Network measures predict neuropsychological outcome after brain injury

Studying the social mind: An updated summary of findings from the Vietnam Head Injury Study

Lesion mapping studies allow us to evaluate the potential causal contribution of specific brain areas to human cognition and complement other cognitive neuroscience methods, as several authors have recently pointed out. Here, we present an updated summary of the findings from the Vietnam Head Injury Study (VHIS) focusing on the studies conducted over the last decade, that examined the social mind and its intricate neural and cognitive underpinnings. The VHIS is a prospective, long-term follow-up study of Vietnam veterans with penetrating traumatic brain injury (pTBI) and healthy controls (HC). The scope of the work is to present the studies from the latest phases (3 and 4) of the VHIS, 70 studies since 2011, when the Raymont et al. paper was published (Raymont et al., 2011). These studies have contributed to our understanding of human social cognition, including political and religious beliefs, theory of mind, but also executive functions, intelligence, and personality. This work finally discusses the usefulness of lesion mapping as an approach to understanding the functions of the human brain from basic science and clinical perspectives.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples

BACKGROUND

The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.

METHODS

We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.

RESULTS

In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.

CONCLUSION

Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Neuroanatomical and neurocognitive correlates of delusion in Alzheimer's disease and mild cognitive impairment

BACKGROUND

Neuropsychiatric symptoms and delusions are highly prevalent among people with dementia. However, multiple roots of neurobiological bases and shared neural basis of delusion and cognitive function remain to be characterized. By utilizing a fine-grained multivariable approach, we investigated distinct neuroanatomical correlates of delusion symptoms across a large population of dementing illnesses.

METHODS

In this study, 750 older adults with mild cognitive impairment and Alzheimer's disease completed brain structural imaging and neuropsychological assessment. We utilized principal component analysis followed by varimax rotation to identify the distinct multivariate correlates of cortical thinning patterns. Five of the cognitive domains were assessed whether the general cognitive abilities mediate the association between cortical thickness and delusion.

RESULTS

The result showed that distributed thickness patterns of temporal and ventral insular cortex (component 2), inferior and lateral prefrontal cortex (component 1), and somatosensory-visual cortex (component 5) showed negative correlations with delusions. Subsequent mediation analysis showed that component 1 and 2, which comprises inferior frontal, anterior insula, and superior temporal regional thickness accounted for delusion largely through lower cognitive functions. Specifically, executive control function assessed with the Trail Making Test mediated the relationship between two cortical thickness patterns and delusions.

DISCUSSION

Our findings suggest that multiple distinct subsets of brain regions underlie the delusions among older adults with cognitive impairment. Moreover, a neural loss may affect the occurrence of delusion in dementia largely due to impaired general cognitive abilities.

Concussion-Related Disruptions to Hub Connectivity in the Default Mode Network Are Related to Symptoms and Cognition

Concussions present with a myriad of symptomatic and cognitive concerns; however, the relationship between these functional disruptions and the underlying changes in the brain are not yet well understood. Hubs, or brain regions that are connected to many different functional networks, may be specifically disrupted after concussion. Given the implications in concussion research, we quantified hub disruption within the default mode network (DMN) and between the DMN and other brain networks. We collected resting-state functional magnetic resonance imaging data from collegiate student-athletes (n = 44) at three time points: baseline (before beginning their athletic season), acute post-injury (approximately 48h after a diagnosed concussion), and recovery (after starting return-to-play progression, but before returning to contact). We used self-reported symptoms and computerized cognitive assessments collected across similar time points to link these functional connectivity changes to clinical outcomes. Concussion resulted in increased connectivity between regions within the DMN compared with baseline and recovery, and this post-injury connectivity was more positively related to symptoms and more negatively related to visual memory performance compared with baseline and recovery. Further, concussion led to decreased connectivity between DMN hubs and visual network non-hubs relative to baseline and recovery, and this post-injury connectivity was more negatively related to somatic symptoms and more positively related to visual memory performance compared with baseline and recovery. Relationships between functional connectivity, symptoms, and cognition were not significantly different at baseline versus recovery. These results highlight a unique relationship between self-reported symptoms, visual memory performance, and acute functional connectivity changes involving DMN hubs after concussion in athletes. This may provide evidence for a disrupted balance of within- and between-network communication highlighting possible network inefficiencies after concussion. These results aid in our understanding of the pathophysiological disruptions after concussion and inform our understanding of the associations between disruptions in brain connectivity and specific clinical presentations acutely post-injury.

