Brain dynamics of post-task resting state are influenced by expertise: Insights from baseball players

Graph Theoretical Analysis of Brain Structural Connectivity in Patients with Alcohol Dependence

This study aimed to compare brain structural connectivity using graph theory between patients with alcohol dependence and social drinkers. The participants were divided into two groups; the alcohol group (N=23) consisting of patients who had been hospitalized and had abstained from alcohol for at least three months and the control group (N=22) recruited through advertisements and were social drinkers. All participants were evaluated using 3T magnetic resonance imaging. A total of 1000 repeated whole-brain tractographies with random parameters were performed using DSI Studio. Four hundred functionally defined cortical regions of interest (ROIs) were parcellated using FreeSurfer based on the Schaefer Atlas. The ROIs were overlaid on the tractography results to generate 1000 structural connectivity matrices per person, and 1000 matrices were averaged into a single matrix per subject. Graph analysis was performed through igraph R package. Graph measures were compared between the two groups using analysis of covariance, considering the effects of age and smoking pack years. The alcohol group showed lower local efficiency than the control group in the whole-brain (F=5.824, p=0.020), somato-motor (F=5.963, p=0.019), and default mode networks (F=4.422, p=0.042). The alcohol group showed a lower global efficiency (F=5.736, p=0.021) in the control network. The transitivity of the alcohol group in the dorsal attention network was higher than that of the control (F=4.257, p=0.046). Our results imply that structural stability of the whole-brain network is affected in patients with alcohol dependence, which can lead to ineffective information processing in cases of local node failure.

Beta-band differences in primary motor cortex between media and non-media professionals when watching motor actions in movies

To watch a person doing an activity has an impact on the viewer. In fact, the film industry hinges on viewers looking at characters doing all sorts of narrative activities. From previous works, we know that media and non-media professionals perceive differently audiovisuals with cuts. Media professionals present a lower eye-blink rate, a lower activity in frontal and central cortical areas, and a more organized functional brain connectivity when watching audiovisual cuts. Here, we aimed to determine how audiovisuals with no formal interruptions such as cuts were perceived by media and non-media professionals. Moreover, we wondered how motor actions of characters in films would have an impact on the brain activities of the two groups of observers. We presented a narrative with 24 motor actions in a one-shot movie in wide shot with no cuts to 40 participants. We recorded the electroencephalographic (EEG) activity of the participants and analyzed it for the periods corresponding to the 24 motor actions (24 actions × 40 participants = 960 potential trials). In accordance with collected results, we observed differences in the EEG activity of the left primary motor cortex. A spectral analysis of recorded EEG traces indicated the presence of significant differences in the beta band between the two groups after the onset of the motor activities, while no such differences were found in the alpha band. We concluded that media expertise is related with the beta band identified in the EEG activity of the left primary motor cortex and the observation of motor actions in videos.

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the "where and when") and then allow for empirical testing of alternative network models of brain function that link information to behavior (the "how"). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach-dynamic activity flow modeling-then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

Decoding motor expertise from fine-tuned oscillatory network organization

Can motor expertise be robustly predicted by the organization of frequency-specific oscillatory brain networks? To answer this question, we recorded high-density electroencephalography (EEG) in expert Tango dancers and naïves while viewing and judging the correctness of Tango-specific movements and during resting. We calculated task-related and resting-state connectivity at different frequency-bands capturing task performance (delta [δ], 1.5-4 Hz), error monitoring (theta [θ], 4-8 Hz), and sensorimotor experience (mu [μ], 8-13 Hz), and derived topographical features using graph analysis. These features, together with canonical expertise measures (i.e., performance in action discrimination, time spent dancing Tango), were fed into a data-driven computational learning analysis to test whether behavioral and brain signatures robustly classified individuals depending on their expertise level. Unsurprisingly, behavioral measures showed optimal classification (100%) between dancers and naïves. When considering brain models, the task-based classification performed well (~73%), with maximal discrimination afforded by theta-band connectivity, a hallmark signature of error processing. Interestingly, mu connectivity during rest outperformed (100%) the task-based approach, matching the optimal classification of behavioral measures and thus emerging as a potential trait-like marker of sensorimotor network tuning by intense training. Overall, our findings underscore the power of fine-tuned oscillatory network signatures for capturing expertise-related differences and their potential value in the neuroprognosis of learning outcomes.

