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ObjectiveWe examined whether fatigue in multiple sclerosis (MS) is linked to switching processes when switching is measured by the Trail Making Test (TMT).MethodEighty-three participants with MS were administered a battery of standardized tests of switching, working memory, and processing speed. Ordinary least squares regression models were used to estimate the association between fatigue severity and switching above and beyond attention, working memory, and processing speed.ResultsWe found a negative association between TMT performance and fatigue severity score. When measures of processing speed and working memory were included in the model, the switching measure continued to uniquely contribute to fatigue severity.ConclusionsThere may be a unique relationship between fatigue and switching processes identifiable by clinical measures of switching. Future research should continue to investigate this relationship by using both behavioral and neural markers to test models of fatigue to eventually identify specific intervention targets.

OBJECTIVE: This study examined whether an alteration in the effort-reward relationship, a theoretical framework based in cognitive neuroscience, could explain cognitive fatigue. PARTICIPANTS AND METHODS: Forty persons with MS and 40 healthy age- and education-matched cognitively healthy controls (HC) participated in a computerized switching task with orthogonal high- and low-demand (effort) and reward manipulations. We used the Visual Analog Scale of Fatigue (VAS-F) to assess subjective state fatigue before and after each condition during the task. We used mixed-effects models to estimate the association and interaction between effort and reward and their relationship to subjective fatigue and task performance. RESULTS: We found the high-demand condition was associated with increased VAS-F scores (p

Cognitive control (CC) is a vital component of cognition associated with problem-solving in everyday life. Many neurological and neuropsychiatric conditions have deficits associated with CC. CC is composed of multiple behaviors including switching, inhibiting, and updating. The fronto-parietal control network B (FPCN-B), the dorsal attention network (DAN), the cingulo-opercular network (CON) and the dorsal default-mode network (dorsal-DMN) have been associated with switching and inhibiting behaviors. However, our understanding of how these brain regions interact to bring about CC behaviors is still unclear. In the current study, participants performed two in-scanner tasks that required switching and inhibiting. We then used a series of support vector regression (SVR) models containing individually-estimated functional connectivity between the networks of interest derived during tasks and at rest to predict inhibition and switching behaviors in individual subjects. We observed that the combination of between-network connectivity from these individually estimated functional networks predicted accurate and timely inhibition and switching behaviors in individuals. We also observed that the relationships between canonical task-positive and task-negative networks predicted inhibiting and switching behaviors. Finally, we observed a functional dissociation between the FPCN-A and FPCNB during rest, and task performance predicted inhibiting and switching behaviors. These results suggest that individually estimated networks can predict individual CC behaviors, that between-network functional connectivity estimated within individuals is vital to understanding how CC arises, and that the fractionation of the FPCN and the DMN may be associated with different behaviors than their canonically accepted behaviors.

Aphasia is a prevalent cognitive syndrome caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion obscures the links between the local effects of the lesion, global anatomic network organization, and aphasia symptoms. We applied a simulated attack approach in humans to examine the effects of 39 stroke lesions (16 females) on anatomic network topology by simulating their effects in a control sample of 36 healthy (15 females) brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores [the Western Aphasia Battery Aphasia Quotient (WAB-AQ), four behavioral factor scores obtained from a neuropsychological battery, and a factor sum], we compared the behavioral variance accounted for by simulated poststroke connectomes to that observed in the randomly permuted data. Global measures of anatomic network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures provide modest statistical value beyond lesion volume when predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke. SIGNIFICANCE STATEMENT Poststroke, the remaining neuroanatomy maintains cognition and supports recovery. However, studies often use small, cross-sectional samples that cannot fully model the interactions between lesions and other variables that affect networks in stroke. Alternate methods are required to account for these effects. “Simulated attack” models are computational approaches that apply virtual damage to the brain and measure their putative consequences. Using a simulated attack model, we estimated how simulated damage to anatomic networks could account for language performance. Overall, our results reveal that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. These findings suggest that more theoretically precise network models could be necessary to robustly predict individual outcomes in aphasia.