Basis of executive functions in fine-grained architecture of cortical and subcortical human brain networks

Theoretical models suggest that executive functions rely on both domain-general and domain-specific processes. Supporting this view, prior brain imaging studies have revealed that executive activations converge and diverge within broadly characterized brain networks. However, the lack of precise anatomical mappings has impeded our understanding of the interplay between domain-general and domain-specific processes. To address this challenge, we used the high-resolution multimodal magnetic resonance imaging approach of the Human Connectome Project to scan participants performing 3 canonical executive tasks: n-back, rule switching, and stop signal. The results reveal that, at the individual level, different executive activations converge within 9 domain-general territories distributed in frontal, parietal, and temporal cortices. Each task exhibits a unique topography characterized by finely detailed activation gradients within domain-general territory shifted toward adjacent resting-state networks; n-back activations shift toward the default mode, rule switching toward dorsal attention, and stop signal toward cingulo-opercular networks. Importantly, the strongest activations arise at multimodal neurobiological definitions of network borders. Matching results are seen in circumscribed regions of the caudate nucleus, thalamus, and cerebellum. The shifting peaks of local gradients at the intersection of task-specific networks provide a novel mechanistic insight into how partially-specialized networks interact with neighboring domain-general territories to generate distinct executive functions.

Abnormalities in modular connectivity of functional brain networks and cognitive changes in patients with anti -N-methyl-D-aspartate receptor encephalitis

OBJECTIVE

To explore potential mechanisms of cognitive changes in patients with anti-NMDAR encephalitis (ANMDARE) from intramodule and intermodule effects of brain functional networks.

METHODS

Resting-state functional MRI(rs-fMRI) imaging data was collected from 30 ANMDARE and 30 healthy controls (HCs). A brain functional matrix was constructed, and sparsity was established by module similarity. For both groups, changes in functional connectivity (FC) within and between modules was calculated, and whole-brain functional topology was analyzed. Finally, the association of brain functional with cognitive function in ANMDARE was further analyzed.

RESULTS

Compared to HCs, ANMDARE had enhanced connectivity within the modules that included the occipito-parietal-temporal and parahippocampal gyri. ANMDARE had significantly higher participation coefficients (PC) in the right inferior frontal gyrus than HCs and significantly lower PC in the left superior parietal lobule, left caudate nucleus, and right putamen. No statistically significant differences in global topological properties were found between the two groups. No correlations were found between functional and structural brain indicators and the Cognitive Assessment Scale and the Emotional Deficit Scale.

CONCLUSIONS

Patients with ANMDARE are manifested by enhanced intramodular FC and intermodular connectivity changes in the brain. This may help to understand the pathophysiological mechanisms of the disease from a global perspective.

Living on the edge: network neuroscience beyond nodes

Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This has come at the expense of the anatomical and functional connections that link these elements to one another. A new perspective - namely one that emphasizes 'edges' - may prove fruitful in addressing outstanding questions in network neuroscience. We highlight one recently proposed 'edge-centric' method and review its current applications, merits, and limitations. We also seek to establish conceptual and mathematical links between this method and previously proposed approaches in the network science and neuroimaging literature. We conclude by presenting several avenues for future work to extend and refine existing edge-centric analysis.

