Neural Activity Research Articles

Feasibility of utilizing functional near-infrared spectroscopy to measure the cognitive load of paramedicine students undertaking high-acuity clinical simulations in Australia: a case study.

Paramedicine education often uses high-fidelity simulations that mimic real-life emergencies. These experiences can trigger stress responses characterized by physiological changes, including alterations in cerebral blood flow and oxygenation. Functional near-infrared spectroscopy (fNIRS) is emerging as a promising tool for assessing cognitive stress in educational settings. Eight final-year undergraduate paramedicine students completed 2 high-acuity scenarios 7 days apart. Real-time continuous recording of cerebral blood flow and oxygenation levels in the prefrontal cortex was undertaken via fNIRS as a means of assessing neural activity during stressful scenarios. fNIRS accurately determined periods of increased cerebral oxygenation when participants were undertaking highly technical skills or making significant clinical decisions. fNIRS holds potential for objectively measuring the cognitive load in undergraduate paramedicine students. By providing real-time insights into neurophysiological responses, fNIRS may enhance training outcomes in paramedicine programs and improve student well-being (Australian New Zealand Clinical Trials Registry: ACTRN12623001214628).

Multi-regional control of amygdalar dynamics reliably reflects fear memory age

The basolateral amygdala (BLA) is crucial for the encoding and expression of fear memory, yet it remains unexplored how neural activity in this region is dynamically influenced by distributed circuits across the brain to facilitate expression of fear memory of different ages. Using longitudinal multisite electrophysiological recordings in male mice, we find that the recall of older contextual fear memory is accompanied by weaker, yet more rhythmic, BLA gamma activity which is distally entrained by theta oscillations in both the hippocampal CA1 and the anterior cingulate cortex. Computational modeling with Light Gradient Boosting Machine using extracted oscillatory features from these three regions, as well as with Transformer using raw local field potentials, accurately classified remote from recent memory recall primarily based on BLA gamma and CA1 theta. These results demonstrate in a non-biased manner that multi-regional control of BLA activity serves as reliable neural signatures for memory age-dependent recall mechanisms.

Imaging distinct neuronal populations with a dual channel miniscope

Miniature fluorescence microscopes (miniscopes) are one of the most powerful and versatile tools for recording large scale neural activity in freely moving rodents with single cell resolution. Recent advances in the design of genetically encoded calcium indicators (GECIs) allow to target distinct neuronal populations with non-overlapping emission spectral profiles. However, conventional miniscopes are limited to a single excitation, single focal plane imaging, which does not allow to compensate for chromatic aberration and image from two spectrally distinct calcium indicators. In this paper we present an open-source dual channel miniscope capable of simultaneous imaging of genetically or functionally distinct neuronal populations. Chromatic aberrations are corrected using an electrowetting lens (EWL), which allows fast focal plane change between frames. To demonstrate the capabilities of the dual channel miniscope, we labeled layer specific excitatory neurons or inhibitory interneurons in the medial prefrontal cortex (mPFC) with a red fluorescence protein, and simultaneously imaged neural activity of distinct neuronal populations of freely moving mice via a green GECI.

Long -term multimodal exercise intervention for patients with frontotemporal lobar degeneration: Feasibility and preliminary outcomes