Functional networks in non-human primate spinal cord and the effects of injury

Spontaneous fluctuations of Blood Oxygenation-Level Dependent (BOLD) MRI signal in a resting state have previously been detected and analyzed to describe intrinsic functional networks in the spinal cord of rodents, non-human primates and human subjects. In this study we combined high resolution imaging at high field with data-driven Independent Component Analysis (ICA) to i) delineate fine-scale functional networks within and between segments of the cervical spinal cord of monkeys, and also to ii) characterize the longitudinal effects of a unilateral dorsal column injury on these networks. Seven distinct functional hubs were revealed within each spinal segment, with new hubs detected at bilateral intermediate and gray commissure regions in addition to the bilateral dorsal and ventral horns previously reported. Pair-wise correlations revealed significantly stronger connections between hubs on the dominant hand side. Unilateral dorsal-column injuries disrupted predominantly inter-segmental rather than intra-segmental functional connectivities as revealed by correlation strengths and graph-theory based community structures. The effects of injury on inter-segmental connectivity were evident along the length of the cord both below and above the lesion region. Connectivity strengths recovered over time and there was revival of inter-segmental communities as animals recovered function. BOLD signals of frequency 0.01-0.033 Hz were found to be most affected by injury. The results in this study provide new insights into the intrinsic functional architecture of spinal cord and underscore the potential of functional connectivity measures to characterize changes in networks after an injury and during recovery.

Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.

Topical Review: Perceptual-cognitive Skills, Methods, and Skill-based Comparisons in Interceptive Sports

SIGNIFICANCE

We give a comprehensive picture of perceptual-cognitive (PC) skills that could contribute to performance in interceptive sports. Both visual skills that are low level and unlikely influenced by experience and higher-level cognitive-attentional skills are considered, informing practitioners for identification and training and alerting researchers to gaps in the literature.Perceptual-cognitive skills and abilities are keys to success in interceptive sports. The interest in identifying which skills and abilities underpin success and hence should be selected and developed is likely going to grow as technologies for skill testing and training continue to advance. Many different methods and measures have been applied to the study of PC skills in the research laboratory and in the field, and research findings across studies have often been inconsistent. In this article, we provide definitional clarity regarding whether a skill is primarily visual attentional (ranging from fundamental/low-level skills to high-level skills) or cognitive. We review those skills that have been studied using sport-specific stimuli or tests, such as postural cue anticipation in baseball, as well as those that are mostly devoid of sport context, considered general skills, such as dynamic visual acuity. In addition to detailing the PC skills and associated methods, we provide an accompanying table of published research since 1995, highlighting studies (for various skills and sports) that have and have not differentiated across skill groups.

Review: The Quiet Eye in Sports Performance-Is the Quiet Eye the Ultimate Explanation or Only the Beginning?

SIGNIFICANCE

The quiet eye is a gaze behavior that seems to differentiate both expert-novice performance and successful and unsuccessful performance in experts; however, the quiet eye may only represent one aspect of the perceptual-cognitive expertise differences that exist between athletes and nonathletes.Research suggests that expert-novice differences in vision and visual-motor coordination skills exist between athletes and nonathletes, although the underlying mechanisms driving these differences are not well understood. The quiet eye is the final fixation or tracking gaze made before the initiation of the action of importance in a motor coordination task and is quite possibly the most well-studied vision strategy variable in sport. The quiet eye has been shown to be a key component of both expert ability and successful performance. However, the quiet eye is not without its criticisms, and the perceptual mechanisms underlying this unique gaze behavior are not yet well understood. The question that remains to be answered is whether the differences in vision and visual-motor coordination skills that exist between athletes and nonathletes can be explained by the quiet eye alone or if the explanation is more complicated.