Perilesional and contralesional brain activations related to associative encoding of unfamiliar face-names pairs in adults with left chronic stroke with or without ischemic infarct on left inferior frontal gyrus

The study of an Ischemic stroke infarction allows verifying how the lesion produces alterations in the neuronal networks resulting in cognitive deficits. It also allows the verification of adaptive and maladaptive cerebral reorganization related to the injury. In our previous fMRI study, we found that patients without ischemic vascular lesions in left inferior frontal gyrus showed an efficient compensation mechanism during the associative encoding of face name pairs, by the increased activation of ventrolateral and dorsolateral areas of contralesional hemisphere associated with better memory performance. While patients with ischemic vascular lesions on left inferior frontal gyrus (IFG) demonstrated worse memory performance and no signs of compensation mechanism. The present study explores more of these findings by analyzing perilesional and contralesional activations related to unfamiliar face name associative encoding in adults with chronic ischemic stroke, with or without left IFG lesion, compared to healthy controls. The main results showed that stroke survivors without lesions in IFG demonstrated increased activation in perilesional and contralesional prefrontal regions associated with better associative memory recognition, which are indicative of adaptive compensatory mechanisms. However, they also showed a negative correlation between the activation of right anterior prefrontal and inferior parietal regions and the associative memory performance, which may indicate the presence of maladaptive interhemispheric disinhibition. On the other hand, stroke survivors with IFG lesions demonstrated negative correlations in activations of the ipsilesional inferior parietal cortex and positive correlations in activations of the left middle frontal gyrus and left precentral cortex, which demonstrate the simultaneous occurrence of adaptive and maladaptive brain reorganization mechanisms in this group. However, the increase in perilesional prefrontal regions, associated with bilateral activation of the hippocampus and amygdala, was not enough to compensate for the inefficiency of associative memory performance. Finally, the differences in activation observed in stroke survivors reflect their clinical heterogeneity and demonstrate that adaptive or maladaptive compensatory mechanisms can coexist in the same group of patients. Furthermore, they reinforce the importance of the left IFG in the associative encoding of unfamiliar face name pairs and may suggest a deficit in associative memory related to injury in this region.

Evaluating functional brain organization in individuals and identifying contributions to network overlap

Individual differences in the spatial organization of resting state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting state networks can be derived using high quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that network overlap is indicative of linear additive coupling. These results suggest that regions of network overlap concurrently process information from all contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

Weighted ordinal connection based functional network classification for schizophrenia disease detection using EEG signal

A brain connectivity network (BCN) is an advanced approach to examining brain functionality in various conditions. However, the predictability of the BCN is affected by the connectivity measure used for the network construction. Various connectivity measures available in the literature differ according to the domain of their working data. The application of random connectivity measures might result in an inefficient BCN that ultimately hampers its predictability. Therefore, selecting an appropriate functional connectivity metric is crucial in clinical as well as cognitive neuroscience. In parallel to this, an effective network identifier plays a vital role in distinguishing different brain states. Hence, the objective of this paper is two-fold, which includes identifying suitable connectivity measures and proposing an efficient network identifier. For this, the weighted BCN (WBCN) is constructed using multiple connectivity measures like correlation coefficient (r), coherence (COH), phase-locking value (PLV), and mutual information (MI) from electroencephalogram (EEG) signals. The most recent technique for feature extraction, i.e., weighted ordinal connections, has been applied to EEG-based BCN. EEG signals data has been taken from the schizophrenia disease database. Further, several classification algorithms such as k-nearest neighbours (KNN), support vector machine (SVM) with linear, radial basis function and polynomial kernels, random forest (RF), and 1D convolutional neural network (CNN1D) are used to classify the brain states based on extracted features. In classification, 90% accuracy is achieved by the CNN1D classifier with WBCN based on the coherence connectivity measure. The study also provides a structural analysis of the BCN.

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed 'variants' in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability.