Introduction: After Alzheimer's disease, frontotemporal lobar degeneration (FTLD) is the second most common form of early-onset dementia. Despite the heavy burden of care for FTLD, pharmacological and non-pharmacological treatments with sufficient efficacy remain scarce. This study aimed to evaluate the feasibility of a multimodal exercise program for FTLD and to examine preliminary changes in the clinical outcomes of the program in FTLD. Methods: This single-arm preliminary study was conducted from July 2017 to July 2018 and recruited four male patients with FTLD aged 60–78 years. Patients exercised under the supervision of an exercise instructor once every 2 weeks for 48 weeks. The multimodal exercise program comprised cognitive training, moderate-intensity continuous training, strength training, balance training, and flexibility and relaxation training. Feasibility was measured using dropout and attendance rates. Cognitive, psychological, physical, and behavioral function tests were conducted before and after the intervention. Results: All patients completed the intervention (100%) and attended well (93.6%). Positive changes in scores in the Stroop Color Word Test (cognitive; 5 out of 6 items), Mood Check List-short form 2 (psychological), movement subscales of the Stereotypy Rating Inventory (behavioral), and timed up-and-go (TUG, physical) assessments demonstrated a medium-to-high effect size (open effect size: 0.52–0.97). While there were improvements in some domains, such as recovery self-efficacy and exercise efficacy, the MMSE-J scores showed an overall slight decline, especially in the SD case where a marked decrease was observed. Additionally, three physical function items showed no effect, except for a positive outcome in the TUG test. Functional near-infrared spectroscopy (fNIRS) revealed increased activation in the frontal lobe, indicated by elevated oxygenated hemoglobin levels before and after the exercise intervention. This pattern of activation suggests that the intervention may have stimulated neural activity in the frontal lobe, potentially enhancing cognitive and behavioral functions, including executive function and attention. Conclusion: The long-term multimodal exercise intervention may be feasible and positively change the cognitive, psychological, physical, and behavioral functions in older adults with FTLD. Although the intervention led to improvements in certain areas, there were also declines observed in various functions, which may not necessarily be due to the intervention itself but rather reflect the natural progression of the disease.

How interoceptive sensibility moderates decision-making: an fMRI study of neuroforecasting mobile games engagement.

Neuroscientists in decision sciencehaveadvanced an affect-integration-motivation (AIM) framework,demonstrating that neural activity associatedwith positive affect or value integrationcan predict individual and aggregate choice.Given that individuals with higher interoceptive sensibility (IS) have tendency to engage their bodily sensations and thus exhibit a more coherent pattern between their neural, affective, and behavioral measures, we investigated how IS may interact with the affective/integrative components for predicting individual and aggregate choice. Thus, we 1) explored neural underpinnings of individual choice, affective ratings, aggregate outcomes, 2) examined howthe above-mentioned measures predict individual and aggregate choices on mobile games, and 3) tested the moderation effect of IS by comparing the differences in how these measures perform in prediction models between subgroups of IS. Neuroimaging results showed that individual choice associated with NAcc activity, aggregate download rate tracked by regions in salience network, and revenue additionally tracked by regions in motor tendency and attention regulation. Affective ratings and AIns activity predicted individual download choice; mPFC activity forecasted aggregate download rate, and positive arousal forecasted aggregate revenue. As hypothesized, the high IS group displayed coherent correlations between affective ratings, individual choice, and neural measures. More importantly, at the aggregate level, mPFC activity (integrative component), forecasted aggregate download rate above and beyond ratings and individual choice in the high IS group, with this prediction significantly stronger compared with the low IS group. These findingsextend the AIMframework by shedding light on the influence of interoceptive sensibility on the neurobehavioral mechanisms underlying human decision-making.

Decoding contextual influences on auditory perception from primary auditory cortex

Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus – a tritone pair – to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pair’s perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that cells responding to pitch-changes are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the individual pitch representations and taking their distance in the circular Shepard tone space predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch and pitch-direction cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.

Decoding contextual influences on auditory perception from primary auditory cortex.

Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus - a tritone pair - to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pair's perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that cells responding to pitch-changes are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the individual pitch representations and taking their distance in the circular Shepard tone space predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch and pitch-direction cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.

Brain region-specific neural activation by low-dose opioid promotes social behavior.

The opioid system plays crucial roles in modulating social behaviors in both humans and animals. However, the pharmacological profiles of opioids regarding social behavior and their therapeutic potential remain unclear. Multiple pharmacological, behavioral, and immunohistological c-Fos mapping approaches were used to characterize the effects of μ-opioid receptor agonists on social behavior and investigate the mechanisms in naive mice and autism spectrum disorder-like (ASD-like) mouse models, such as prenatally valproic acid-treated mice and Fmr1-KO mice. Here, we report that low-dose morphine, a μ-opioid receptor agonist, promoted social behavior by selectively activating neurons in prosocial brain regions, including the nucleus accumbens, but not those in the dorsomedial periaqueductal gray (dmPAG), which are only activated by analgesic high-dose morphine. Critically, intra-dmPAG morphine injection counteracted the prosocial effect of low-dose morphine, suggesting that dmPAG neural activation suppresses social behavior. Moreover, buprenorphine, a μ-opioid receptor partial agonist with less abuse liability and a well-established safety profile, ameliorated social behavior deficits in two mouse models recapitulating ASD symptoms by selectively activating prosocial brain regions without dmPAG neural activation. Our findings highlight the therapeutic potential of brain region-specific neural activation induced by low-dose opioids for social behavior deficits in ASD.

