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Anticipating the behaviour of moving objects in the physical environment is essential for a wide range of daily actions. This ability is thought to rely on mental simulations and has been shown to involve frontoparietal and early visual areas. Yet, the connectivity patterns between these regions during intuitive physical inference remain largely unknown. In this study, participants underwent fMRI while performing a task requiring them to infer the parabolic trajectory of an occluded ball falling under Newtonian physics, and a control task. Building on our previous research showing that when solving the physical inference task, early visual areas encode task-specific and perception-like information about the inferred trajectory, the present study aimed to (i) identify regions that are functionally coupled with early visual areas during the physical inference task, and (ii) investigate changes in effective connectivity within this network of regions. We found that early visual areas are functionally connected to a set of parietal and premotor regions when inferring occluded trajectories. Using dynamic causal modelling, we show that predicting occluded trajectories is associated with changes in effective connectivity within a parieto-premotor network, which may drive internally generated early visual activity in a top-down fashion. These findings offer new insights into the interaction between early visual and frontoparietal regions during physical inference, contributing to our understanding of the neural mechanisms underlying the ability to predict physical outcomes., (© 2024. The Author(s).)

Understanding how the brain's macroscale dynamics are shaped by underlying microscale mechanisms is a key problem in neuroscience. In animal models, we can now investigate this relationship in unprecedented detail by directly manipulating cellular-level properties while measuring the whole-brain response using resting-state fMRI. Here, we focused on understanding how blood-oxygen-level-dependent (BOLD) dynamics, measured within a structurally well-defined striato-thalamo-cortical circuit in mice, are shaped by chemogenetically exciting or inhibiting D1 medium spiny neurons (MSNs) of the right dorsomedial caudate putamen (CPdm). We characterize changes in both the BOLD dynamics of individual cortical and subcortical brain areas, and patterns of inter-regional coupling (functional connectivity) between pairs of areas. Using a classification approach based on a large and diverse set of time-series properties, we found that CPdm neuromodulation alters BOLD dynamics within thalamic subregions that project back to dorsomedial striatum. In the cortex, changes in local dynamics were strongest in unimodal regions (which process information from a single sensory modality) and weakened along a hierarchical gradient towards transmodal regions. In contrast, a decrease in functional connectivity was observed only for cortico-striatal connections after D1 excitation. Our results show that targeted cellular-level manipulations affect local BOLD dynamics at the macroscale, such as by making BOLD dynamics more predictable over time by increasing its self-correlation structure. This contributes to ongoing attempts to understand the influence of structure-function relationships in shaping inter-regional communication at subcortical and cortical levels., Competing Interests: MM, OS, JB, MR, VZ, BF, NW No competing interests declared, (© 2023, Markicevic et al.)

