Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity.

The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and ß frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.

Controlling target brain regions by optimal selection of input nodes.

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

Spontaneous Activity Patterns in Human Attention Networks Code for Hand Movements.

Recent evidence suggests that, in the absence of any task, spontaneous brain activity patterns and connectivity in the visual and motor cortex code for natural stimuli and actions, respectively. These "resting-state" activity patterns may underlie the maintenance and consolidation (replay) of information states coding for ecological stimuli and behaviors. In this study, we examine whether replay patterns occur in resting-state activity in association cortex grouped into high-order cognitive networks not directly processing sensory inputs or motor outputs. Fifteen participants (7 females) performed four hand movements during an fMRI study. Three movements were ecological. The fourth movement as control was less ecological. Before and after the task scans, we acquired resting-state fMRI scans. The analysis examined whether multivertex task activation patterns for the four movements computed at the cortical surface in different brain networks resembled spontaneous activity patterns measured at rest. For each movement, we computed a vector of r values indicating the strength of the similarity between the mean task activation pattern and frame-by-frame resting-state patterns. We computed a cumulative distribution function of r 2 values and used the 90th percentile cutoff value for comparison. In the dorsal attention network, resting-state patterns were more likely to match task patterns for the ecological movements than the control movement. In contrast, rest-task pattern correlation was more likely for less ecological movement in the ventral attention network. These findings show that spontaneous activity patterns in human attention networks code for hand movements.SIGNIFICANCE STATEMENT fMRI indirectly measures neural activity noninvasively. Resting-state (spontaneous) fMRI signals measured in the absence of any task resemble signals evoked by task performance both in topography and inter-regional (functional) connectivity. However, the function of spontaneous brain activity is unknown. We recently showed that spatial activity patterns evoked by visual and motor tasks in visual and motor cortex, respectively, occur at rest in the absence of any stimulus or response. Here we show that activity patterns related to hand movements replay at rest in frontoparietal regions of the human attention system. These findings show that spontaneous activity in the human cortex may mediate the maintenance and consolidation of information states coding for ecological stimuli and behaviors.

Indirect functional connectivity does not predict overall survival in glioblastoma.

BACKGROUND: Lesion network mapping (LNM) is a popular framework to assess clinical syndromes following brain injury. The classical approach involves embedding lesions from patients into a normative functional connectome and using the corresponding functional maps as proxies for disconnections. However, previous studies indicated limited predictive power of this approach in behavioral deficits. We hypothesized similarly low predictiveness for overall survival (OS) in glioblastoma (GBM). METHODS: A retrospective dataset of patients with GBM was included (n = 99). Lesion masks were registered in the normative space to compute disconnectivity maps. The brain functional normative connectome consisted in data from 173 healthy subjects obtained from the Human Connectome Project. A modified version of the LNM was then applied to core regions of GBM masks. Linear regression, classification, and principal component (PCA) analyses were conducted to explore the relationship between disconnectivity and OS. OS was considered both as continuous and categorical (low, intermediate, and high survival) variable. RESULTS: The results revealed no significant associations between OS and network disconnection strength when analyzed at both voxel-wise and classification levels. Moreover, patients stratified into different OS groups did not exhibit significant differences in network connectivity patterns. The spatial similarity among the first PCA of network maps for each OS group suggested a lack of distinctive network patterns associated with survival duration. CONCLUSIONS: Compared with indirect structural measures, functional indirect mapping does not provide significant predictive power for OS in patients with GBM. These findings are consistent with previous research that demonstrated the limitations of indirect functional measures in predicting clinical outcomes, underscoring the need for more comprehensive methodologies and a deeper understanding of the factors influencing clinical outcomes in this challenging disease.

Convergence of Visual and Motor Awareness in Human Parietal Cortex.

OBJECTIVE: Brain lesions sometimes induce a failure of recognition of one's own deficits (anosognosia). Lack of deficit awareness may underlie damage of modality-specific systems, for example, visual cortex for visual anosognosia or motor/premotor cortex for motor anosognosia. However, focal lesions induce widespread remote structural and functional disconnection, and anosognosia, independent of modality, may also involve common neural mechanisms. METHODS: Here, we study the neural correlates of Anton syndrome (AS), anosognosia of blindness, and compare them with anosognosia for hemiplegia to test whether they share different or common mechanisms. We measured both local damage and patterns of structural-functional disconnection as predicted from healthy normative atlases. RESULTS: AS depends on bilateral striate and extrastriate occipital damage, and disconnection of ventral and dorsal frontoparietal regions involved in attention control. Visual and motor anosognosia each share damage of modality-specific regions, but also involve the disruption of white matter tracts, leading to functional disconnection within dorsal frontal-parietal regions that play critical roles in motor control, visuospatial attention, and multisensory integration. INTERPRETATION: These results reveal the unique shared combination of content-specific and supramodal mechanisms in anosognosia. ANN NEUROL 2023.