Brain computer interface for diagnosis of ASD

ASD, or Autism Spectrum Disorder, is a complex neurodevelopmental disorder that significantly impacts an individual's social communication abilities and overall communication skills. Traditional diagnostic methods for ASD face several challenges, including a high degree of subjectivity and difficulties in early identification, which can delay crucial interventions. In this context, Brain-Computer Interface (BCI) technology emerges as a promising solution. BCI technology offers the capability to provide real-time and objective data on neural activity by directly interpreting brain signals. The paper begins by detailing the clinical characteristics of ASD, highlighting the diverse range of symptoms and behaviors that can manifest in affected individuals. It also discusses the limitations of current diagnostic methods, emphasizing the need for more objective tools. Following this, the fundamental principles and classifications of BCI technology are explained in depth, outlining how it works and the various types of BCIs available. Specific application cases of BCI technology in the diagnosis of ASD are presented, illustrating its practical utility in clinical settings. In conclusion, the paper summarizes the key points discussed and anticipates future developments in the field.

FMRI used to observe the acute craniocerebral response of esophageal cancer related depressive patients treated by rTMS: Initial experience.

To observe the immediate craniocerebral response, changes of spontaneous nerve activity and functional connection after repeated transcranial magnetic stimulation (rTMS) in esophageal cancer patients with depression (ECPD) by functional magnetic resonance imaging (fMRI), and to explore the therapeutic effect, neuroactivity response and mechanism. Eleven patients with ECPD were enrolled to treated with single rTMS. The patients were examined by fMRI before and after the treatment. The changes of low frequency amplitude (ALFF) and the functional connection network between different brain regions of ALFF in patients were compared before and after rTMS treatment. Compared with those before rTMS treatment, the Hamilton Depression Rating Scale (HAMD) score decreased significantly after rTMS treatment (t = -7.63, P = .0001). The ALFF of bilateral putamen, left thalamus, left posterior cingulate gyrus and right middle temporal gyrus decreased significantly (P = .02). In addition, the functional connection between the cortex-limbic system-striatum-thalamus nerve loop increased in patients after rTMS treatment. rTMS may achieve the effect of rehabilitation treatment by improving the spontaneous neural activity and regulating the neural connection network of cortical-limbic systems-triatum-thalamus loop in ECPD.

Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.

To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all three participants, the neural network led to offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modification for new datasets, brain areas and participants.

Inferring laminar origins of MEG signals with optically pumped magnetometers (OPMs): a simulation study

Abstract We explore the potential of optically pumped magnetometers (OPMs) to non-invasively infer the laminar origins of neural activity. OPM sensors can be positioned closer to the scalp than conventional cryogenic MEG sensors, opening an avenue to higher spatial resolution when combined with high-precision source space modelling. By simulating the forward model projection of single dipole sources at deep and superficial cortical surfaces onto OPM sensor arrays with varying sensor densities and measurement axes, and employing sparse source reconstruction approaches, we find that laminar inference with OPM arrays is possible at relatively low sensor counts under moderate to high signal-to-noise ratios (SNR). We observe improvements in laminar inference with increasing spatial sampling densities and measurement axes and demonstrate the advantage of placing the sensors closer to the scalp for inferring the laminar origins of cortical sources. However, challenges remain, such as biases towards both the superficial and deep surfaces at very low SNRs and a notable bias towards the deep surface when combining empirical Bayesian beamformer (EBB) source reconstruction with a whole-brain analysis. Adequate SNR through appropriate trial numbers and shielding, as well as precise co-registration, is crucial for reliable laminar inference with OPMs.