Scientists, clinicians, and the public aim to optimize cognition using numerous techniques. While drugs and education have become mainstays in the public and professional practice, brain stimulation is rapidly emerging. The public reports some concerns about brain stimulation used to optimize cognition. In our prior work using hypothetical vignettes involving brain stimulation, the moral acceptability of specific uses was only somewhat related to the public’s willingness to administer brain stimulation to influence a given cognitive function. Notably, whether the moral framing influences willingness to optimize cognition with various mechanisms is unknown. Here, we randomly assigned subjects to one of four mechanism vignette conditions: education, drugs, invasive brain stimulation, and noninvasive brain stimulation. Within each mechanism, subjects were assigned to think morally either before or after reporting their willingness to optimize specific cognitive functions in others. Across 1328 subjects, we found that thinking morally reduced the public’s willingness to optimize cognition overall. Moreover, willingness decreased the most in invasive and noninvasive brain stimulation. Encouraging moral reasoning uniquely reduces openness to using brain stimulation among other ways to optimize cognition. Studying how specific moral concerns drive public hesitancy about brain stimulation could suggest avenues for education and ethical discourse.

In the era of "big data", we are gaining rich person-specific information about neuroanatomy, neural function, and cognitive functions. However, the optimal ways to create precise approaches to optimize individuals' mental functions in health and disease are unclear. Multimodal analysis and modeling approaches can guide neuromodulation by combining anatomical networks, functional signal analysis, and cognitive neuroscience paradigms in single subjects. Our progress could be improved by progressing from statistical fits to mechanistic models. Using transcranial magnetic stimulation as an example, we discuss how integrating methods with a focus on mechanisms could improve our predictions TMS effects within individuals, refine our models of health and disease, and improve our treatments.

Face processing supports our ability to recognize friend from foe, form tribes and understand the emotional implications of changes in facial musculature. This skill relies on a distributed network of brain regions, but how these regions interact is poorly understood. Here we integrate anatomical and functional connectivity measurements with behavioural assays to create a global model of the face connectome. We dissect key features, such as the network topology and fibre composition. We propose a neurocognitive model with three core streams; face processing along these streams occurs in a parallel and reciprocal manner. Although long-range fibre paths are important, the face network is dominated by short-range fibres. Finally, we provide evidence that the well-known right lateralization of face processing arises from imbalanced intra- and interhemispheric connections. In summary, the face network relies on dynamic communication across highly structured fibre tracts, enabling coherent face processing that underpins behaviour and cognition.

Transcranial magnetic stimulation (TMS) is used in several FDA-approved treatments and, increasingly, to treat neurological disorders in off-label uses. However, the mechanism by which TMS causes physiological change is unclear, as are the origins of response variability in the general population. Ideally, objective in vivo biomarkers could shed light on these unknowns and eventually inform personalized interventions. Continuous theta burst stimulation (cTBS) is a form of TMS which has been observed to reduce motor evoked potentials (MEPs) for 60 minutes or longer post-stimulation, although the consistency of this effect and its mechanism continue to be under debate. Here, we use glutamate-weighted chemical exchange saturation transfer (gluCEST) magnetic resonance imaging (MRI) at ultra-high magnetic field (7T) to measure changes in glutamate concentration at the site of cTBS. We find that gluCEST signal in the ipsilateral hemisphere of the brain generally decreases in response to cTBS, whereas consistent changes were not detected in the contralateral or in subjects receiving a sham stimulation.One Sentence SummaryWe used glutamate-weighted Chemical Exchange Saturation Transfer (GluCEST) imaging to detect changes in glutamate contrast in the brains of young, healthy adults undergoing transcranial magnetic stimulation (TMS) to the motor cortex.

Aphasia is one of the most prevalent cognitive syndromes caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion size and extent obfuscates the links between the local effects of the lesion, global anatomical network organization, and aphasia symptoms. We applied a simulated attack approach to examine the effects of 39 stroke lesions on network topology by simulating their effects in a control sample of 36 healthy brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores (the Western Aphasia Battery Aphasia Quotient; WAB-AQ), four behavioral factor scores obtained from a neuropsychological battery, and a factor sum), we compared the behavioral variance accounted for by simulated post-stroke connectomes to that observed in the randomly permuted data. Overall, global measures of network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke.

