Cortical Connectivity Research Articles

Effects of rhythmic visual cues on cortical activation and functional connectivity features during stepping: an fNIRS study.

Rhythmic visual cues (RVCs) may influence gait initiation by modulating cognition resources. However, it is unknown how RVCs modulate cognitive resources allocation during gait movements. This study focused on investigating the effects of RVCs on cortical hemodynamic response features during stepping to evaluate the changes of cognitive resources. We recorded cerebral hemoglobin concentration changes of 14 channels in 17 healthy subjects using functional near-infrared spectroscopy (fNIRS) during stepping tasks under exposure to RVCs and non-rhythmic visual cues (NRVCs). We reported mean oxygenated hemoglobin (HbO) concentration changes, β-values, and functional connectivity (FC) between channels. The results showed that, the RVC conditions revealed lower HbO responses compared to the NRVC conditions during the preparation and early stepping. Correspondingly, the β-values reflected that RVCs elicited lower hemodynamic responses than NRVCs, and there was a decreasing trend in stimulus-evoked cortical activation as the task progressed. However, the FC between channels were stronger under RVCs than under NRVCs during the stepping progress, and there were more significant differences in FC during the early stepping. In conclusion, there were lower cognitive demand and stronger FC under RVC conditions than NRVC conditions, which indicated higher efficiency of cognitive resources allocation during stepping tasks. This study may provide a new insight for further understanding the mechanism on how RVCs alleviate freezing of gait.

Resting-state networks and their relationship with MoCA performance in PD patients.

Although mild cognitive impairment is a common non-motor symptom experienced by individuals with Parkinson's Disease, the changes in intrinsic resting-state networks associated with its onset in Parkinson's remain underexamined. To address the issue, our study sought to examine resting-state network alterations and their association with total performance in the Montreal Cognitive Assessment and its cognitive domains in Parkinson's by means of functional magnetic resonance imaging of 29 Parkinson's patients with normal cognition, 25 Parkinson's patients with mild cognitive impairment, and 13 healthy controls. To contrast the Parkinson's groups with each other and the controls, the images were used to estimate the Z-score coefficient between the regions of interest from the default mode network, the salience network and the central executive network. Our first finding was that default mode and salience network connectivity decreased significantly in Parkinson's patients regardless of their cognitive status. Additionally, default mode network nodes had a negative and salience network nodes a positive correlation with the global assessment in Parkinson's with normal cognition; this inverse relationship of both networks to total score was not found in the group with cognitive impairment. Finally, a positive correlation was found between executive scores and anterior and posterior cortical network connectivity and, in the group with cognitive impairment, between language scores and salience network connectivity. Our results suggest that specific resting-state networks of Parkinson's patients with cognitive impairment differ from those of Parkinson's patients with normal cognition, supporting the evidence that cognitive impairment in Parkinson's Disease displays a differentiated neurodegenerative pattern.

Suspected Mal de Debarquement Syndrome: A Case Report Highlighting the Difficulty in Diagnosis and Management.

MdDS is syndrome of oscillating vertigo following cessation of passive motion. The pathogenesis of this disorder is not well understood, but functional imaging studies suggest cortical connectivity dysfunction in feedback loops of the vestibulo-ocular system and visuo-spatial system. Patients go through multiple appointments and often specialist referrals before being diagnosed. After diagnosis, optimal management is difficult. Several treatment modalities, including medication, vestibular rehabilitation, and neuromodulation, have had variable success in management. We present the case of a young, female active duty Air Force Captain who developed symptoms while deployed. She underwent multiple treatments with variable success. Her clinical course highlights the difficulties for patients and providers posed by suspected MdDS.

Effects of different frequencies music on cortical responses and functional connectivity in patients with minimal conscious state.