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI

Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.

The Effect of Media Professionalization on Cognitive Neurodynamics During Audiovisual Cuts

Experts apply their experience to the proper development of their routine activities. Their acquired expertise or professionalization is expected to help in the development of those recurring tasks. Media professionals spend their daily work watching narrative contents on screens, so learning how they manage visual perception of those contents could be of interest in an increasingly audiovisual society. Media works require not only the understanding of the storytelling, but also the decoding of the formal rules and presentations. We recorded electroencephalographic (EEG) signals from 36 participants (18 media professionals and 18 non-media professionals) while they were watching audiovisual contents, and compared their eyeblink rate and their brain activity and connectivity. We found that media professionals decreased their blink rate after the cuts, suggesting that they can better manage the loss of visual information that blinks entail by sparing them when new visual information is being presented. Cuts triggered similar activation of basic brain processing in the visual cortex of the two groups, but different processing in medial and frontal cortical areas, where media professionals showed a lower activity. Effective brain connectivity occurred in a more organized way in media professionals–possibly due to a better communication between cortical areas that are coordinated for decoding new visual content after cuts.

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning

OBJECTIVE

The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs).

METHODS

EEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects.

RESULTS

We show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification.

CONCLUSION

The presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings.

SIGNIFICANCE

We present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG.

Bilingual Language Experience Shapes Resting-State Brain Rhythms

An increasing body of research has investigated how bilingual language experience changes brain structure and function, including changes to task-free, or "resting-state" brain connectivity. Such findings provide important evidence about how the brain continues to be shaped by different language experiences throughout the lifespan. The neural effects of bilingual language experience can provide evidence about the additional processing demands placed on the linguistic and/or executive systems by dual-language use. While considerable research has used MRI to examine where these changes occur, such methods cannot reveal the temporal dynamics of functioning brain networks at rest. The current study used data from task-free EEGS to disentangle how the linguistic and cognitive demands of bilingual language use impact brain functioning. Data analyzed from 106 bilinguals and 91 monolinguals revealed that bilinguals had greater alpha power, and significantly greater and broader coherence in the alpha and beta frequency ranges than monolinguals. Follow-up analyses showed that higher alpha was related to language control: more second-language use, higher native-language proficiency, and earlier age of second-language acquisition. Bilateral beta power was related to native-language proficiency, whereas theta was related to native-language proficiency only in left-hemisphere electrodes. The results contribute to our understanding of how the linguistic and cognitive requirements of dual-language use shape intrinsic brain activity, and what the broader implications for information processing may be.

Paired Associative Stimulation Rewired: A Novel Paradigm to Modulate Resting-State Intracortical Connectivity

Recent neuroimaging research has demonstrated that resting-state intracortical connectivity (i.e., the shared communication between two brain regions) can serve as a robust predictor of motor performance and learning. Theoretically, direct modulation of resting-state intracortical connectivity within the motor system could then improve motor performance and learning. However, previous neuromodulation techniques such as repetitive Transcranial Magnetic Stimulation may be limited in the capacity to modulate targeted intracortical connectivity. Paired Associative Stimulation (PAS) has shown efficacy in facilitating connectivity primarily between the central and peripheral nervous system based on the neuroplasticity mechanism of Spike Timing Dependent Plasticity. It may therefore be plausible for a reconfigured corticocortical PAS paradigm to modulate resting-state intracortical connectivity using a dual stimulator methodology over specific cortical nodes. However, potential theoretical and technological considerations of such a paradigm first need to be addressed prior to application for the purposes of manipulating motor behavior. We posit a corticocortical PAS paradigm used in conjunction with resting-state electroencephalography to demonstrate efficacy of potentiating motor learning associated resting-state intracortical connectivity within the human brain. Here we provide a precise PAS/EEG experimental design, details on data analysis, recommendations for maintaining scientific rigor, and preliminary proof of principle within a single-subject.