Long-term outcome of alcohol withdrawal seizures.

BACKGROUND AND PURPOSE: Alcohol withdrawal seizures (AWS) are a well-known complication of chronic alcohol abuse, but there is currently little knowledge of their long-term relapse rate and prognosis. The aims of this study were to identify risk factors for AWS recurrence and to study the overall outcome of patients after AWS. METHODS: In this retrospective single-center study, we included patients who were admitted to the Emergency Department after an AWS between January 1, 2013 and August 10, 2021 and for whom an electroencephalogram (EEG) was requested. AWS relapses up until April 29, 2022 were researched. We compared history, treatment with benzodiazepines or antiseizure medications (ASMs), laboratory, EEG and computed tomography findings between patients with AWS relapse (r-AWS) and patients with no AWS relapse (nr-AWS). RESULTS: A total of 199 patients were enrolled (mean age 53 ± 12 years; 78.9% men). AWS relapses occurred in 11% of patients, after a median time of 470.5 days. Brain computed tomography (n = 182) showed pathological findings in 35.7%. Risk factors for relapses were history of previous AWS (p = 0.013), skull fractures (p = 0.004) at the index AWS, and possibly epileptiform EEG abnormalities (p = 0.07). Benzodiazepines or other ASMs, taken before or after the index event, did not differ between the r-AWS and the nr-AWS group. The mortality rate was 2.9%/year of follow-up, which was 13 times higher compared to the general population. Risk factors for death were history of AWS (p < 0.001) and encephalopathic EEG (p = 0.043). CONCLUSIONS: Delayed AWS relapses occur in 11% of patients and are associated with risk factors (previous AWS >24 h apart, skull fractures, and pathological EEG findings) that also increase the epilepsy risk, that is, predisposition for seizures, if not treated. Future prospective studies are mandatory to determine appropriate long-term diagnostic and therapeutic strategies, in order to reduce the risk of relapse and mortality associated with AWS.

Latent disconnectome prediction of long-term cognitive-behavioural symptoms in stroke.

Stroke significantly impacts the quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need to better predict long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnection-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace of white matter disconnections (disconnectome) to predict neuropsychological scores 1 year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights that are available as the first comprehensive atlas of disconnectome-deficit relations across 86 scores-a Neuropsychological White Matter Atlas. Our novel predictive framework, the Disconnectome Symptoms Discoverer, achieved better predictivity performances than six other models, including functional disconnection, lesion topology and volume modelling. Out-of-sample prediction derived from this atlas presented a mean absolute error below 20% and allowed personalize neuropsychological predictions. Prediction on an external cohort achieved an R2 = 0.201 for semantic fluency. In addition, training and testing were replicated on two external cohorts achieving an R2 = 0.18 for visuospatial performance. This framework is available as an interactive web application (http://disconnectomestudio.bcblab.com) to provide the foundations for a new and practical approach to modelling cognition in stroke. We hope our atlas and web application will help to reduce the burden of cognitive deficits on patients, their families and wider society while also helping to tailor future personalized treatment programmes and discover new targets for treatments. We expect our framework's range of assessments and predictive power to increase even further through future crowdsourcing.

Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping.

Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.

Current territorial organization for access to revascularization therapies for acute ischemic stroke in the Veneto region (Italy) from 2017 to 2021.