The objective of this study was to investigate brain activation and functional network patterns during musical interventions in different frequency bands using functional near-infrared spectroscopy, and to provide a basis for more effective music therapy strategy selection for patients in minimally conscious state (MCS). Twenty six MCS patients and 20 healthy people were given music intervention with low frequency (31-180 Hz), medium frequency (180-4k Hz), and high frequency (4k-22k Hz) audio. In MCS patients, low frequency music intervention induced activation of left prefrontal cortex and left primary sensory cortex (S1), also a left-hemisphere lateralization effect of dorsolateral prefrontal cortex (DLPFC). And the functional connectivity of right DLPFC-right S1 was significantly improved by high frequency music intervention. The low frequency and high frequency music may contribute more than medium frequency music to the recovery of consciousness. This study also validated the effectiveness of fNIRS in studies of brain function in MCS patients.

Functional subtypes of synaptic dynamics in mouse and human

Synapses preferentially respond to particular temporal patterns of activity with a large degree of heterogeneity that is informally or tacitly separated into classes. Yet, the precise number and properties of such classes are unclear. Do they exist on a continuum and, if so, when is it appropriate to divide that continuum into functional regions? In a large dataset of glutamatergic cortical connections, we perform model-based characterization to infer the number and characteristics of functionally distinct subtypes of synaptic dynamics. In rodent data, we find five clusters that partially converge with transgenic-associated subtypes. Strikingly, the application of the same clustering method in human data infers a highly similar number of clusters, supportive of stable clustering. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.

Combining electrodermal activity analysis and dynamic causal modeling to investigate the visual-odor multimodal integration during face perception

Objective. This study presents a novel methodological approach for incorporating information related to the peripheral sympathetic response into the investigation of neural dynamics. Particularly, we explore how hedonic contextual olfactory stimuli influence the processing of neutral faces in terms of sympathetic response, event-related potentials and effective connectivity analysis. The objective is to investigate how the emotional valence of odors influences the cortical connectivity underlying face processing and the role of face-induced sympathetic arousal in this visual-olfactory multimodal integration. Approach. To this aim, we combine electrodermal activity (EDA) analysis and dynamic causal modeling to examine changes in cortico-cortical interactions. Results. The results reveal that stimuli arising sympathetic EDA responses are associated with a more negative N170 amplitude, which may be a marker of heightened arousal in response to faces. Hedonic odors, on the other hand, lead to a more negative N1 component and a reduced the vertex positive potential when they are unpleasant or pleasant. Concerning connectivity, unpleasant odors strengthen the forward connection from the inferior temporal gyrus (ITG) to the middle temporal gyrus, which is involved in processing changeable facial features. Conversely, the occurrence of sympathetic responses after a stimulus is correlated with an inhibition of this same connection and an enhancement of the backward connection from ITG to the fusiform face gyrus. Significance. These findings suggest that unpleasant odors may enhance the interpretation of emotional expressions and mental states, while faces capable of eliciting sympathetic arousal prioritize identity processing.

Neuroadaptive Tracking Control of Affine Nonlinear Systems Using Echo State Networks Embedded With Multiclustered Structure and Intrinsic Plasticity.

In this article, we present an echo state network (ESN)-based tracking control approach for a class of affine nonlinear systems. Different from the most existing neural-network (NN)-based control methods that are focused on the feedforward NN, the proposed method adopts a bioinspired recurrent NN fusing with multiple cluster and intrinsic plasticity (IP) to deal with modeling uncertainties and coupling nonlinearities in the systems. The key features of this work can be summarized as follows: 1) the proposed control is built upon the ESN embedded with multiclustered reservoir inspired from the hierarchically clustered organizations of cortical connections in mammalian brains; 2) the developed neuroadaptive control scheme utilizes unsupervised learning rules inspired from the neural plasticity mechanism of the individual neuron in nervous systems, called IP; 3) a multiclustered reservoir with IP is integrated into the algorithm to enhance the approximation performance of NN; and 4) the multiclustered reservoir is constructed offline and is task-independent, rendering the proposed method less expensive in computation. The effectiveness of the method is also confirmed by comparison with the existing neuroadaptive methods via numerical simulations, demonstrating that better tracking precision is achieved by the proposed method.