Soft bioelectronic interfaces for mapping and modulating excitable networks at high resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and treatment strategies. Yet, current technologies largely rely on materials and fabrication schemes that are expensive, do not scale, and critically limit the maximum attainable resolution and coverage. Solution processing is a cost-effective manufacturing alternative, but biocompatible conductive inks matching the performance of conventional metals are lacking. Here, we introduce MXtrodes, a class of soft, high-resolution, large-scale bioelectronic interfaces enabled by Ti(3)C(2) MXene (a two-dimensional transition metal carbide nanomaterial) and scalable solution processing. We show that the electrochemical properties of MXtrodes exceed those of conventional materials, and do not require conductive gels when used in epidermal electronics. Furthermore, we validate MXtrodes in applications ranging from mapping large scale neuromuscular networks in humans to cortical neural recording and microstimulation in swine and rodent models. Finally, we demonstrate that MXtrodes are compatible with standard clinical neuroimaging modalities.

Optimal performance in any task relies on the ability to detect and repair errors. The anterior cingulate cortex and the broader posterior medial frontal cortex (pMFC) are active during error processing. However, it is unclear whether damage to the pMFC impairs error monitoring. We hypothesized that successful error monitoring critically relies on connections between the pMFC and broader cortical networks involved in executive functions and the task being monitored. We tested this hypothesis in the context of speech error monitoring in people with post-stroke aphasia. Diffusion weighted images were collected in 51 adults with chronic left-hemisphere stroke and 37 age-matched control participants. Whole-brain connectomes were derived using constrained spherical deconvolution and anatomically-constrained probabilistic tractography. Support vector regressions identified white matter connections in which lost integrity in stroke survivors related to reduced error detection during confrontation naming. Lesioned connections to the bilateral pMFC were related to reduce error monitoring, including many connections to regions associated with speech production and executive function. We conclude that connections to the pMFC support error monitoring. Error monitoring in speech production is supported by the structural connectivity between the pMFC and regions involved in speech production and executive function. Interactions between pMFC and other taskrelevant processors may similarly be critical for error monitoring in other task contexts.

The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain.Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than those observed in the average network of the most reliably observed connections in age-matched controls. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a factor analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using grid-search cross-validated Support Vector Regression, a technique that extracts robust relationships in high-dimensional data analysis.We discovered that the length of bypasses reliably predicted variance in lexical production (R2= .576) and auditory comprehension scores (R2= .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.

Recent work has combined cognitive neuroscience and control theory to make predictions about cognitive control functions. Here, we test a link between whole-brain theories of semantics and the role of the left inferior frontal gyrus (LIFG) in controlled language performance using network control theory (NCT), a branch of systems engineering. Specifically, we examined whether two properties of node controllability, boundary and modal controllability, were linked to semantic selection and retrieval on sentence completion and verb generation tasks. We tested whether the controllability of the left IFG moderated language selection and retrieval costs and the effects of continuous θ burst stimulation (cTBS), an inhibitory form of transcranial magnetic stimulation (TMS) on behavior in 41 human subjects (25 active, 16 sham). We predicted that boundary controllability, a measure of the theoretical ability of a node to integrate and segregate brain networks, would be linked to word selection in the contextually-rich sentence completion task. In contrast, we expected that modal controllability, a measure of the theoretical ability of a node to drive the brain into specifically hard-to-reach states, would be linked to retrieval on the low-context verb generation task. Boundary controllability was linked to selection and to the ability of TMS to reduce response latencies on the sentence completion task. In contrast, modal controllability was not linked to performance on the tasks or TMS effects. Overall, our results suggest a link between the network integrating role of the LIFG and selection and the overall semantic demands of sentence completion.