With the greying population, it is increasingly necessary to establish robust and individualized markers of cognitive decline. This requires the combination of well-established neural mechanisms, and the development of increasingly sensitive methodologies. The P300 event-related potential (ERP) has been one of the most heavily investigated neural markers of attention and cognition, and studies have reliably shown that changes in the amplitude and latency of the P300 ERP index the process of aging. However, it is still not clear whether either the P3a or P3b sub-components additionally index levels of cognitive impairment. Here, we used a traditional visual three-stimulus oddball paradigm to investigate both the P3a and P3b ERP components in sixteen young and thirty-four healthy elderly individuals with varying degrees of cognitive ability. EEG data extraction was enhanced through the use of a novel signal processing method called Functional Source Separation (FSS) that increases signal-to-noise ratio by using a weighted sum of all electrodes rather than relying on a single, or a small sub-set, of EEG channels. Whilst clear differences in both the P3a and P3b ERPs were seen between young and elderly groups, only P3b amplitude differentiated older people with low memory performance relative to IQ from those with consistent memory and IQ. A machine learning analysis showed that P3b amplitude (derived from FSS analysis) could accurately categorise high and low performing elderly individuals (78% accuracy). A comparison of Bayes Factors found that differences in cognitive decline within the elderly group were 87 times more likely to be detected using FSS compared to the best performing single electrode (Cz). In conclusion, we propose that P3b amplitude could be a sensitive marker of early, age-independent, episodic memory dysfunction within a healthy older population. In addition, we advocate for the use of more advanced signal processing methods, such as FSS, for detecting subtle neural changes in clinical populations., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Autism Spectrum Disorder (ASD) has been associated with abnormal synaptic development causing a breakdown in functional connectivity. However, when measured at the macro scale using resting state fMRI, these alterations are subtle and often difficult to detect due to the large heterogeneity of the pathology. Recently, we outlined a novel approach for generating robust biomarkers of resting state functional magnetic resonance imaging (RS-fMRI) using connectivity based parcellation of gross morphological structures to improve single-subject reproducibility and generate more robust connectivity fingerprints. Here we apply this novel approach to investigating the organization and connectivity strength of the cortico-striatal system in a large sample of ASD individuals and typically developed (TD) controls (N=130 per group). Our results showed differences in the parcellation of the striatum in ASD. Specifically, the putamen was found to be one single structure in ASD, whereas this was split into anterior and posterior segments in an age, IQ, and head movement matched TD group. An analysis of the connectivity fingerprints revealed that the group differences in clustering were driven by differential connectivity between striatum and the supplementary motor area, posterior cingulate cortex, and posterior insula. Our approach for analysing RS-fMRI in clinical populations has provided clear evidence that cortico-striatal circuits are organized differently in ASD. Based on previous task-based segmentations of the striatum, we believe that the anterior putamen cluster present in TD, but not in ASD, likely contributes to social and language processes., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

The superior temporal sulcus (STS) forms a key region for social information processing and disruptions of its function have been associated with socio-communicative impairments characteristic of autism spectrum disorders (ASD). Task-based fMRI was applied in 15 adults with ASD and 15 matched typical-controls (TC) to explore differences in activity and effective connectivity of STS while discriminating either 'intact' versus 'scrambled' biological motion point light displays (explicit processing) or responding to a color-change while the 'intact' versus 'scrambled' nature of the stimulus was irrelevant for the task (implicit processing). STS responded stronger to 'intact' than 'scrambled' stimuli in both groups, indicating that the basic encoding of 'biological' versus 'non-biological' motion seems to be intact in ASD. Only in the TC-group however, explicit attention to the biological motion content induced an augmentation of STS-activity, which was not observed in the ASD-group. Overall, these findings suggest an inadequacy to recruit STS upon task demand in ASD, rather than a generalized alteration in STS neural processing. The importance of attention orienting for recruiting relevant neural resources was further underlined by the observation that connectivity between STS and medial prefrontal cortex (mPFC), a key region in attention regulation, effectively modulated STS-recruitment in the ASD-group.

Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Although resting state fMRI (RS-fMRI) is increasingly used to generate biomarkers of psychiatric illnesses, analytical choices such as seed size and placement can lead to variable findings. Seed placement especially impacts on RS-fMRI studies of Autism Spectrum Disorder (ASD), because individuals with ASD are known to possess more variable network topographies. Here, we present a novel pipeline for analysing RS-fMRI in ASD using the cingulate cortex as an exemplar anatomical region of interest. Rather than using seeds based on previous literature, or gross morphology, we used a combination of structural information, task-independent (RS-fMRI) and task-dependent functional connectivity (Meta-Analytic Connectivity Modeling) to partition the cingulate cortex into six subregions with unique connectivity fingerprints and diverse behavioural profiles. This parcellation was consistent between groups and highly replicable across individuals (up to 93% detection) suggesting that the organisation of cortico-cingulo connections is highly similar between groups. However, our results showed an age-related increase in connectivity between the anterior middle cingulate cortex and right lateral prefrontal cortex in ASD, whilst this connectivity decreased in controls. There was also a Group × Grey Matter (GM) interaction, showing increased connectivity between the anterior cingulate cortex and the rectal gyrus in concert with increasing rectal gyrus GM in controls. By comparing our approach to previously established methods we revealed that our approach improves network detection in both groups, and that the ability to detect group differences using 4 mm radius spheres varies greatly with seed placement. Using our multi-modal approach we find disrupted cortico-cingulo circuits that, based on task-dependent information, may contribute to ASD deficits in attention and social interaction. Moreover, we highlight how more sensitive approaches to RS-fMRI are crucial for establishing robust and reproducible connectivity-based biomarkers in psychiatric disorders.