Traumatic brain injury disrupts state-dependent functional cortical connectivity in a mouse model.

Traumatic brain injury (TBI) is the leading cause of death in young people and can cause cognitive and motor dysfunction and disruptions in functional connectivity between brain regions. In human TBI patients and rodent models of TBI, functional connectivity is decreased after injury. Recovery of connectivity after TBI is associated with improved cognition and memory, suggesting an important link between connectivity and functional outcome. We examined widespread alterations in functional connectivity following TBI using simultaneous widefield mesoscale GCaMP7c calcium imaging and electrocorticography (ECoG) in mice injured using the controlled cortical impact (CCI) model of TBI. Combining CCI with widefield cortical imaging provides us with unprecedented access to characterize network connectivity changes throughout the entire injured cortex over time. Our data demonstrate that CCI profoundly disrupts functional connectivity immediately after injury, followed by partial recovery over 3weeks. Examining discrete periods of locomotion and stillness reveals that CCI alters functional connectivity and reduces theta power only during periods of behavioral stillness. Together, these findings demonstrate that TBI causes dynamic, behavioral state-dependent changes in functional connectivity and ECoG activity across the cortex.

Corticocortical connections of the rostral forelimb area in rats: a quantitative tract-tracing study.

The rostral forelimb area (RFA) in the rat is a premotor cortical region based on its dense efferent projections to primary motor cortex. This study describes corticocortical connections of RFA and the relative strength of connections with other cortical areas. The goal was to provide a better understanding of the cortical network in which RFA participates, and thus, determine its function in sensorimotor behavior. The RFA of adult male Long-Evans rats (n= 6) was identified using intracortical microstimulation techniques and injected with the tract-tracer, biotinylated dextran amine (BDA). In post-mortem tissue, locations of BDA-labeled terminal boutons and neuronal somata were plotted and superimposed on cortical field boundaries. Quantitative estimates of terminal boutons in each region of interest were based on unbiased stereological methods. The results demonstrate that RFA has dense connections with primary motor cortex and frontal cortex medial and lateral to RFA. Moderate connections were found with insular cortex, primary somatosensory cortex (S1), the M1/S1 overlap zone, and lateral somatosensory areas. Cortical connections of RFA in rat are strikingly similar to cortical connections of the ventral premotor cortex in non-human primates, suggesting that these areas share similar functions and allow greater translation of rodent premotor cortex studies to primates.

Cell type-specific connectome predicts distributed working memory activity in the mouse brain.

Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.

Obstacle Avoidance in Healthy Adults and People With Multiple Sclerosis: Preliminary fNIRS Study.

This study examined how gait adaptation during predictable and non-predictable obstacle avoidance affects the sensorimotor network in both healthy controls (HC) and persons with multiple sclerosis (pwMS). We utilized fNIRS measurements of HbO2 and HHb to estimate cortical activations and connectivity networks, which were then analyzed using power spectral density (PSD) and partial directed coherence (PDC). The findings revealed distinct patterns of cortical activation and connectivity for each task condition in both groups. Healthy individuals displayed lower cortical activations in the bilateral motor cortex (MC) during non-predictable obstacle avoidance, indicating efficient neural processing. On the other hand, pwMS exhibited lower cortical activations across most brain areas during non-predictable tasks, suggesting potential limitations in neural resource allocation. When tasks were combined, pwMS demonstrated higher cortical activation across all recorded brain areas compared to HC, indicating a compensatory mechanism to maintain gait stability. Functional connectivity analysis revealed that pwMS recruited higher bilateral somatosensory association cortex (SAC) than HC, whereas healthy individuals engaged more bilateral premotor cortices (PMC). These findings suggest alterations in sensorimotor integration and motor planning in pwMS. Four machine learning models (KNN, SVM, DT, and DA) achieved high classification accuracies (92-99%) in differentiating between task conditions within each group. These results highlight the potential of integrating fNIRS-based cortical activation and connectivity measures with machine learning as biomarkers for MS-related impairments in cognitive-motor interaction. Such biomarkers could aid in predicting future mobility decline, fall risk, and disease progression.