From State-to-Trait Meditation: Reconfiguration of Central Executive and Default Mode Networks

While brain default mode network (DMN) activation in human subjects has been associated with mind wandering, meditation practice has been found to suppress it and to increase psychological well-being. In addition to DMN activity reduction, experienced meditators (EMs) during meditation practice show an increased connectivity between the DMN and the central executive network (CEN).

While brain default mode network (DMN) activation in human subjects has been associated with mind wandering, meditation practice has been found to suppress it and to increase psychological well-being. In addition to DMN activity reduction, experienced meditators (EMs) during meditation practice show an increased connectivity between the DMN and the central executive network (CEN). However, the gradual change between DMN and CEN configuration from pre-meditation, during meditation, and post-meditation is unknown. Here, we investigated the change in DMN and CEN configuration by means of brain activity and functional connectivity (FC) analyses in EMs across three back-to-back functional magnetic resonance imaging (fMRI) scans: pre-meditation baseline (trait), meditation (state), and post-meditation (state-to-trait). Pre-meditation baseline group comparison was also performed between EMs and healthy controls (HCs). Meditation trait was characterized by a significant reduction in activity and FC within DMN and increased anticorrelations between DMN and CEN. Conversely, meditation state and meditation state-to-trait periods showed increased activity and FC within the DMN and between DMN and CEN. However, the latter anticorrelations were only present in EMs with limited practice. The interactions between networks during these states by means of positive diametric activity (PDA) of the fractional amplitude of low-frequency fluctuations (fALFFs) defined as CEN fALFF¯ − DMN fALFF¯ revealed no trait differences but significant increases during meditation state that persisted in meditation state-to-trait. The gradual reconfiguration in DMN and CEN suggest a neural mechanism by which the CEN negatively regulates the DMN and is probably responsible for the long-term trait changes seen in meditators and reported psychological well-being.

The Role of Quiet Eye Timing and Location in the Basketball Three-Point Shot: A New Research Paradigm

We investigated three areas of uncertainty about the role of vision in basketball shooting, the timing of fixations (early, late), the location of fixations (hoop centre, non-centre) and the effect of the defender on performance. We also sought to overcome a limitation of past quiet eye studies that reported only one quiet eye (QE) period prior to a phase of the action. Elite basketball players received the pass and took three-point shots in undefended and defended conditions. Five sequential QE periods were analyzed that were initiated prior to each phase of the shooting action: QE catch, QE arm preparation, QE arm flexion, QE arm extension, and QE ball release. We used a novel design in which the number of hits and misses were held constant by condition, thus leaving the timing and location of QE fixations free to vary across the phases during an equal number of successful and unsuccessful trials. The number of QE fixations accounted for 87% of total fixations. The greatest percent occurred during QE catch (43.6%), followed by QE arm flexion (34.1%), QE arm extension (17.5%) and QE ball release (4.8%). No fixations were found prior to QE arm preparation, due to a saccade made immediately to the target after QE catch. Fixation frequency averaged 2.20 per trial, and 1.25 during the final shooting action, meaning that most participants had time for only one fixation as the shot was taken. Accuracy was enhanced when: (1) an early QE offset occurred prior to the catch, (2) an early saccade was made to the target, (3) a longer QE duration occurred during arm flexion, and (4) QE arm flexion was located on the centre of the hoop, rather than on non-centre locations. Overall, the results provide evidence that vision of the hoop was severely limited during the last phase of the shooting action (QE ball release). The significance of the results is explored in the discussion, along with a QE training program designed to improve three-point shooting. Overall, the results greatly expand the role of the QE in explaining optimal motor performance.