The use of resting state fMRI (rs-fMRI) in translational research is a powerful tool to assess brain connectivity and investigate neuropathology in mouse models. However, despite encouraging initial results, the characterization of consistent and robust resting state networks in mice remains a methodological challenge. One key reason is that the quality of the measured MR signal is degraded by the presence of structural noise from non-neural sources. Notably, in the current pipeline of the Human Connectome Project, a novel approach has been introduced to clean rs-fMRI data, which involves automatic artifact component classification and data cleaning (FIX). FIX does not require any external recordings of physiology or the segmentation of CSF and white matter. In this study, we evaluated the performance of FIX for analyzing mouse rs-fMRI data. Our results showed that FIX can be easily applied to mouse datasets and detects true signals with 100% accuracy and true noise components with very high accuracy (>98%), thus reducing both within- and between-subject variability of rs-fMRI connectivity measurements. Using this improved pre-processing pipeline, maps of 23 resting state circuits in mice were identified including two networks that displayed default mode network-like topography. Hierarchical clustering grouped these neural networks into meaningful larger functional circuits. These mouse resting state networks, which are publicly available, might serve as a reference for future work using mouse models of neurological disorders., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Diffusion weighted imaging (DWI) studies in humans have shown that seniors exhibit reduced white matter integrity compared with young adults, with the most pronounced change occurring in frontal white matter. It is generally assumed that this structural deterioration underlies inhibitory control deficits in old age, but specific evidence from a structural neuroscience perspective is lacking. Cognitive action control is thought to rely on an interconnected network consisting of right inferior frontal cortex (r-IFC), pre-supplementary motor area (preSMA), and the subthalamic nucleus (STN). Here we performed probabilistic DWI tractography to delineate this cognitive control network and had the same individuals (20 young, 20 older adults) perform a task probing both response inhibition and action reprogramming. We hypothesized that structural integrity (fractional anisotropy) and connection strength within this network would be predictive of individual and age-related differences in task performance. We show that the integrity of r-IFC white matter is an age-independent predictor of stop-signal reaction time (SSRT). We further provide evidence that the integrity of white matter projecting to STN predicts both outright stopping (SSRT) and transient braking of response initiation to buy time for action reprogramming (stopping interference effects). These associations remain even after controlling for Go task performance, demonstrating specificity to the Stop component of this task. Finally, a multiple regression analysis reveals bilateral preSMA-STN tract strength as a significant predictor of SSRT in older adults. Our data link age-related decline in inhibitory control with structural decline of STN projections.

Our sense of body position and movement independent of vision (i.e., proprioception) relies on muscle spindle feedback and is vital for performing motor acts. In this study, we first sought to elucidate age-related differences in the central processing of proprioceptive information by stimulating foot muscle spindles and by measuring neural activation with functional magnetic resonance imaging. We found that healthy older adults activated a similar, distributed network of primary somatosensory and secondary-associative cortical brain regions as young individuals during the vibration-induced muscle spindle stimulation. A significant decrease in neural activity was also found in a cluster of right putamen voxels for the older age group when compared with the younger age group. Given these differences, we performed two additional analyses within each group that quantified the degree to which age-dependent activity was related to (1) brain structure and (2) a behavioral measure of proprioceptive ability. Using diffusion tensor imaging, older (but not younger) adults with higher mean fractional anisotropy were found to have increased right putamen neural activity. Age-dependent right putamen activity seen during tendon vibration was also correlated with a behavioral test of proprioceptive ability measuring ankle joint position sense in both young and old age groups. Partial correlation tests determined that the relationship between elderly joint position sense and neural activity in right putamen was mediated by brain structure, but not vice versa. These results suggest that structural differences within the right putamen are related to reduced activation in the elderly and potentially serve as biomarker of proprioceptive sensibility in older adults., (Copyright © 2011 Wiley Periodicals, Inc.)