Representation of a perceptual bias in the prefrontal cortex

Perception is influenced by sensory stimulation, prior knowledge, and contextual cues, which collectively contribute to the emergence of perceptual biases. However, the precise neural mechanisms underlying these biases remain poorly understood. This study aims to address this gap by analyzing neural recordings from the prefrontal cortex (PFC) of monkeys performing a vibrotactile frequency discrimination task. Our findings provide empirical evidence supporting the hypothesis that perceptual biases can be reflected in the neural activity of the PFC. We found that the state-space trajectories of PFC neuronal activity encoded a warped representation of the first frequency presented during the task. Remarkably, this distorted representation of the frequency aligned with the predictions of its Bayesian estimator. The identification of these neural correlates expands our understanding of the neural basis of perceptual biases and highlights the involvement of the PFC in shaping perceptual experiences. Similar analyses could be employed in other delayed comparison tasks and in various brain regions to explore where and how neural activity reflects perceptual biases during different stages of the trial.

Propofol‐induced moderate–deep sedation modulates pediatric neural activity: A functional connectivity study

AbstractBackgroundPrevious studies have demonstrated the underlying neurophysiologic mechanism during general anesthesia in adults. However, the mechanism of propofol‐induced moderate–deep sedation (PMDS) in modulating pediatric neural activity remains unknown, which therefore was investigated in the present study based on functional magnetic resonance imaging (fMRI).MethodsA total of 41 children (5.10 ± 1.14 years, male/female 21/20) with fMRI were employed to construct the functional connectivity network (FCN). The network communication, graph‐theoretic properties, and network hub identification were statistically analyzed (t test and Bonferroni correction) between sedation (21 children) and awake (20 children) groups. All involved analyses were established on the whole‐brain FCN and seven sub‐networks, which included the default mode network (DMN), dorsal attentional network (DAN), salience network (SAN), auditory network (AUD), visual network (VIS), subcortical network (SUB), and other networks (Other).ResultsUnder PMDS, significant decreases in network communication were observed between SUB‐VIS, SUB‐DAN, and VIS‐DAN, and between brain regions from the temporal lobe, limbic system, and subcortical tissues. However, no significant decrease in thalamus‐related communication was observed. Most graph‐theoretic properties were significantly decreased in the sedation group, and all graphical features of the DMN showed significant group differences. The superior parietal cortex with different neurological functions was identified as a network hub that was not greatly affected.ConclusionsAlthough the children had a depressed level of neural activity under PMDS, the crucial thalamus‐related communication was maintained, and the network hub superior parietal cortex stayed active, which highlighted clinical practices that the human body under PMDS is still perceptible to external stimuli and can be awakened by sound or touch.

Loss of the mitochondrial protein mitoNEET in mice is associated with cognitive impairments and increased neuroinflammation.

Mitochondrial dysfunction is implicated in several neurodegenerative diseases associated with memory and cognitive deficits, including Alzheimer's disease. Changes in bioenergetic function results in reactive oxygen species, oxidative damage and consequently neuroinflammation, which contributes to neuronal cell loss. In this study, we evaluated the impact of the loss of the redox active [2Fe-2S] mitochondrial-associated protein mitoNEET (CISD1) on neuroinflammation and cognition using an age-appropriate preclinical model. While associations between neuroinflammation and poor cognitive impacts have been shown in recent work, little has been done to assess whether loss of mitoNEET is associated with changes in neuroinflammatory markers or negative cognitive-behavioral outcomes. Using 9-11-month-old mitoNEET knockout (CISD1-/-) and wild-type mice, we conducted a battery of cognitive tests to assess the impact of mitoNEET loss on performance. We then histologically evaluated the effect of absence of mitoNEET on markers of neuroinflammation in the aged brain. We found loss of mitoNEET in mice was associated with a significant reduction in willingness to explore within an open field and impaired short-term spatial working memory in the Y-maze. We also found a significant reduction in novel object recognition memory that was gene-dependent and accompanied by reduced c-fos expression in hippocampus and cortical regions. Our findings indicate that mitoNEET loss is significantly associated with impairments in cognitive-behavioral and neuroinflammatory outcomes; specifically, learning and memory, anxiety-like behaviors, neuroinflammation, and neural activation. This is the first study to demonstrate cognitive-associated behavioral deficits with neuroinflammation in the mitoNEET knockout mouse model.

Caregiver or Playmate? Fathers' and mothers' brain responses to ball-play with children.