Early Trajectory Prediction in Elite Athletes

Cerebellar plasticity is a critical mechanism for optimal feedback control. While Purkinje cell activity of the oculomotor vermis predicts eye movement speed and direction, more lateral areas of the cerebellum may play a role in more complex tasks, including decision-making. It is still under question how this motor-cognitive functional dichotomy between medial and lateral areas of the cerebellum plays a role in optimal feedback control. Here we show that elite athletes subjected to a trajectory prediction, go/no-go task manifest superior subsecond trajectory prediction accompanied by optimal eye movements and changes in cognitive load dynamics. Moreover, while interacting with the cerebral cortex, both the medial and lateral cerebellar networks are prominently activated during the fast feedback stage of the task, regardless of whether or not a motor response was required for the correct response. Our results show that cortico-cerebellar interactions are widespread during dynamic feedback and that experience can result in superior task-specific decision skills.

Mathematical expertise modulates the architecture of dorsal and cortico-thalamic white matter tracts

To what extent are levels of cognitive expertise reflected in differential structural connectivity of the brain? We addressed this question by analyzing the white matter brain structure of experts (mathematicians) versus non-experts (non-mathematicians) using probabilistic tractography. Having mathematicians and non-mathematicians as participant groups enabled us to directly compare profiles of structural connectivity arising from individual levels of expertise in mathematics. Tracking from functional seed regions activated during the processing of complex arithmetic formulas revealed an involvement of various fiber bundles such the inferior fronto-occipital fascicle, arcuate fasciculus/superior longitudinal fasciculus (AF/SLF), cross-hemispheric connections of frontal lobe areas through the corpus callosum and cortico-subcortical connectivity via the bilateral thalamic radiation. With the aim of investigating expertise-dependent structural connectivity, the streamline density was correlated with the level of expertise, defined by automaticity of processing complex mathematics. The results showed that structural integrity of the AF/SLF was higher in individuals with higher automaticity, while stronger cortico-thalamic connectivity was associated with lower levels of automaticity. Therefore, we suggest that expertise in the domain of mathematics is reflected in plastic changes of the brain's white matter structure, possibly reflecting a general principle of cognitive expertise.

When to collect resting-state data: The influence of odor on post-task resting-state connectivity

The human brain networks at rest represent spontaneous activity that is highly correlated between different brain regions. Previous studies have shown that these resting-state networks are flexible and dynamic, and they can be affected by performance of different types of tasks. Moreover, it has been suggested that the re-activation of a task-related brain network during rest promotes learning and improves the expertise on that task. However, it is still unclear whether the presence of different sensory information in the on-task state affects functional connectivity in subsequent resting-state fMRI even though the perception of the sensory information did not induce significant behavioral effects. To clarify this issue, we compared pre- and post-task resting-state fMRI of two groups of participants performing the same task either with an odor context (ODOR group) or without an odor context (AIR group). Seed-based functional connectivity analyses were performed with orbitofrontal cortex, piriform cortex and working-memory core network as seeds. The results showed that an odor context presented during an encoding task induced significant changes in the functional connectivity only within the olfactory network of the post-task resting-state compared to the same post-task situation without previous odor context. No significant difference in functional connectivity were found for the working-memory core network. This evidence emphasizes how the sensory context, in which a task is performed, is relevant for understanding the observed changes of functional connectivity during rest.

Global brain dynamics during social exclusion predict subsequent behavioral conformity

Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences.

Data-driven brain network models differentiate variability across language tasks

The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavior is far from understood. To investigate the effect of anatomical brain organization on human task performance, we use a data-driven computational modeling approach and explore the functional effects of naturally occurring structural differences in brain networks. We construct personalized brain network models by combining anatomical connectivity estimated from diffusion spectrum imaging of individual subjects with a nonlinear model of brain dynamics. By performing computational experiments in which we measure the excitability of the global brain network and spread of synchronization following a targeted computational stimulation, we quantify how individual variation in the underlying connectivity impacts both local and global brain dynamics. We further relate the computational results to individual variability in the subjects' performance of three language-demanding tasks both before and after transcranial magnetic stimulation to the left-inferior frontal gyrus. Our results show that task performance correlates with either local or global measures of functional activity, depending on the complexity of the task. By emphasizing differences in the underlying structural connectivity, our model serves as a powerful tool to assess individual differences in task performances, to dissociate the effect of targeted stimulation in tasks that differ in cognitive demand, and to pave the way for the development of personalized therapeutics.