Proprioceptive information from the foot/ankle provides important information regarding body sway for balance control, especially in situations where visual information is degraded or absent. Given known increases in catastrophic injury due to falls with older age, understanding the neural basis of proprioceptive processing for balance control is particularly important for older adults. In the present study, we linked neural activity in response to stimulation of key foot proprioceptors (i.e., muscle spindles) with balance ability across the lifespan. Twenty young and 20 older human adults underwent proprioceptive mapping; foot tendon vibration was compared with vibration of a nearby bone in an fMRI environment to determine regions of the brain that were active in response to muscle spindle stimulation. Several body sway metrics were also calculated for the same participants on an eyes-closed balance task. Based on regression analyses, multiple clusters of voxels were identified showing a significant relationship between muscle spindle stimulation-induced neural activity and maximum center of pressure excursion in the anterior-posterior direction. In this case, increased activation was associated with greater balance performance in parietal, frontal, and insular cortical areas, as well as structures within the basal ganglia. These correlated regions were age- and foot-stimulation side-independent and largely localized to right-sided areas of the brain thought to be involved in monitoring stimulus-driven shifts of attention. These findings support the notion that, beyond fundamental peripheral reflex mechanisms, central processing of proprioceptive signals from the foot is critical for balance control.

Sensory information is critical to correct performance errors online during the execution of complex tasks and can be complemented by augmented feedback (FB). Here, 2 groups of participants acquired a new bimanual coordination pattern under different augmented FB conditions: 1) visual input reflecting coordination between the 2 hands and 2) auditory pacing integrating the timing of both hands into a single temporal structure. Behavioral findings revealed that the visual group became dependent on this augmented FB for performance, whereas the auditory group performed equally well with or without augmented FB by the end of practice. Functional magnetic resonance imaging (fMRI) results corroborated these behavioral findings: the visual group showed neural activity increases in sensory-specific areas during practice, supporting increased reliance on augmented FB. Conversely, the auditory group showed a neural activity decrease, specifically in areas associated with cognitive/sensory monitoring of motor task performance, supporting the development of a control mode that was less reliant on augmented FB sources. Finally, some remnants of brain activity in sensory-specific areas in the absence of augmented FB were found for the visual group only, illustrating ongoing reliance on these areas. These findings provide the first neural account for the "guidance hypothesis of information FB," extensively supported by behavioral research.

Coordinated hand use is an essential component of many activities of daily living. Although previous studies have demonstrated age-related behavioral deficits in bimanual tasks, studies that assessed the neural basis underlying such declines in function do not exist. In this fMRI study, 16 old and 16 young healthy adults performed bimanual movements varying in coordination complexity (i.e., in-phase, antiphase) and movement frequency (i.e., 45, 60, 75, 90% of critical antiphase speed) demands. Difficulty was normalized on an individual subject basis leading to group performances (measured by phase accuracy/stability) that were matched for young and old subjects. Despite lower overall movement frequency, the old group "overactivated" brain areas compared with the young adults. These regions included the supplementary motor area, higher order feedback processing areas, and regions typically ascribed to cognitive functions (e.g., inferior parietal cortex/dorsolateral prefrontal cortex). Further, age-related increases in activity in the supplementary motor area and left secondary somatosensory cortex showed positive correlations with coordinative ability in the more complex antiphase task, suggesting a compensation mechanism. Lastly, for both old and young subjects, similar modulation of neural activity was seen with increased movement frequency. Overall, these findings demonstrate for the first time that bimanual movements require greater neural resources for old adults in order to match the level of performance seen in younger subjects. Nevertheless, this increase in neural activity does not preclude frequency-induced neural modulations as a function of increased task demand in the elderly., (2010 Wiley-Liss, Inc.)