Parents and children often engage in joint play-a domain where mothers and fathers are thought to exhibit disparate behaviors and impact child development via distinct mechanisms. However, little is known about the neural substrates of mother-child and father-child play. In this fMRI study, we sampled thebrain activation of parents of preschoolers (N = 88) during a novel event-related adaptation of the virtual ball-tossing game "Cyberball." Mothers (N = 40) and fathers (N = 48) played "Cyberball" ostensibly with their own and an unrelated child, whoconsecutively included, excluded, and reincluded parents. We found that overall, exclusion yielded comparable neural activations in mothers and fathers associated with mentalizing, saliency, and emotionprocessing. We also observed a parent gender effect in several brain areas. While mothers exhibited increased reward- and attention-related activity during inclusion, fathers displayed increased mentalizing-related activity during exclusion. Furthermore,we tested parents' response to reinclusion, which revealed a selective decrease in reward-related activity. Finally, exploratory analyses showed that parental involvement was positively correlated with parental brain activity within attention- and mentalizing-related areas during inclusion, as opposed to other game phases, and that an anxious parenting style was associated with increased neural sensitivity for game events involving their own child. Overall, our study elucidates the common and distinct neural networks that mothers and fathers engage during play interactions with their children, supporting theories that postulate only a partial differentiation of paternal and maternal parenting systems.

AutoMEA: machine learning-based burst detection for multi-electrode array datasets

Neuronal activity in the highly organized networks of the central nervous system is the vital basis for various functional processes, such as perception, motor control, and cognition. Understanding interneuronal connectivity and how activity is regulated in the neuronal circuits is crucial for interpreting how the brain works. Multi-electrode arrays (MEAs) are particularly useful for studying the dynamics of neuronal network activity and their development as they allow for real-time, high-throughput measurements of neural activity. At present, the key challenge in the utilization of MEA data is the sheer complexity of the measured datasets. Available software offers semi-automated analysis for a fixed set of parameters that allow for the definition of spikes, bursts and network bursts. However, this analysis remains time-consuming, user-biased, and limited by pre-defined parameters. Here, we present autoMEA, software for machine learning-based automated burst detection in MEA datasets. We exemplify autoMEA efficacy on neuronal network activity of primary hippocampal neurons from wild-type mice monitored using 24-well multi-well MEA plates. To validate and benchmark the software, we showcase its application using wild-type neuronal networks and two different neuronal networks modeling neurodevelopmental disorders to assess network phenotype detection. Detection of network characteristics typically reported in literature, such as synchronicity and rhythmicity, could be accurately detected compared to manual analysis using the autoMEA software. Additionally, autoMEA could detect reverberations, a more complex burst dynamic present in hippocampal cultures. Furthermore, autoMEA burst detection was sufficiently sensitive to detect changes in the synchronicity and rhythmicity of networks modeling neurodevelopmental disorders as well as detecting changes in their network burst dynamics. Thus, we show that autoMEA reliably analyses neural networks measured with the multi-well MEA setup with the precision and accuracy compared to that of a human expert.

Circuits and mechanisms for TMS-induced corticospinal waves: Connecting sensitivity analysis to the network graph.

Transcranial magnetic stimulation (TMS) is a non-invasive, FDA-cleared treatment for neuropsychiatric disorders with broad potential for new applications, but the neural circuits that are engaged during TMS are still poorly understood. Recordings of neural activity from the corticospinal tract provide a direct readout of the response of motor cortex to TMS, and therefore a new opportunity to model neural circuit dynamics. The study goal was to use epidural recordings from the cervical spine of human subjects to develop a computational model of a motor cortical macrocolumn through which the mechanisms underlying the response to TMS, including direct and indirect waves, could be investigated. An in-depth sensitivity analysis was conducted to identify important pathways, and machine learning was used to identify common circuit features among these pathways. Sensitivity analysis identified neuron types that preferentially contributed to single corticospinal waves. Single wave preference could be predicted using the average connection probability of all possible paths between the activated neuron type and L5 pyramidal tract neurons (PTNs). For these activations, the total conduction delay of the shortest path to L5 PTNs determined the latency of the corticospinal wave. Finally, there were multiple neuron type activations that could preferentially modulate a particular corticospinal wave. The results support the hypothesis that different pathways of circuit activation contribute to different corticospinal waves with participation of both excitatory and inhibitory neurons. Moreover, activation of both afferents to the motor cortex as well as specific neuron types within the motor cortex initiated different I-waves, and the results were interpreted to propose the cortical origins of afferents that may give rise to certain I-waves. The methodology provides a workflow for performing computationally tractable sensitivity analyses on complex models and relating the results to the network structure to both identify and understand mechanisms underlying the response to acute stimulation.