Motor function improvement and acceptability of non-invasive brain stimulation in patients with Parkinson's disease: a Bayesian network analysis

Background

Parkinson's disease (PD) is a neurodegenerative disorder defined by progressive motor and non-motor symptoms. Currently, the pro-cognitive effects of transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS) are well-supported in previous literatures. However, controversy surrounding the optimal therapeutic target for motor symptom improvement remains.

Objective

This network meta-analysis (NMA) was conducted to comprehensively evaluate the optimal strategy to use rTMS and tDCS to improve motor symptoms in PD.

Methods

We searched PubMed, Embase, and Cochrane electronic databases for eligible randomized controlled studies (RCTs). The primary outcome was the changes of Unified Parkinson's Disease Rating Scale (UPDRS) part III score, the secondary outcomes were Time Up and Go Test (TUGT) time, and Freezing of Gait Questionnaire (FOGQ) score. The safety outcome was indicated by device-related adverse events (AEs).

Result

We enrolled 28 studies that investigated various strategies, including high-frequency rTMS (HFrTMS), low-frequency rTMS (LFrTMS), anodal tDCS (AtDCS), AtDCS_ cathode tDCS (CtDCS), HFrTMS_LFrTMS, and Sham control groups. Both HFrTMS (short-term: mean difference (MD) -5.21, 95% credible interval (CrI) -9.26 to -1.23, long-term: MD -4.74, 95% CrI -6.45 to -3.05), and LFrTMS (long-term: MD -4.83, 95% CrI -6.42 to -3.26) were effective in improving UPDRS-III score compared with Sham stimulation. For TUGT time, HFrTMS (short-term: MD -2.04, 95% CrI -3.26 to -0.8, long-term: MD -2.66, 95% CrI -3.55 to -1.77), and AtDCS (short-term: MD -0.8, 95% CrI -1.26 to -0.34, long-term: MD -0.69, 95% CrI -1.31 to -0.08) produced a significant difference compared to Sham stimulation. However, no statistical difference was found in FOGQ score among the various groups. According to the surface under curve ranking area, HFrTMS ranked first in short-term UPDRS-III score (0.77), short-term (0.82), and long-term (0.84) TUGT time, and short-term FOGQ score (0.73). With respect to the safety outcomes, all strategies indicated few and self-limiting AEs.

Conclusion

HFrTMS may be the optimal non-invasive brain stimulation (NIBS) intervention to improve motor function in patients with PD while NIBS has generally been well tolerated. However, further studies focusing on the clinical outcomes resulting from the different combined schedules of tDCS and rTMS are required.

Systematic review registration

https://inplasy.com/inplasy-2023-4-0087/, identifier: 202340087.

Multiscale effects of excitatory-inhibitory homeostasis in lesioned cortical networks: A computational study

Stroke-related disruptions in functional connectivity (FC) often spread beyond lesioned areas and, given the localized nature of lesions, it is unclear how the recovery of FC is orchestrated on a global scale. Since recovery is accompanied by long-term changes in excitability, we propose excitatory-inhibitory (E-I) homeostasis as a driving mechanism. We present a large-scale model of the neocortex, with synaptic scaling of local inhibition, showing how E-I homeostasis can drive the post-lesion restoration of FC and linking it to changes in excitability. We show that functional networks could reorganize to recover disrupted modularity and small-worldness, but not network dynamics, suggesting the need to consider forms of plasticity beyond synaptic scaling of inhibition. On average, we observed widespread increases in excitability, with the emergence of complex lesion-dependent patterns related to biomarkers of relevant side effects of stroke, such as epilepsy, depression and chronic pain. In summary, our results show that the effects of E-I homeostasis extend beyond local E-I balance, driving the restoration of global properties of FC, and relating to post-stroke symptomatology. Therefore, we suggest the framework of E-I homeostasis as a relevant theoretical foundation for the study of stroke recovery and for understanding the emergence of meaningful features of FC from local dynamics.