Here we review behavioral and brain imaging results on stimulus-response selection in the context of bimanual movements, which is a prototypical paradigm frequently used to investigate the coordination of complex motor behavior. We propose that stimulus-response selection is constrained at the motor, perceptual, and cognitive levels, with the relative importance of each dependent on the task context. Motor constraints seem to dominate when response selection requirements are low, whereas perceptual and cognitive constraints become increasingly important when the appropriate movement has to be associated with a visual cue. We argue that certain cue features determine how task goals are conceptualized, which influences how a particular motor response is selected and implemented by the nervous system.

Intensive practice of a new complex motor skill results in progressive improvement of performance. This induces neuroplastic changes, reflecting the transition from attention-demanding to more automatic performance throughout the learning. In the present fMRI study, learning-related cerebral activation changes during the acquisition of a new complex bimanual coordination pattern were examined, i.e., the 90 degrees out-of-phase pattern (90Phi). Furthermore, we investigated whether practice of this new pattern influenced the neural correlates associated with performance of a preferred intrinsic pattern. Twelve young healthy subjects were intensively trained on the 90Phi task, and underwent two fMRI scanning sessions in early (PRE) and late (POST) learning. Scanning sessions included performance of the trained 90Phi pattern, as well as the nontrained intrinsic in-phase pattern (InPhi). Kinematics registered during training and scanning experiments showed that the new 90Phi pattern was acquired successfully, resulting in learning-related brain activation changes. Activation decreases were observed in the right prefrontal cortex (DLPFC and dorsal premotor), in the right middle temporal and occipital cortices and in the posterior cerebellum. Conversely, increases were found in the basal ganglia and hippocampus. Interestingly, activity elicited by the InPhi task also evidenced within-subjects PRE/POST differences (although kinematics InPhi performance was equivalent in both sessions). In particular, the learning-related decreases found for the 90Phi pattern in the cerebellum, the occipital and temporal gyri were similarly observed for the intrinsic InPhi pattern. Moreover, InPhi performance induced PRE/POST increases of activity in the left superior frontal gyrus. Our fMRI results suggest that intensive practice of a new complex coordination pattern impacted, at least temporarily, on the neural correlates of preferred intrinsic coordination patterns. Additional neural recruitment might reflect increased mental effort to prevent negative transfer from the learned mode onto the intrinsic coordination mode.

Although functional imaging studies have frequently examined age-related changes in neural recruitment during cognitive tasks, much less is known about such changes during motor performance. In the present study, we used functional magnetic resonance imaging to investigate age-related changes in cyclical hand and/or foot movements across different degrees of complexity. Right-handed volunteers (11 young, 10 old) were scanned while performing isolated flexion-extension movements of the right wrist and foot as well as their coordination, according to the "easy" isodirectional and "difficult" nonisodirectional mode. Findings revealed activation of a typical motor network in both age groups, but several additional brain areas were involved in the elderly. Regardless of the performed motor task, the elderly exhibited additional activation in areas involved in sensory processing and integration, such as contralateral anterior insula, frontal operculum, superior temporal gyrus, supramarginal gyrus, secondary somatosensory area, and ipsilateral precuneus. Age-related activation differences during coordination of both segments were additionally observed in areas reflecting increased cognitive monitoring of motor performance, such as the pre-supplementary motor area, pre-dorsal premotor area, rostral cingulate, and prefrontal cortex. In the most complex coordination task, the elderly exhibited additional activation in anterior rostral cingulate and dorsolateral prefrontal cortex, known to be involved in suppression of prepotent response tendencies and inhibitory cognitive control. Overall, these findings are indicative of an age-related shift along the continuum from automatic to more controlled processing of movement. This increased cognitive monitoring of movement refers to enhanced attentional deployment, more pronounced processing of sensory information, and intersensory integration.