Effectiveness of unilateral lower-limb exoskeleton robot on balance and gait recovery and neuroplasticity in patients with subacute stroke: a randomized controlled trial

BackgroundImpaired balance and gait in stroke survivors are associated with decreased functional independence. This study aimed to evaluate the effectiveness of unilateral lower-limb exoskeleton robot-assisted overground gait training compared with conventional treatment and to explore the relationship between neuroplastic changes and motor function recovery in subacute stroke patients.MethodsIn this randomized, single-blind clinical trial, 40 patients with subacute stroke were recruited and randomly assigned to either a robot-assisted training (RT) group or a conventional training (CT) group. All outcome measures were assessed at the enrollment baseline (T0), 2nd week (T1) and 4th week (T2) of the treatment. The primary outcome was the between-group difference in the change in the Berg balance scale (BBS) score from baseline to T2. The secondary measures included longitudinal changes in the Fugl-Meyer assessment of the lower limb (FMA-LE), modified Barthel index (mBI), functional ambulation category (FAC), and locomotion assessment with gait analysis. In addition, the cortical activation pattern related to robot-assisted training was measured before and after intervention via functional near-infrared spectroscopy.ResultsA total of 30 patients with complete data were included in this study. Clinical outcomes improved after 4 weeks of training in both groups, with significantly better BBS (F = 6.341, p = 0.018, partial η2 = 0.185), FMA-LE (F = 5.979, p = 0.021, partial η2 = 0.176), FAC (F = 7.692, p = 0.010, partial η2 = 0.216), and mBI scores (F = 7.255, p = 0.042, partial η2 = 0.140) in the RT group than in the CT group. Both groups showed significant improvement in gait speed and stride cadence on the locomotion assessment. Only the RT group presented a significantly increased stride length (F = 4.913, p = 0.015, partial η2 = 0.267), support phase (F = 5.335, p = 0.011, partial η2 = 0.283), and toe-off angle (F = 3.829, p = 0.035, partial η2 = 0.228) on the affected side after the intervention. The RT group also showed increased neural activity response over the ipsilesional motor area and bilateral prefrontal cortex during robot-assisted weight-shift and gait training following 4 weeks of treatment.ConclusionsOverground gait training with a unilateral exoskeleton robot showed improvements in balance and gait functions, resulting in better gait patterns and increased gait stability for stroke patients. The increased cortical response related to the ipsilesional motor areas and their related functional network is crucial in the rehabilitation of lower limb gait in post-stroke patients.

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses.

The ready availability of brain connectome data has both inspired and facilitated the modelling of whole brain activity using networks of phenomenological neural mass models that can incorporate both interaction strength and tract length between brain regions. Recently, a new class of neural mass model has been developed from an exact mean field reduction of a network of spiking cortical cell models with a biophysically realistic model of the chemical synapse. Moreover, this new population dynamics model can naturally incorporate electrical synapses. Here we demonstrate the ability of this new modelling framework, when combined with data from the Human Connectome Project, to generate patterns of functional connectivity (FC) of the type observed in both magnetoencephalography and functional magnetic resonance neuroimaging. Some limited explanatory power is obtained via an eigenmode description of frequency-specific FC patterns, obtained via a linear stability analysis of the network steady state in the neigbourhood of a Hopf bifurcation. However, direct numerical simulations show that empirical data is more faithfully recapitulated in the nonlinear regime, and exposes a key role of gap junction coupling strength in generating empirically-observed neural activity, and associated FC patterns and their evolution. Thereby, we emphasise the importance of maintaining known links with biological reality when developing multi-scale models of brain dynamics. As a tool for the study of dynamic whole brain models of the type presented here we further provide a suite of C++ codes for the efficient, and user friendly, simulation of neural mass networks with multiple delayed interactions.