INTRODUCTION: To evaluate the access to treatments with intravenous thrombolysis (IVT) and/or mechanical thrombectomy (MT) in acute ischemic stroke patients admitted to stroke units (SUs) of Veneto region (Italy) according to current "hub-and-spoke" model from 2017 to 2021. PATIENTS AND METHODS: We retrospectively analyzed data on treatments with IVT and/or MT for stroke patients admitted to the 23 SUs (6 Hubs and 17 Spokes) of the 6 macro-areas including 9 local sanitary units (LSUs) and 2 hospitals. RESULTS: We reported 6093 treatments with IVT alone, 1114 with IVT plus MT, and 921 with MT alone. Number of stroke unit (SU) beds/100,000 inhabitants ranges from 2.3 to 2.8, and no difference was found among different macro-areas. Number of treatments/100,000 inhabitants/year ranges from 19 to 34 for IVT alone, from 2 to 7 for IVT plus MT, and from 2 to 5 for MT alone. Number of IVT alone/SU bed/year ranges from 9 to 21 in the Hub and from 6 to 12 in the Spokes. Rate of IVT plus MT in patients directly arrived in the same LSU's Hub ranges from 50 to 81%, likewise the one of MT alone ranges from 49 to 84%. CONCLUSIONS: Treatment target rates of IVT and MT set by Action Plan for Stroke in Europe 2018-2030 has been globally exceeded in the Veneto region. However, the target rate of MT and access revascularization treatments is heterogeneous among different macro-areas. Further efforts should be made to homogenize the current territorial organization.

Variability of regional glucose metabolism and the topology of functional networks in the human brain.

The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes' resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation. Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using 18F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.

Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions.

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.

Unveiling whole-brain dynamics in normal aging through Hidden Markov Models.

During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs-fMRI) data showing a link between aging and the increase of between-networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data-driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs-fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph-based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process.

The contribution of the human posterior parietal cortex to episodic memory.

The posterior parietal cortex (PPC) is traditionally associated with attention, perceptual decision making and sensorimotor transformations, but more recent human neuroimaging studies support an additional role in episodic memory retrieval. In this Opinion article, we present a functional-anatomical model of the involvement of the PPC in memory retrieval. Parietal regions involved in perceptual attention and episodic memory are largely segregated and often show a push-pull relationship, potentially mediated by prefrontal regions. Moreover, different PPC regions carry out specific functions during retrieval - for example, representing retrieved information, recoding this information based on task demands, or accumulating evidence for memory decisions.

Multivariate Lesion-Behavior Mapping of General Cognitive Ability and Its Psychometric Constituents.

General cognitive ability, or general intelligence (g), is central to cognitive science, yet the processes that constitute it remain unknown, in good part because most prior work has relied on correlational methods. Large-scale behavioral and neuroanatomical data from neurologic patients with focal brain lesions can be leveraged to advance our understanding of the key mechanisms of g, as this approach allows inference on the independence of cognitive processes along with elucidation of their respective neuroanatomical substrates. We analyzed behavioral and neuroanatomical data from 402 humans (212 males; 190 females) with chronic, focal brain lesions. Structural equation models (SEMs) demonstrated a psychometric isomorphism between g and working memory in our sample (which we refer to as g/Gwm), but not between g and other cognitive abilities. Multivariate lesion-behavior mapping analyses indicated that g and working memory localize most critically to a site of converging white matter tracts deep to the left temporo-parietal junction. Tractography analyses demonstrated that the regions in the lesion-behavior map of g/Gwm were primarily associated with the arcuate fasciculus. The anatomic findings were validated in an independent cohort of acute stroke patients (n = 101) using model-based predictions of cognitive deficits generated from the Iowa cohort lesion-behavior maps. The neuroanatomical localization of g/Gwm provided the strongest prediction of observed g in the new cohort (r = 0.42, p < 0.001), supporting the anatomic specificity of our findings. These results provide converging behavioral and anatomic evidence that working memory is a key mechanism contributing to domain-general cognition.SIGNIFICANCE STATEMENT General cognitive ability (g) is thought to play an important role in individual differences in adaptive behavior, yet its core processes remain unknown, in large part because of difficulties in making causal inferences from correlated data. Using data from patients with focal brain damage, we demonstrate that there is a strong psychometric correspondence between g and working memory - the ability to maintain and control mental information, and that the critical neuroanatomical substrates of g and working memory include the arcuate fasciculus. This work provides converging behavioral and neuroanatomical evidence that working memory is a key mechanism contributing to domain-general cognition.

Rule Perseveration during Task-Switching in Brain Tumor: A Severe Form of Task-Setting Impairment.