The provocative paper by Ioannidis (2005) claiming that “most research findings are false” re-ignited longstanding concerns (see Meehl, 1967) that findings in the behavioral sciences are unlikely to be replicated. Then, a landmark paper by Nosek et al. (2015a) substantiated this conjecture, showing that, study reproducibility in psychology hovers at 40%. With the unfortunate failure of clinical trials in brain injury and other neurological disorders, it may be time to reconsider approaches not only in clinical interventions, but also how we establish their efficacy. A scientific community galvanized by a history of failed clinical trials and motivated by this “crisis” may be at critical cross-roads for change engendering a culture of transparent, open science where the primary goal is to test and not support hypotheses about specific interventions. The outcome of this scientific introspection could be a paradigm shift that accelerates our science bringing investigators closer to important advancements in rehabilitation medicine. In this commentary we offer a brief summary of how open science, study pre-registration and reorganization of scientific incentive structure could advance the clinical sciences.

Electrocorticography (ECoG) data can be used to estimate brain-wide connectivity patterns. Yet, the invasiveness of ECoG, incomplete cortical coverage, and variability in electrode placement across individuals make the network analysis of ECoG data challenging. Here, we show that the architecture of whole-brain ECoG networks and the factors that shape it can be studied by analysing whole-brain, interregional and band-limited ECoG networks from a large cohort-in this case, of individuals with medication-resistant epilepsy. Using tools from network science, we characterized the basic organization of ECoG networks, including frequency-specific architecture, segregated modules and the dependence of connection weights on interregional Euclidean distance. We then used linear models to explain variabilities in the connection strengths between pairs of brain regions, and to highlight the joint role, in shaping the brain-wide organization of ECoG networks, of communication along white matter pathways, interregional Euclidean distance and correlated gene expression. Moreover, we extended these models to predict out-of-sample, single-subject data. Our predictive models may have future clinical utility; for example, by anticipating the effect of cortical resection on interregional communication.

The same concept can mean different things or be instantiated in different forms, depending on context, suggesting a degree of flexibility within the conceptual system. We propose that a feature-based network model can be used to capture and predict this flexibility. We modeled individual concepts (e.g., BANANA, BOTTLE) as graph-theoretical networks, in which properties (e.g., YELLOW, SWEET) were represented as nodes and their associations as edges. In this framework, networks capture within-concept statistics that reflect how properties relate to one another across instances of a concept. We extracted formal measures of these networks that capture different aspects of network structure, and explored whether a concept's network structure relates to its flexibility of use. To do so, we compared network measures to a text-based measure of semantic diversity, as well as to empirical data from a figurative-language task and an alternative-uses task. We found that network-based measures were predictive of the text-based and empirical measures of flexible concept use, highlighting the ability of this approach to formally capture relevant characteristics of conceptual structure. Conceptual flexibility is a fundamental attribute of the cognitive and semantic systems, and in this proof of concept we reveal that variations in concept representation and use can be formally understood in terms of the informational content and topology of concept networks.

Background The availability of technological means to enhance and repair human cognitive function raises questions about the perceived morality of their use. However, we have limited knowledge about the public's intuitive attitudes toward uses of brain stimulation. Studies that enlighten us about the public's willingness to endorse specific uses of brain stimulation on themselves and others could provide a basis to understand the moral psychology guiding intuitions about neuromodulation and opportunities to inform public education and public policy. Objective Hypothesis: We expected that subjects would be less willing to enhance or repair cognitive functions perceived as more “core" to “authentic" self-identity, prioritize brain stimulation uses for themselves, and more willing to enhance “core" functions in others. Across specific hypothetical uses, we expected the moral acceptability of specific uses to be associated with subjects’ willingness to endorse them. Methods We administered two surveys to the public in which subjects were asked to report how willing they would be to enhance or repair specific cognitive abilities using a hypothetical brain stimulation device called “Ceremode". Results Among 894 subjects, we found that subjects were more willing to use technologies to repair other people than themselves. They were most inclined to repair core functions in others. Subjects’ ratings of the moral acceptability of specific uses was related to their reported willingness to use brain stimulation. Conclusion Moral acceptability is related to the public's willingness to use brain stimulation. These findings suggest that the public endorses a generous approach to applying brain stimulation for cognitive gains in others. Further, this study establishes a basis to guide moral psychological studies of cognitive modification and social processes that guide attitudes toward and uses of brain stimulation.