Assessment of vibration modulated regional cerebral blood flow with MRI

Human brain experiences vibration of certain magnitude and frequency during various physical activities such as vehicle transportation and machine operation, which may cause traumatic brain injury or other brain diseases. However, the mechanisms of brain pathogenesis due to vibration are not fully elucidated due to the lack of techniques to study brain functions while applying vibration to the brain at a specific magnitude and frequency. Here, this study reported a custom-built head-worn electromagnetic actuator that applied vibration to the brain in vivo at an accurate frequency inside a magnetic resonance imaging scanner while cerebral blood flow (CBF) was acquired. Using this technique, CBF values from 45 healthy volunteers were quantitatively measured immediately following vibration at 20, 30, 40 Hz, respectively. Results showed increasingly reduced CBF with increasing frequency at multiple regions of the brain, while the size of the regions expanded. Importantly, the vibration-induced CBF reduction regions largely fell inside the brain's default mode network (DMN), with about 58 or 46% overlap at 30 or 40 Hz, respectively. These findings demonstrate that vibration as a mechanical stimulus can change strain conditions, which may induce CBF reduction in the brain with regional differences in a frequency-dependent manner. Furthermore, the overlap between vibration-induced CBF reduction regions and DMN suggested a potential relationship between external mechanical stimuli and cognitive functions.

Individual Variability in Performance Reflects Selectivity of the Multiple Demand Network among Children and Adults

Executive function (EF) is essential for humans to effectively engage in cognitively demanding tasks. In adults, EF is subserved by frontoparietal regions in the multiple demand (MD) network, which respond to various cognitively demanding tasks. However, children initially show poor EF and prolonged development. Do children recruit the same network as adults? Is it functionally and connectionally distinct from adjacent language cortex, as in adults? And is this activation or connectivity dependent on age or ability? We examine task-dependent (spatial working memory and passive language tasks) and resting state functional data in 44 adults (18-38 years, 68% female) and 37 children (4-12 years, 35% female). Subject-specific functional ROIs (ss-fROIs) show bilateral MD network activation in children. In both children and adults, these MD ss-fROIs are not recruited for linguistic processing and are connectionally distinct from language ss-fROIs. While MD activation was lower in children than in adults (even in motion- and performance-matched groups), both showed increasing MD activation with better performance, especially in right hemisphere ss-fROIs. We observe this relationship even when controlling for age, cross-sectionally and in a small longitudinal sample of children. These data suggest that the MD network is selective to cognitive demand in children, is distinct from adjacent language cortex, and increases in selectivity as performance improves. These findings show that neural structures subserving domain-general EF emerge early and are sensitive to ability even in children. This research advances understanding of how high-level human cognition emerges and could inform interventions targeting cognitive control.SIGNIFICANCE STATEMENT This study provides evidence that young children already show differentiated brain network organization between regions that process cognitive demand and language. These data support the hypothesis that children recruit a similar network as adults to process cognitive demand; and despite immature characteristics, children's selectivity looks more adult-like as their executive function ability increases. Mapping early stages of network organization furthers our understanding of the functional architecture underlying domain-general executive function. Determining typical variability underlying cognitive processing across developmental periods helps establish a threshold for executive dysfunction. Early markers of dysfunction are necessary for effective early identification, prevention, and intervention efforts for individuals struggling with deficits in processing cognitive demand.

Maturation of large-scale brain systems over the first month of life

The period immediately after birth is a critical developmental window, capturing rapid maturation of brain structure and a child's earliest experiences. Large-scale brain systems are present at delivery, but how these brain systems mature during this narrow window (i.e. first weeks of life) marked by heightened neuroplasticity remains uncharted. Using multivariate pattern classification techniques and functional connectivity magnetic resonance imaging, we detected robust differences in brain systems related to age in newborns (n = 262; R2 = 0.51). Development over the first month of life occurred brain-wide, but differed and was more pronounced in brain systems previously characterized as developing early (i.e. sensorimotor networks) than in those characterized as developing late (i.e. association networks). The cingulo-opercular network was the only exception to this organizing principle, illuminating its early role in brain development. This study represents a step towards a normative brain "growth curve" that could be used to identify atypical brain maturation in infancy.