It has been proposed that at least two distinct processes are engaged during task-switching: reconfiguration of the currently relevant task-set and interference resolution arising from the competing task-set. Whereas in healthy individuals the two are difficult to disentangle, their disruption is thought to cause different impairments in brain-damaged patients. Yet, the observed deficits are inconsistent across studies and do not allow drawing conclusions regarding their independence. Forty-one brain tumor patients were tested on a task-switching paradigm. We compared their performance between switch and repeat trials (switch cost) to assess rule reconfiguration, and between trials requiring the same response (congruent) and a different response for the two tasks (incongruent) to assess interference control. In line with previous studies, we found the greatest proportion of errors on incongruent trials, suggesting an interference control impairment. However, a closer look at the distribution of errors between two task rules revealed a rule perseveration impairment: Patients with high error rate on incongruent trials often applied only one task rule throughout the task and less frequently switched to the alternative one. Multivariate lesion-symptom mapping analysis unveiled the relationship between lesions localized in left orbitofrontal and posterior subcortical regions and perseveration scores, measured as absolute difference in accuracy between two task rules. This finding points to a more severe task-setting impairment, not reflected as a mere switching deficit, but instead as a difficulty in creating multiple stable task representations, in line with recent accounts of OFC functions suggesting its critical role in representing task states.

Spectral signature of attentional reorienting in the human brain.

As we move in the environment, attention shifts to novel objects of interest based on either their sensory salience or behavioral value (reorienting). This study measures with magnetoencephalography (MEG) different properties (amplitude, onset-to-peak duration) of event-related desynchronization/synchronization (ERD/ERS) of oscillatory activity during a visuospatial attention task designed to separate activity related to reorienting vs. maintaining attention to the same location, controlling for target detection and response processes. The oscillatory activity was measured both in fMRI-defined regions of interest (ROIs) of the dorsal attention (DAN) and visual (VIS) networks, previously defined as task-relevant in the same subjects, or whole-brain in a pre-defined set of cortical ROIs encompassing the main brain networks. Reorienting attention (shift cues) as compared to maintaining attention (stay cues) produced a temporal sequence of ERD/ERS modulations at multiple frequencies in specific anatomical regions/networks. An early (∼330 ms), stronger, transient theta ERS occurred in task-relevant (DAN, VIS) and control networks (VAN, CON, FPN), possibly reflecting an alert/reset signal in response to the cue. A more sustained, behaviorally relevant, low-beta band ERD peaking ∼450 ms following shift cues (∼410 for stay cues) localized in frontal and parietal regions of the DAN. This modulation is consistent with a control signal re-routing information across visual hemifields. Contralateral vs. ipsilateral shift cues produced in occipital visual regions a stronger, sustained alpha ERD (peak ∼470 ms) and a longer, transient high beta/gamma ERS (peak ∼490 ms) related to preparatory visual modulations in advance of target occurrence. This is the first description of a cascade of oscillatory processes during attentional reorienting in specific anatomical regions and networks. Among these processes, a behaviorally relevant beta desynchronization in the FEF is likely associated with the control of attention shifts.

Multi-band MEG signatures of BOLD connectivity reorganization during visuospatial attention.

The functional architecture of the resting brain, as measured with the blood oxygenation level-dependent functional connectivity (BOLD-FC), is slightly modified during task performance. In previous work, we reported behaviorally relevant BOLD-FC modulations between visual and dorsal attention regions when subjects performed a visuospatial attention task as compared to central fixation (Spadone et al., 2015). Here we use magnetoencephalography (MEG) in the same group of subjects to identify the electrophysiological correlates of the BOLD-FC modulation found in our previous work. While BOLD-FC topography, separately at rest and during visual attention, corresponded to neuromagnetic Band-Limited Power (BLP) correlation in the alpha and beta bands (8-30 Hz), BOLD-FC modulations evoked by performing the visual attention task (Spadone et al. 2015) did not match any specific oscillatory band BLP modulation. Conversely, following the application of an orthogonal spatial decomposition that identifies common inter-subject co-variations, we found that attention-rest BOLD-FC modulations were recapitulated by multi-spectral BLP-FC components. Notably, individual variability of alpha connectivity between Frontal Eye Fields and visual occipital regions, jointly with decreased interaction in the Visual network, correlated with visual discrimination accuracy. In summary, task-rest BOLD connectivity modulations match multi-spectral MEG BLP connectivity.

Alertness Training Improves Spatial Bias and Functional Ability in Spatial Neglect.