Soft bioelectronic interfaces for mapping and modulating excitable networks at high resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and treatment strategies. Yet, current technologies largely rely on materials and fabrication schemes that are expensive, do not scale, and critically limit the maximum attainable resolution and coverage. Solution processing is a cost-effective manufacturing alternative, but biocompatible conductive inks matching the performance of conventional metals are lacking. Here, we introduce MXtrodes, a novel class of soft, high-resolution, large-scale bioelectronic interfaces enabled by Ti3C2 MXene and scalable solution processing. We show that the electrochemical properties of MXtrodes exceed those of conventional materials, and do not require conductive gels when used in epidermal electronics. Furthermore, we validate MXtrodes in a number of applications ranging from mapping large scale neuromuscular networks in humans to delivering cortical microstimulation in small animal models. Finally, we demonstrate that MXtrodes are compatible with standard clinical neuroimaging modalities.

This work exploits Riemannian manifolds to introduce a geometric framework for online state and community classification in dynamic multilayer networks where nodes are annotated with time series. A bottom-up approach is followed, starting from the extraction of Riemannian features from nodal time series, and reaching up to online/sequential classification of features via geodesic distances and angular information in the tangent spaces of a Riemannian manifold. As a case study, features in the Grassmann manifold are generated by fitting a kernel autoregressive-moving-average model to the nodal time series of the multilayer network. The paper highlights also numerical tests on synthetic and real brain-network data, where it is shown that the proposed geometric framework outperforms state-of-the-art deep-learning models in classification accuracy, especially in cases where the number of training data is small with respect to the number of the testing ones.-------© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Alexia is common in the context of aphasia. It is widely agreed that damage to phonological and semantic systems not specific to reading causes co-morbid alexia and aphasia. Studies of alexia to date have only examined phonology and semantics as singular processes or axes of impairment, typically in the context of stereotyped alexia syndromes. However, phonology, in particular, is known to rely on subprocesses, including sensory-phonological processing, motor-phonological processing, and sensory-motor integration. Moreover, many people with stroke aphasia demonstrate mild or mixed patterns of reading impairment that do not fit neatly with one syndrome. This cross-sectional study tested whether the hallmark symptom of phonological reading impairment, the lexicality effect, emerges from damage to a specific subprocess of phonology in stroke patients not selected for alexia syndromes. Participants were 30 subjects with left-hemispheric stroke and 37 age- and education-matched controls. A logistic mixed-effects model tested whether post-stroke impairments in sensory phonology, motor phonology, or sensory-motor integration modulated the effect of item lexicality on patient accuracy in reading aloud. Support vector regression voxel-based lesion-symptom mapping localized brain regions necessary for reading and non-orthographic phonological processing. Additionally, a novel support vector regression structural connectome-symptom mapping method identified the contribution of both lesioned and spared but disconnected, brain regions to reading accuracy and non-orthographic phonological processing. Specifically, we derived whole-brain structural connectomes using constrained spherical deconvolution-based probabilistic tractography and identified lesioned connections based on comparisons between patients and controls. Logistic mixed-effects regression revealed that only greater motor-phonological impairment related to lower accuracy reading aloud pseudowords versus words. Impaired sensory-motor integration was related to lower overall accuracy in reading aloud. No relationship was identified between sensory-phonological impairment and reading accuracy. Voxel-based and structural connectome lesion-symptom mapping revealed that lesioned and disconnected left ventral precentral gyrus related to both greater motor-phonological impairment and lower sublexical reading accuracy. In contrast, lesioned and disconnected left temporoparietal cortex is related to both impaired sensory-motor integration and reduced overall reading accuracy. These results clarify that at least two dissociable phonological processes contribute to the pattern of reading impairment in aphasia. First, impaired sensory-motor integration, caused by lesions disrupting the left temporoparietal cortex and its structural connections, non-selectively reduces accuracy in reading aloud. Second, impaired motor-phonological processing, caused at least partially by lesions disrupting left ventral premotor cortex and structural connections, selectively reduces sublexical reading accuracy. These results motivate a revised cognitive model of reading aloud that incorporates a sensory-motor phonological circuit., Reading models do not incorporate known sensory-motor subprocesses of speech processing. Dickens et al. report two types of acquired reading impairment: posterior frontal lesions impair motor speech and novel word reading, whereas temporoparietal lesions impair sensory-motor integration and known and novel word reading., Graphical Abstract Graphical Abstract