Effect of different spatial normalization approaches on tractography and structural brain networks

To facilitate the comparison of white matter morphologic connectivity across target populations, it is invaluable to map the data to a standardized neuroanatomical space. Here, we evaluated direct streamline normalization (DSN), where the warping was applied directly to the streamlines, with two publically available approaches that spatially normalize the diffusion data and then reconstruct the streamlines. Prior work has shown that streamlines generated after normalization from reoriented diffusion data do not reliably match the streamlines generated in native space. To test the impact of these different normalization methods on quantitative tractography measures, we compared the reproducibility of the resulting normalized connectivity matrices and network metrics with those originally obtained in native space. The two methods that reconstruct streamlines after normalization led to significant differences in network metrics with large to huge standardized effect sizes, reflecting a dramatic alteration of the same subject’s native connectivity. In contrast, after normalizing with DSN we found no significant difference in network metrics compared with native space with only very small-to-small standardized effect sizes. DSN readily outperformed the other methods at preserving native space connectivity and introduced novel opportunities to define connectome networks without relying on gray matter parcellations.

Direct streamline normalization (DSN) directly warps the streamlines into any template space by using the transformations output from Advanced Normalization Tools (ANTs). DSN overcomes the limitations of diffusion weighted images (DWI) spatial normalization. It allows DWIs to be acquired with any desired sampling scheme. Fiber orientation distributions (FODs) or orientation distribution functions (ODFs) can also be reconstructed using any desired method and streamlines generated using any algorithm. Most importantly, it avoids the problem of generating tracts from FODs or ODFs that have become distorted because of spatial normalization. Our results show that DSN has minimal influence on tractography measures such as tract count and structure and does not significantly alter structural networks with only very small to small effect sizes. We have developed a framework in Python that works with most diffusion software platforms. It is available at http://github.com/clintg6/DSN.

Nonlinear neuroplasticity corresponding to sports experience: A voxel-based morphometry and resting-state functional connectivity study

We aimed to investigate the structural neuroplasticity associated with different levels of sports experience and its effect on the corresponding resting-state functional circuitry. We recruited 18 skilled baseball batters (SB), 19 intermediate baseball batters (IB), and 17 healthy controls (HC), and used magnetic resonance imaging methods to compare their regional gray-matter volume (GMV) and seed-based resting-state functional connectivity (rsFC). Our results revealed that a quadratic function could better depict intergroup differences in regional GMV than a linear function. In particular, the IB showed lower or higher regional GMV than the other two groups. The difference in GMV in the supplementary motor area and areas belonging to the ventral stream, including the middle temporal gyrus and middle temporal pole, might be possibly related to baseball-specific motor and perceptual experience, such as inhibitory action control and pitch identification. On the other hand, the stronger rsFC seeded from the right middle temporal pole to the default mode network, particularly in the precuneus, in the SB and IB relative to that in the HC might be possibly associated with the theory of mind, such as deciding whether to swing or not against the pitcher by detecting the spatial information of pitches. In conclusion, our three-group design enabled the capture of the unique and transient changes that occur during the intermediate phase of expertise development. Our findings indicated that structural and functional brain changes do not necessarily linearly increase as a function of experience as previously suggested by the literature.

A New Analysis of Resting State Connectivity and Graph Theory Reveals Distinctive Short-Term Modulations due to Whisker Stimulation in Rats

Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short-term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions.

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

Local connectome phenotypes predict social, health, and cognitive factors

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.

The local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel, and it reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This intersubject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how the local connectome has both the sensitivity and the specificity to be used as a phenotypic marker for subject-specific attributes.