Investigating cortical complexity and connectivity in rats with schizophrenia.

The above studies indicate that the SCZ animal model has abnormal gamma oscillations and abnormal functional coupling ability of brain regions at the cortical level. However, few researchers have focused on the correlation between brain complexity and connectivity at the cortical level. In order to provide a more accurate representation of brain activity, we studied the complexity of electrocorticogram (ECoG) signals and the information interaction between brain regions in schizophrenic rats, and explored the correlation between brain complexity and connectivity. We collected ECoG signal from SCZ rats. The frequency domain and time domain functional connectivity of SCZ rats were evaluated by magnitude square coherence and mutual information (MI). Permutation entropy (PE) and permutation Lempel-Ziv complexity (PLZC) were used to analyze the complexity of ECoG, and the relationship between them was evaluated. In addition, in order to further understand the causal structure of directional information flow among brain regions, we used phase transfer entropy (PTE) to analyze the effective connectivity of the brain. Firstly, in the high gamma band, the complexity of brain regions in SCZ rats is higher than that in normal rats, and the neuronal activity is irregularity. Secondly, the information integration ability of SCZ rats decreased and the communication of brain network information was hindered at the cortical level. Finally, compared with normal rats, the causal relationship between brain regions of SCZ rats was closer, but the information interaction center was not clear. The above findings suggest that at the cortical level, complexity and connectivity are valid biomarkers for identifying SCZ. This bridges the gap between peak potentials and EEG. This may help to understand the pathophysiological mechanisms at the cortical level in schizophrenics.

Cortical and subcortical functional connectivity and cognitive impairment in Parkinson’s disease

Parkinson’s disease (PD) is a neurodegenerative disease with cognitive as well as motor impairments. While much is known about the brain networks leading to motor impairments in PD, less is known about the brain networks contributing to cognitive impairments. Here, we leveraged resting-state functional magnetic resonance imaging (rs-fMRI) data from the Parkinson’s Progression Marker Initiative (PPMI) to examine network dysfunction in PD patients with cognitive impairment. We focus on canonical cortical networks linked to cognition, including the salience network (SAL), frontoparietal network (FPN), and default mode network (DMN), as well as a subcortical basal ganglia network (BGN). We used the Montreal Cognitive Assessment (MoCA) as a continuous index of coarse cognitive function in PD. In 82 PD patients, we found that lower MoCA scores were linked with lower intra-network connectivity of the FPN. We also found that lower MoCA scores were linked with lower inter-network connectivity between the SAL and the BGN, the SAL and the DMN, as well as the FPN and the DMN. These data elucidate the relationship of cortical and subcortical functional connectivity with cognitive impairments in PD.

Intrinsic functional connectivity strength of SuperAgers in the default mode and salience networks: Insights from ADNI

There exists a group of older individuals who appear to be resistant to age-related memory decline. These “SuperAgers” have been shown to demonstrate preservation of cortical thickness and functional connectivity strength across the cortex which positively correlates with memory performance. Over the last decade, roughly 30 articles have been published regarding SuperAgers; however, to our knowledge, no replications of these studies have been published. The current study sought to conceptually replicate Zhang and colleagues’ (2020) findings that SuperAgers demonstrate stronger intrinsic functional connectivity within the default mode (DMN) and salience networks (SN), and that connectivity strength within these networks correlates with memory performance. We identified 20 SuperAgers and 20 matched Normal Agers in the control cohort of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We compared the functional connectivity strength of the DMN and SN between these groups, and used the Rey Auditory Verbal Learning Test (RAVLT) to evaluate correlations between functional connectivity and memory performance. Our results did not replicate Zhang and colleagues’ (2020) results, as we found negligible differences between SuperAgers and Normal Agers in the DMN and SN, and no significant correlations between functional connectivity and memory performance after accounting for multiple comparisons. More replications are needed to confirm existing work. In addition, more research with larger SuperAger samples and more consistent definitions of SuperAging is needed, so that we can better understand this remarkable group of older adults.