This Essay addresses recent research and commentary regarding the potential contributions of cognitive neuroscience to law. For the first time, cognitive neuroscience methods have been brought to bear on the Model Penal Code’s (MPC’s) culpable–mental state categories through a neuroimaging study seeking to identify the neural correlates of knowledge and recklessness. Subsequently, this study has been presented as a paradigm for utilizing cognitive neuroscience to answer important legal questions. However, the original experiment appears to suffer serious experimental-design and conceptual limitations, belying subsequent advocacy for the legal utility of cognitive neuroscience. This Essay methodically details these limitations and argues that the original study does not seem to have actually elicited knowledge or recklessness in subjects or, even if it did, did not elicit them in discrete enough fashion to permit identification of the mental states’ neural correlates. The Essay also contends, more broadly, that cognitive neuroscience appears inapt for investigating the propriety of the MPC’s mens rea delineations since these are articulated in purely psychological-behavioral terms: mental states are the only requisites. Only psychological-behavioral manifestations provide base evidence of mental states’ existences. And psychological-behavioral research, not cognitive neuroscience, is the most direct way to investigate the practical, moral, and legal appropriateness of the MPC’s mental states by illuminating how individuals experience them, identify them in others, or employ them to dispense blame and punishment. Ultimately, recent cognitive neuroscience research does not appear to reveal anything of legal significance regarding the MPC. And, more broadly and contrary to recent assertions, cognitive neuroscience has substantial limitations when it comes to producing legally relevant information. Going forward, psychological-behavioral research should be given primacy in cognitive science investigations of MPC concepts. Cognitive neuroscience studies, on the other hand, should be treated with exceptional skepticism.

Combining transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging offers an unprecedented tool for studying how brain networks interact in vivo and how repetitive trains of TMS modulate those networks among patients diagnosed with affective disorders. TMS compliments neuroimaging by allowing the interrogation of causal control among brain circuits. Together with TMS, neuroimaging can provide valuable insight into the mechanisms underlying treatment effects and downstream circuit communication. Here we provide a background of the method, review relevant study designs, consider methodological and equipment options, and provide statistical recommendations. We conclude by describing emerging approaches that will extend these tools into exciting new applications. This article is categorized under: Psychology > Emotion and Motivation Psychology > Theory and Methods Neuroscience > Clinical Neuroscience.

The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group’s short-term, intermediate, and long-term goals. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds through the Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: landmark points are first identified in a non-random way to reveal the underlying geometric information of the feature point-cloud, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information on tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework is validated by extensive numerical tests against several state-of-the-art manifold-learning and (brain-)network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electro-encephalogram (EEG) data.

Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach. Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass-univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results - higher dice overlap and smaller average displacement - compared to VLSM. This advantage persisted at different sample sizes (N=20-131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation-based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross-validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions showed a systematic advantage of SCCAN over VLSM; under no circumstance could VLSM exceed the accuracy obtained with SCCAN. When considering functional units composed of multiple brain areas VLSM identified fewer areas than SCCAN. The investigation of real scores of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately identify known language-critical areas, while VLSM either produced diffuse maps (FDR correction) or few scattered voxels (FWER correction). Overall, this study shows that a multivariate method, such as, SCCAN, outperforms VLSM in a number of scenarios, including functional dependency on single or multiple areas, different sample sizes, different multi-area combinations, and different thresholding mechanisms (FWER, Bonferroni, FDR). These results support previous claims that multivariate methods are in general more accurate than mass-univariate approaches, and should be preferred over traditional VLSM approaches. All the methods described in this study are available in the newly developed LESYMAP package for R.