The relevance of rich club regions for functional outcome post-stroke is enhanced in women

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

High gamma activity distinguishes frontal cognitive control regions from adjacent cortical networks

Though the lateral frontal cortex is broadly implicated in cognitive control, functional MRI (fMRI) studies suggest fine-grained distinctions within this region. To examine this question electrophysiologically, we placed electrodes on the lateral frontal cortex in patients undergoing awake craniotomy for tumor resection. Patients performed verbal tasks with a manipulation of attentional switching, a canonical control demand. Power in the high gamma range (70-250 Hz) distinguished electrodes based on their location within a high-resolution fMRI network parcellation of the frontal lobe. Electrodes within the canonical fronto-parietal control network showed increased power in the switching condition, a result absent in electrodes within default mode, language and somato-motor networks. High gamma results contrasted with spatially distributed power decreases in the beta range (12-30 Hz). These results confirm the importance of fine-scale functional distinctions within the human frontal lobe, and pave the way for increased precision of functional mapping in tumor surgeries.

Brain dynamics in triple-network interactions and its relation to multiple cognitive impairments in mild traumatic brain injury

Traumatic brain injury (TBI) disrupt the coordinated activity of triple-network and produce impairments across several cognitive domains. The triple-network model posits a key role of the salience network (SN) that regulates interactions with the central executive network (CEN) and default mode network (DMN). However, the aberrant dynamic interactions among triple-network and associations with neurobehavioral symptoms in mild TBI was still unclear. In present study, we used brain network interaction index (NII) and dynamic functional connectivity to examine the time-varying cross-network interactions among the triple-network in 109 acute patients, 41 chronic patients, and 65 healthy controls. Dynamic cross-network interactions were significantly increased and more variable in mild TBI compared to controls. Crucially, mild TBI exhibited an increased NII as enhanced integrations between the SN and CEN while reduced coupling of the SN with DMN. The increased NII also implied much severer and multiple domains of cognitive impairments at both acute and chronic mild TBI. Abnormities in time-varying engagement of triple-network is a clinically relevant neurobiological signature of psychopathology in mild TBI. The findings provided align with and advance an emerging perspective on the importance of aberrant brain dynamics associated with highly disparate cognitive and behavioral outcomes in trauma.

Reorganized Hubs of Brain Functional Networks after Acute Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI)-associated damage to hub regions can lead to disrupted modular structures of functional brain networks and may result in widespread cognitive and behavioral deficits. The spatial layout of brain connections and modules is essential for understanding the reorganization of brain networks to trauma. We investigated the roles of hubs in inter-subnetwork information coordination and integration using participation coefficients (PCs) in 74 patients with acute mTBI and 51 matched healthy controls. In some brain networks, such as default mode network (DMN) and frontoparietal network (FPN), mild TBI patients had decreased PC levels, while this measure was saliently increased in patients in other networks, such as the visual network. The hub disruption index was defined as the gradient of a straight line fitted to scatterplots of individual mTBI in participation coefficient versus mean participation coefficient of healthy groups. There was a trend of radical reorganization of some efficient "hub" nodes in patients (κ = -0.15), compared with controls (κ close to 0). The PC of brain hubs can also differentiate mTBI patients from controls with an 88% accuracy, and decreased PC levels in FPN can predict patient' s worse cognitive information processing speed (r = 0.36, p < 0.002) and working memory performance (r = 0.35, p < 0.002). Reduced PC within the DMN was associated with patients' complaints of post-concussion symptoms (r = -0.35, p < 0.002). This evidence suggests a trend of spatial transition of hub profiles in acute mTBI, and graph metrics of PC measures can be used as potential diagnostic biomarkers.