OBJECTIVE: We conducted a multisite, randomized, double-blinded, controlled trial to examine the effectiveness of a digital health intervention targeting the intrinsic regulation of goal-directed alertness in patients with chronic hemispatial neglect. METHODS: Forty-nine participants with hemispatial neglect, who demonstrated significant spatially biased attention after acquired brain injury, were randomly assigned to the experimental attention remediation treatment or the active control group. The participants engaged with the remotely administered interventions for 12 weeks. The primary outcome was spatial bias on the Posner cueing task (response time difference: left minus right target trials). Secondary outcomes included functional abilities (measured via the Catherine Bergego scale and Barthel index), spatial cognition, executive function, quality of life, and sleep. Assessments were conducted before and immediately after participation in the experimental intervention or control condition, and again after a 3-month no-contact period. RESULTS: Compared with the active control group, the intervention group exhibited a significant improvement in the primary outcome, a reduction in spatially biased attention on the Posner cueing task (p = 0.010, Cohen's d = 0.96), in addition to significant improvements in functional abilities as measured on the Catherine Bergego and Barthel indices (p = 0.027, Cohen's d = 0.24). INTERPRETATION: Our results demonstrate that our attention training program was effective in improving the debilitating attention deficits common to hemispatial neglect. This benefit generalized to improvements in real-world functional abilities. This safe, highly scalable, and self-administered treatment for hemispatial neglect might serve as a useful addition to the existing standard of care. ANN NEUROL 2020;88:747-758.

Post-stroke deficit prediction from lesion and indirect structural and functional disconnection.

Behavioural deficits in stroke reflect both structural damage at the site of injury, and widespread network dysfunction caused by structural, functional, and metabolic disconnection. Two recent methods allow for the estimation of structural and functional disconnection from clinical structural imaging. This is achieved by embedding a patient's lesion into an atlas of functional and structural connections in healthy subjects, and deriving the ensemble of structural and functional connections that pass through the lesion, thus indirectly estimating its impact on the whole brain connectome. This indirect assessment of network dysfunction is more readily available than direct measures of functional and structural connectivity obtained with functional and diffusion MRI, respectively, and it is in theory applicable to a wide variety of disorders. To validate the clinical relevance of these methods, we quantified the prediction of behavioural deficits in a prospective cohort of 132 first-time stroke patients studied at 2 weeks post-injury (mean age 52.8 years, range 22-77; 63 females; 64 right hemispheres). Specifically, we used multivariate ridge regression to relate deficits in multiple functional domains (left and right visual, left and right motor, language, spatial attention, spatial and verbal memory) with the pattern of lesion and indirect structural or functional disconnection. In a subgroup of patients, we also measured direct alterations of functional connectivity with resting-state functional MRI. Both lesion and indirect structural disconnection maps were predictive of behavioural impairment in all domains (0.16 < R2 < 0.58) except for verbal memory (0.05 < R2 < 0.06). Prediction from indirect functional disconnection was scarce or negligible (0.01 < R2 < 0.18) except for the right visual field deficits (R2 = 0.38), even though multivariate maps were anatomically plausible in all domains. Prediction from direct measures of functional MRI functional connectivity in a subset of patients was clearly superior to indirect functional disconnection. In conclusion, the indirect estimation of structural connectivity damage successfully predicted behavioural deficits post-stroke to a level comparable to lesion information. However, indirect estimation of functional disconnection did not predict behavioural deficits, nor was a substitute for direct functional connectivity measurements, especially for cognitive disorders.

Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke.

Focal brain lesions disrupt resting-state functional connectivity, but the underlying structural mechanisms are unclear. Here, we examined the direct and indirect effects of structural disconnections on resting-state functional connectivity in a large sample of sub-acute stroke patients with heterogeneous brain lesions. We estimated the impact of each patient's lesion on the structural connectome by embedding the lesion in a diffusion MRI streamline tractography atlas constructed using data from healthy individuals. We defined direct disconnections as the loss of direct structural connections between two regions, and indirect disconnections as increases in the shortest structural path length between two regions that lack direct structural connections. We then tested the hypothesis that functional connectivity disruptions would be more severe for disconnected regions than for regions with spared connections. On average, nearly 20% of all region pairs were estimated to be either directly or indirectly disconnected by the lesions in our sample, and extensive disconnections were associated primarily with damage to deep white matter locations. Importantly, both directly and indirectly disconnected region pairs showed more severe functional connectivity disruptions than region pairs with spared direct and indirect connections, respectively, although functional connectivity disruptions tended to be most severe between region pairs that sustained direct structural disconnections. Together, these results emphasize the widespread impacts of focal brain lesions on the structural connectome and show that these impacts are reflected by disruptions of the functional connectome. Further, they indicate that in addition to direct structural disconnections, lesion-induced increases in the structural shortest path lengths between indirectly structurally connected region pairs provide information about the remote functional disruptions caused by focal brain lesions.