Modern neuroimaging techniques provide us with unique views on brain structure and function; i.e., how the brain is wired, and where and when activity takes place. Data acquired using these techniques can be analyzed in terms of its network structure to reveal organizing principles at the systems level. Graph representations are versatile models where nodes are associated to brain regions and edges to structural or functional connections. Structural graphs model neural pathways in white matter, which are the anatomical backbone between regions. Functional graphs are built based on functional connectivity, which is a pairwise measure of statistical interdependency between pairs of regional activity traces. Therefore, most research to date has focused on analyzing these graphs reflecting structure or function. Graph signal processing (GSP) is an emerging area of research where signals recorded at the nodes of the graph are studied atop the underlying graph structure. An increasing number of fundamental operations have been generalized to the graph setting, allowing to analyze the signals from a new viewpoint. Here, we review GSP for brain imaging data and discuss their potential to integrate brain structure, contained in the graph itself, with brain function, residing in the graph signals. We review how brain activity can be meaningfully filtered based on concepts of spectral modes derived from brain structure. We also derive other operations such as surrogate data generation or decompositions informed by cognitive systems. In sum, GSP offers a novel framework for the analysis of brain imaging data.

Cognitive flexibility describes the human ability to switch between modes of mental function to achieve goals. Mental switching is accompanied by transient changes in brain activity, which must occur atop an anatomical architecture that bridges disparate cortical and subcortical regions by underlying white matter tracts. However, an integrated perspective regarding how white matter networks might constrain brain dynamics during cognitive processes requiring flexibility has remained elusive. To address this challenge, we applied emerging tools from graph signal processing to examine whether BOLD signals measured at each point in time correspond to complex underlying anatomical networks in 28 individuals performing a perceptual task that probed cognitive flexibility. We found that the alignment between functional signals and the architecture of the underlying white matter network was associated with greater cognitive flexibility across subjects. By computing a concise measure using multi-modal neuroimaging data, we uncovered an integrated structure-function correlate of human behavior.

There is growing interest in how neuromodulators shape brain networks. Recent neuroimaging studies provide evidence that brainstem arousal systems, such as the locus coeruleus-norepinephrine system (LC-NE), influence functional connectivity and brain network topology, suggesting they have a role in flexibly reconfiguring brain networks in order to adapt behavior and cognition to environmental demands. To date, however, the relationship between brainstem arousal systems and functional connectivity has not been assessed within the context of a task with an established relationship between arousal and behavior, with most prior studies relying on incidental variations in arousal or pharmacological manipulation and static brain networks constructed over long periods of time. These factors have likely contributed to a heterogeneity of effects across studies. To address these issues, we took advantage of the association between LC-NE-linked arousal and exploration to probe the relationships between exploratory choice, arousal—as measured indirectly via pupil diameter—and brain network dynamics. Exploration in a bandit task was associated with a shift toward fewer, more weakly connected modules that were more segregated in terms of connectivity and topology but more integrated with respect to the diversity of cognitive systems represented in each module. Functional connectivity strength decreased, and changes in connectivity were correlated with changes in pupil diameter, in line with the hypothesis that brainstem arousal systems influence the dynamic reorganization of brain networks. More broadly, we argue that carefully aligning dynamic network analyses with task designs can increase the temporal resolution at which behaviorally- and cognitively-relevant modulations can be identified, and offer these results as a proof of concept of this approach.