Computational Fractal-Based Neurosurgery.

Fractal geometry is a branch of mathematics used to characterize and quantify the geometrical complexity of natural objects, with many applications in different fields, including physics, astronomy, geology, meteorology, finances, social sciences, and computer graphics. In the biomedical sciences, the use of fractal parameters has allowed the introduction of novel morphometric parameters, which have been shown to be useful to characterize any biomedical images as well as any time series within different domains of applications. Specifically, in the neurosciences and neurosurgery, the use of the fractal dimension and other computationally inferred fractal parameters has offered robust morphometric quantitators to characterize the brain in its wholeness, from neurons to the cortical structure and connections, and introduced new prognostic, diagnostic, and eventually therapeutic markers of many diseases of neurosurgical interest, including brain tumors and cerebrovascular malformations, as summarized in this chapter.

Sex and Gender in Population Neuroscience.

To understand psychiatric and neurological disorders and the structural and functional properties of the human brain, it is essential to consider the roles of sex and gender. In this chapter, I first define sex and gender and describe studies of sex differences in non-human animals. In humans, I describe the sex differences in behavioral and clinical phenotypes and neuroimaging-derived phenotypes, including whole-brain measures, regional subcortical and cortical measures, and structural and functional connectivity. Although structural whole-brain sex differences are large, regional effects (adjusting for whole-brain volumes) are typically much smaller and often fail to replicate. Nevertheless, while an individual neuroimaging feature may have a small effect size, aggregating them in a "maleness/femaleness" score or machine learning multivariate paradigm may prove to be predictive and informative of sex- and gender-related traits. Finally, I conclude by summarizing emerging investigations of gender norms and gender identity and provide methodological recommendations to incorporate sex and gender in population neuroscience research.

Cognitive Adaptation and Cortical Connectivity Alterations during Daily Tasks with Supernumerary Robotic Finger Assistance

Cognitive Adaptation and Cortical Connectivity Alterations during Daily Tasks with Supernumerary Robotic Finger Assistance

Deciphering Authentic Nociceptive Thalamic Responses in Rats.

The thalamus and its cortical connections play a pivotal role in pain information processing, yet the exploration of its electrophysiological responses to nociceptive stimuli has been limited. Here, in 2 experiments we recorded neural responses to nociceptive laser stimuli in the thalamic (ventral posterior lateral nucleus and medial dorsal nucleus) and cortical regions (primary somatosensory cortex [S1] and anterior cingulate cortex) within the lateral and medial pain pathways. We found remarkable similarities in laser-evoked brain responses that encoded pain intensity within thalamic and cortical regions. Contrary to the expected temporal sequence of ascending information flow, the recorded thalamic response (N1) was temporally later than its cortical counterparts, suggesting that it may not be a genuine thalamus-generated response. Importantly, we also identified a distinctive component in the thalamus, i.e., the early negativity (EN) occurring around 100 ms after the onset of nociceptive stimuli. This EN component represents an authentic nociceptive thalamic response and closely synchronizes with the directional information flow from the thalamus to the cortex. These findings underscore the importance of isolating genuine thalamic neural responses, thereby contributing to a more comprehensive understanding of the thalamic function in pain processing. Additionally, these findings hold potential clinical implications, particularly in the advancement of closed-loop neuromodulation treatments for neurological diseases targeting this vital brain region.

Cortical Network Disruption Is Minimal in Early Stages of Psychosis.