Little is known about activation changes reflecting overlearning, i.e., extensive motor training beyond asymptotic performance. Here we used functional magnetic resonance imaging to trace the neural shifts from an initial to a skilled (learning) and finally overlearned stage (automatization). Scanning occurred before training (PRE) and after 1 (MID) and 2 weeks (POST) of intensive practice on a new bimanual coordination task (>10,500 cycles). Kinematics revealed major improvements between PRE and MID sessions, whereas MID to POST session performance leveled off, indicative of learning and automatization, respectively. Imaging findings showed that activation decreased in bilateral opercular areas, bilateral ventrolateral prefrontal cortex, the right ventral premotor and supramarginal gyrus, and the anterior cingulate sulcus during the learning stage and in the supplementary motor area during the automatization stage. These changes are hypothesized to reflect decreases in attention-demanding sensory processing, as well as suppression of preferred coordination tendencies as a prelude to acquiring new coordination modes. Conversely, learning-related increases were observed in the primary motor cortex (M1), posterior cingulate zone (PCZ), putamen, and right anterior cerebellum. Importantly, both M1 and PCZ activation decreased again to initial level (PRE) during automated performance (POST). Only the putamen and anterior cerebellum remained more activated across both learning and automatization stages, supporting their crucial role in long-term motor memory formation for coordination tasks.

Autism spectrum disorders (ASD) are a set of complex neurodevelopmental disorders for which there is currently no targeted therapeutic approach. It is thought that alterations of genes regulating migration and synapse formation during development affect neural circuit formation and result in aberrant connectivity within distinct circuits that underlie abnormal behaviors. However, it is unknown whether deviant developmental trajectories are circuit-specific for a given autism risk-gene. We used MRI to probe changes in functional and structural connectivity from childhood to adulthood in Fragile-X (Fmr1-/y) and contactin-associated (CNTNAP2-/-) knockout mice. Young Fmr1-/y mice (30 days postnatal) presented with a robust hypoconnectivity phenotype in corticocortico and corticostriatal circuits in areas associated with sensory information processing, which was maintained until adulthood. Conversely, only small differences in hippocampal and striatal areas were present during early postnatal development in CNTNAP2-/- mice, while major connectivity deficits in prefrontal and limbic pathways developed between adolescence and adulthood. These findings are supported by viral tracing and electron micrograph approaches and define 2 clearly distinct connectivity endophenotypes within the autism spectrum. We conclude that the genetic background of ASD strongly influences which circuits are most affected, the nature of the phenotype, and the developmental time course of the associated changes.

Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance. [ABSTRACT FROM AUTHOR]

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations of brain dynamics ("functional connectivity"), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics in each brain region by computing 6930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag τ=34 s (partial Spearman correlation P = 0:58), as well as a range of related measures such as relative high frequency power (f>0.4Hz: P = -0:43). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations. [ABSTRACT FROM AUTHOR]

Despite recent advancements in MR imaging, non-invasive mapping of myelin in the brain still remains an open issue. Here we attempted to provide a potential solution. Specifically, we developed a processing workflow based on T1-w and T2-w MR data to generate an optimized myelin enhanced contrast image. The workflow allows whole brain mapping using the T1-w/T2-w technique, which was originally introduced as a non-invasive method for assessing cortical myelin content. The hallmark of our approach is a retrospective calibration algorithm, applied to bias-corrected T1-w and T2-w images, that relies on image intensities outside the brain. This permits standardizing the intensity histogram of the ratio image, thereby allowing for across-subject statistical analyses. Quantitative comparisons of image histograms within and across different datasets confirmed the effectiveness of our normalization procedure. Not only did the calibrated T1-w/T2-w images exhibit a comparable intensity range, but also the shape of the intensity histograms was largely corresponding. We also assessed the reliability and specificity of the ratio image compared to other MR-based techniques, such as magnetization transfer ratio (MTR), fractional anisotropy (FA), and fluid-attenuated inversion recovery (FLAIR). With respect to these other techniques, T1-w/T2-w had consistently high values, as well as low inter-subject variability, in brain structures where myelin is most abundant. Overall, our results suggested that the T1-w/T2-w technique may be a valid tool supporting the non-invasive mapping of myelin in the brain. Therefore, it might find important applications in the study of brain development, aging and disease. [ABSTRACT FROM AUTHOR]

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods. [ABSTRACT FROM AUTHOR]