The severity of post-stroke aphasia and the potential for recovery are highly variable and difficult to predict. Evidence suggests that optimal estimation of aphasia severity requires the integration of multiple neuroimaging modalities and the adoption of new methods that can detect multivariate brain-behavior relationships. We created and tested a multimodal framework that relies on three information sources (lesion maps, structural connectivity, and functional connectivity) to create an array of unimodal predictions which are then fed into a final model that creates "stacked multimodal predictions" (STAMP). Crossvalidated predictions of four aphasia scores (picture naming, sentence repetition, sentence comprehension, and overall aphasia severity) were obtained from 53 left hemispheric chronic stroke patients (age: 57.1 ± 12.3 yrs, post-stroke interval: 20 months, 25 female). Results showed accurate predictions for all four aphasia scores (correlation true vs. predicted: r = 0.79-0.88). The accuracy was slightly smaller but yet significant (r = 0.66) in a full split crossvalidation with each patient considered as new. Critically, multimodal predictions produced more accurate results that any single modality alone. Topological maps of the brain regions involved in the prediction were recovered and compared with traditional voxel-based lesion-to-symptom maps, revealing high spatial congruency. These results suggest that neuroimaging modalities carry complementary information potentially useful for the prediction of aphasia scores. More broadly, this study shows that the translation of neuroimaging findings into clinically useful tools calls for a shift in perspective from unimodal to multimodal neuroimaging, from univariate to multivariate methods, from linear to nonlinear models, and, conceptually, from inferential to predictive brain mapping. Hum Brain Mapp 38:5603-5615, 2017. © 2017 Wiley Periodicals, Inc.

Objective:To apply network-based statistics to diffusion-weighted imaging tractography data and detect Alzheimer disease vs non-Alzheimer degeneration in the context of corticobasal syndrome.Methods:In a cross-sectional design, pathology was confirmed by autopsy or a pathologically validated CSF total tau-to-β-amyloid ratio (T-tau/Aβ). Using structural MRI data, we identify association areas in fronto-temporo-parietal cortex with reduced gray matter density in corticobasal syndrome (n = 40) relative to age-matched controls (n = 40). Using these fronto-temporo-parietal regions of interest, we construct structural brain networks in clinically similar subgroups of individuals with Alzheimer disease (n = 21) or non-Alzheimer pathology (n = 19) by linking these regions by the number of white matter streamlines identified in a deterministic tractography analysis of diffusion tensor imaging data. We characterize these structural networks using 5 graph-based statistics, and assess their relative utility in classifying underlying pathology with leave-one-out cross-validation using a supervised support vector machine.Results:Gray matter density poorly discriminates between Alzheimer disease and non-Alzheimer pathology subgroups with low sensitivity (57%) and specificity (52%). In contrast, a statistic of local network efficiency demonstrates very good discriminatory power, with 85% sensitivity and 84% specificity.Conclusions:Our results indicate that the underlying pathologic sources of corticobasal syndrome can be classified more accurately using graph theoretical statistics derived from patterns of white matter network organization in association cortex than by regional gray matter density alone. These results highlight the importance of a multimodal neuroimaging approach to diagnostic analyses of corticobasal syndrome.

Networks of cognitive processes describe some of the key findings emerging from cognitive network neuroscience. Cognition is organized in distinct networks in the human brain. These cognitive networks interact via complex dynamics to process our environments and enact our decisions on the world. Within the emerging subdiscipline known as cognitive network neuroscience, we can connect classical neuroscience approaches to network science. This allows us to consider how major cognitive functions ranging from sensation to cognitive control and emotion are organized in the human brain. Through the lens of network neuroscience, we can enrich our understanding of normal and disordered cognitive function to be manifestations of processes and representations in ordered or disorded neural networks.

We thank Adolf and Fried (1) for their insightful commentary on our paper (2). We agree, in principle, that group-to-individual generalizability lies along a continuum. Some intraindividual and interindividual statistical estimates may be ergodic, sharing equivalent values across all statistical moments. Under these conditions, inferences from cross-sectional data could be applied to individuals. On the other end of this continuum, intra- and interindividual estimates are orthogonal, rendering them unrelated and nontransferable. Adolf and Fried (1) argue that under nonergodic conditions, conditional equivalence may still be achieved if the sources of nonergodicity can be identified, facilitating conditional inferences across levels of analysis. We agree that the notion of … [↵][1]1To whom correspondence should be addressed. Email: afisher{at}berkeley.edu. [1]: #xref-corresp-1-1