugs, aripiprazole and haloperidol, resulted in a strong reduction in correlations of layer 5 activity between cortical areas and impaired the spread of visuomotor prediction errors generated in visual cortex. Our results are consistent with the interpretation that a major functional effect of antipsychotic drugs is a selective alteration of long-range layer 5-mediated communication.

Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers

Psychosis is characterized by a diminished ability of the brain to distinguish externally driven activity patterns from self-generated activity patterns. Antipsychotic drugs are a class of small molecules with relatively broad binding affinity for a variety of neuromodulator receptors that, in humans, can prevent or ameliorate psychosis. How these drugs influence the function of cortical circuits, and in particular their ability to distinguish between externally and self-generated activity patterns, is still largely unclear. To have experimental control over self-generated sensory feedback, we used a virtual reality environment in which the coupling between movement and visual feedback can be altered. We then used widefield calcium imaging to determine the cell type-specific functional effects of antipsychotic drugs in mouse dorsal cortex under different conditions of visuomotor coupling. By comparing cell type-specific activation patterns between locomotion onsets that were experimentally coupled to self-generated visual feedback and locomotion onsets that were not coupled, we show that deep cortical layers were differentially activated in these two conditions. We then show that the antipsychotic drug clozapine disrupted visuomotor integration at locomotion onsets also primarily in deep cortical layers. Given that one of the key components of visuomotor integration in cortex is long-range cortico-cortical connections, we tested whether the effect of clozapine was detectable in the correlation structure of activity patterns across dorsal cortex. We found that clozapine as well as two other antipsychotic drugs, aripiprazole and haloperidol, resulted in a strong reduction in correlations of layer 5 activity between cortical areas and impaired the spread of visuomotor prediction errors generated in visual cortex. Our results are consistent with the interpretation that a major functional effect of antipsychotic drugs is a selective alteration of long-range layer 5-mediated communication.

Real-time cortical dynamics during motor inhibition

The inhibition of action is a fundamental executive mechanism of human behaviour that involve a complex neural network. In spite of the progresses made so far, many questions regarding the brain dynamics occurring during action inhibition are still unsolved. Here, we used a novel approach optimized to investigate real-time effective brain dynamics, which combines transcranial magnetic stimulation (TMS) with simultaneous electroencephalographic (EEG) recordings. 22 healthy volunteers performed a motor Go/NoGo task during TMS of the hand-hotspot of the primary motor cortex (M1) and whole-scalp EEG recordings. We reconstructed source-based real-time spatiotemporal dynamics of cortical activity and cortico-cortical connectivity throughout the task. Our results showed a task-dependent bi-directional change in theta/gamma supplementary motor cortex (SMA) and M1 connectivity that, when participants were instructed to inhibit their response, resulted in an increase of a specific TMS-evoked EEG potential (N100), likely due to a GABA-mediated inhibition. Interestingly, these changes were linearly related to reaction times, when participants were asked to produce a motor response. In addition, TMS perturbation revealed a task-dependent long-lasting modulation of SMA–M1 natural frequencies, i.e. alpha/beta activity. Some of these results are shared by animal models and shed new light on the physiological mechanisms of motor inhibition in humans.

Predictability of cortico-cortical connections in the mammalian brain.

Despite a five order of magnitude range in size, the brains of mammals share many anatomical and functional characteristics that translate into cortical network commonalities. Here we develop a machine learning framework to quantify the degree of predictability of the weighted interareal cortical matrix. Partial network connectivity data were obtained with retrograde tract-tracing experiments generated with a consistent methodology, supplemented by projection length measurements in a nonhuman primate (macaque) and a rodent (mouse). We show that there is a significant level of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an area under the ROC curve of at least 0.8 for the macaque. Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species. These observations reinforce earlier observations that the formation and evolution of the cortical network at the mesoscale is, to a large extent, rule based. Using the methodology presented here, we performed imputations on all area pairs, generating samples for the complete interareal network in both species. These are necessary for comparative studies of the connectome with minimal bias, both within and across species.