The thalamus is the causal hub of intervention in patients with major depressive disorder: Evidence from the Granger causality analysis

Major depressive disorder (MDD) is the leading mental disorder and afflicts more than 350 million people worldwide. The underlying neural mechanisms of MDD remain unclear, hindering the accurate treatment. Recent brain imaging studies have observed functional abnormalities in multiple brain regions in patients with MDD, identifying core brain regions is the key to locating potential therapeutic targets for MDD. The Granger causality analysis (GCA) measures directional effects between brain regions and, therefore, can track causal hubs as potential intervention targets for MDD. We reviewed literature employing GCA to investigate abnormal brain connections in patients with MDD. The total degree of effective connections in the thalamus (THA) is more than twice that in traditional targets such as the superior frontal gyrus and anterior cingulate cortex. Altered causal connections in patients with MDD mainly included enhanced bottom-up connections from the thalamus to various cortical and subcortical regions and reduced top-down connections from these regions to the THA, indicating excessive uplink sensory information and insufficient downlink suppression information for negative emotions. We suggest that the thalamus is the most crucial causal hub for MDD, which may serve as the downstream target for non-invasive brain stimulation and medication approaches in MDD treatment.

Thalamocortical contributions to cognitive task activity

Thalamocortical interaction is a ubiquitous functional motif in the mammalian brain. Previously (Hwang et al., 2021), we reported that lesions to network hubs in the human thalamus are associated with multi-domain behavioral impairments in language, memory, and executive functions. Here, we show how task-evoked thalamic activity is organized to support these broad cognitive abilities. We analyzed functional magnetic resonance imaging (MRI) data from human subjects that performed 127 tasks encompassing a broad range of cognitive representations. We first investigated the spatial organization of task-evoked activity and found a basis set of activity patterns evoked to support processing needs of each task. Specifically, the anterior, medial, and posterior-medial thalamus exhibit hub-like activity profiles that are suggestive of broad functional participation. These thalamic task hubs overlapped with network hubs interlinking cortical systems. To further determine the cognitive relevance of thalamic activity and thalamocortical functional connectivity, we built a data-driven thalamocortical model to test whether thalamic activity can be used to predict cortical task activity. The thalamocortical model predicted task-specific cortical activity patterns, and outperformed comparison models built on cortical, hippocampal, and striatal regions. Simulated lesions to low-dimensional, multi-task thalamic hub regions impaired task activity prediction. This simulation result was further supported by profiles of neuropsychological impairments in human patients with focal thalamic lesions. In summary, our results suggest a general organizational principle of how the human thalamocortical system supports cognitive task activity.

Brain network architecture constrains age-related cortical thinning

Age-related cortical atrophy, approximated by cortical thickness measurements from magnetic resonance imaging, follows a characteristic pattern over the lifespan. Although its determinants remain unknown, mounting evidence demonstrates correspondence between the connectivity profiles of structural and functional brain networks and cortical atrophy in health and neurological disease. Here, we performed a cross-sectional multimodal neuroimaging analysis of 2633 individuals from a large population-based cohort to characterize the association between age-related differences in cortical thickness and functional as well as structural brain network topology. We identified a widespread pattern of age-related cortical thickness differences including "hotspots" of pronounced age effects in sensorimotor areas. Regional age-related differences were strongly correlated within the structurally defined node neighborhood. The overall pattern of thickness differences was found to be anchored in the functional network hierarchy as encoded by macroscale functional connectivity gradients. Lastly, the identified difference pattern covaried significantly with cognitive and motor performance. Our findings indicate that connectivity profiles of functional and structural brain networks act as organizing principles behind age-related cortical thinning as an imaging surrogate of cortical atrophy.

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.