Schizophrenia is associated with white matter disruption and topological reorganization of cortical connectivity but the trajectory of these changes, from the first psychotic episode to established illness, is poorly understood. Current studies in first-episode psychosis (FEP) patients using diffusion magnetic resonance imaging (dMRI) suggest such disruption may be detectable at the onset of psychosis, but specific results vary widely, and few reports have contextualized their findings with direct comparison to young adults with established illness. Diffusion and T1-weighted 7T MR scans were obtained from N = 112 individuals (58 with untreated FEP, 17 with established schizophrenia, 37 healthy controls) recruited from London, Ontario. Voxel- and network-based analyses were used to detect changes in diffusion microstructural parameters. Graph theory metrics were used to probe changes in the cortical network hierarchy and to assess the vulnerability of hub regions to disruption. The analysis was replicated with N = 111 (57 patients, 54 controls) from the Human Connectome Project-Early Psychosis (HCP-EP) dataset. Widespread microstructural changes were found in people with established illness, but changes in FEP patients were minimal. Unlike the established illness group, no appreciable topological changes in the cortical network were observed in FEP patients. These results were replicated in the early psychosis patients of the HCP-EP datasets, which were indistinguishable from controls in most metrics. The white matter structural changes observed in established schizophrenia are not a prominent feature in the early stages of this illness.

Three-phase Enriched Environment Improves Post-stroke Gait Dysfunction via Facilitating Neuronal Plasticity in the Bilateral Sensorimotor Cortex: A Multimodal MRI/PET Analysis in Rats.

The three-phase Enriched Environment (EE) paradigm has been shown to promote post-stroke functional improvement, but the neuronal mechanisms are still unclear. In this study, we applied a multimodal neuroimaging protocol combining magnetic resonance imaging (MRI) and positron emission tomography (PET) to examine the effects of post-ischemic EE treatment on structural and functional neuroplasticity in the bilateral sensorimotor cortex. Rats were subjected to permanent middle cerebral artery occlusion. The motor function of the rats was examined using the DigiGait test. MRI was applied to investigate the EE-induced structural modifications of the bilateral sensorimotor cortex. [18F]-fluorodeoxyglucose PET was used to detect glucose metabolism. Blood oxygen level-dependent (BOLD)-functional MRI (fMRI) was used to identify the regional brain activity and functional connectivity (FC). In addition, the expression of neuroplasticity-related signaling pathways including neurotrophic factors (BDNF/CREB), axonal guidance proteins (Robo1/Slit2), and axonal growth-inhibitory proteins (NogoA/NgR) as well as downstream proteins (RhoA/ROCK) in the bilateral sensorimotor cortex were measured by Western blots. Our results showed the three-phase EE improved the walking ability. Structural T2 mapping imaging and diffusion tensor imaging demonstrated that EE benefited structure integrity in the bilateral sensorimotor cortex. PET-MRI fused images showed improved glucose metabolism in the corresponding regions after EE intervention. Specifically, the BOLD-based amplitude of low-frequency fluctuations showed that EE increased spontaneous activity in the bilateral motor cortex and ipsilateral sensory cortex. In addition, FC results showed increased sensorimotor connectivity in the ipsilateral hemisphere and increased interhemispheric motor cortical connectivity and motor cortical-thalamic connectivity following EE intervention. In addition, a strong correlation was found between increased functional connectivity and improved motor performance of limbs. Specifically, EE regulated the expression of neuroplasticity-related signaling, involving BDNF/CREB, Slit2/Robo1, as well as the axonal growth-inhibitory pathways Nogo-A/Nogo receptor and RhoA/ROCK in the bilateral sensorimotor cortex. Our results indicated that the three-phase enriched environment paradigm enhances neuronal plasticity of the bilateral sensorimotor cortex and consequently ameliorates post-stroke gait deficits. These findings might provide some new clues for the development of EE and thus facilitate the clinical translation of EE.