Connectivity impairment of cerebellar and sensorimotor connector hubs in Parkinson’s disease

Cognitive and movement processes involved integration of several large-scale brain networks. Central to these integrative processes are connector hubs, brain regions characterized by strong connections with multiple networks. Growing evidence suggests that many neurodegenerative and psychiatric disorders are associated with connector hub dysfunctions. Using a network metric called functional connectivity overlap ratio, we investigated connector hub alterations in Parkinson’s disease. Resting-state functional MRI data from 99 patients (male/female = 44/55) and 99 age- and sex-matched healthy controls (male/female = 39/60) participating in our cross-sectional study were used in the analysis. We have identified two sets of connector hubs, mainly located in the sensorimotor cortex and cerebellum, with significant connectivity alterations with multiple resting-state networks. Sensorimotor connector hubs have impaired connections primarily with primary processing (sensorimotor, visual), visuospatial, and basal ganglia networks, whereas cerebellar connector hubs have impaired connections with basal ganglia and executive control networks. These connectivity alterations correlated with patients’ motor symptoms. Specifically, values of the functional connectivity overlap ratio of the cerebellar connector hubs were associated with tremor score, whereas that of the sensorimotor connector hubs with postural instability and gait disturbance score, suggesting potential association of each set of connector hubs with the disorder’s two predominant forms, the akinesia/rigidity and resting tremor subtypes. In addition, values of the functional connectivity overlap ratio of the sensorimotor connector hubs were highly predictive in classifying patients from controls with an accuracy of 75.76%. These findings suggest that, together with the basal ganglia, cerebellar and sensorimotor connector hubs are significantly involved in Parkinson’s disease with their connectivity dysfunction potentially driving the clinical manifestations typically observed in this disorder.

Earlier Alzheimer’s disease onset is associated with tau pathology in brain hub regions and facilitated tau spreading

In Alzheimer’s disease (AD), younger symptom onset is associated with accelerated disease progression and tau spreading, yet the mechanisms underlying faster disease manifestation are unknown. To address this, we combined resting-state fMRI and longitudinal tau-PET in two independent samples of controls and biomarker-confirmed AD patients (ADNI/BioFINDER, n = 240/57). Consistent across both samples, we found that younger symptomatic AD patients showed stronger tau-PET in globally connected fronto-parietal hubs, i.e., regions that are critical for maintaining cognition in AD. Stronger tau-PET in hubs predicted faster subsequent tau accumulation, suggesting that tau in globally connected regions facilitates connectivity-mediated tau spreading. Further, stronger tau-PET in hubs mediated the association between younger age and faster tau accumulation in symptomatic AD patients, which predicted faster cognitive decline. These independently validated findings suggest that younger AD symptom onset is associated with stronger tau pathology in brain hubs, and accelerated tau spreading throughout connected brain regions and cognitive decline.

Structural disconnectome mapping of cognitive function in poststroke patients

BACKGROUND AND PURPOSE

Sequalae following stroke represents a significant challenge in current rehabilitation. The location and size of focal lesions are only moderately predictive of the diverse cognitive outcome after stroke. One explanation building on recent work on brain networks proposes that the cognitive consequences of focal lesions are caused by damages to anatomically distributed brain networks supporting cognition rather than specific lesion locations.

METHODS

To investigate the association between poststroke structural disconnectivity and cognitive performance, we estimated individual level whole-brain disconnectivity probability maps based on lesion maps from 102 stroke patients using normative data from healthy controls. Cognitive performance was assessed in the whole sample using Montreal Cognitive Assessment, and a more comprehensive computerized test protocol was performed on a subset (n = 82).

RESULTS

Multivariate analysis using Partial Least Squares on the disconnectome maps revealed that higher disconnectivity in right insular and frontal operculum, superior temporal gyrus and putamen was associated with poorer MoCA performance, indicating that lesions in regions connected with these brain regions are more likely to cause cognitive impairment. Furthermore, our results indicated that disconnectivity within these clusters was associated with poorer performance across multiple cognitive domains.

CONCLUSIONS

These findings demonstrate that the extent and distribution of structural disconnectivity following stroke are sensitive to cognitive deficits and may provide important clinical information predicting poststroke cognitive sequalae.

Mapping correlated neurological deficits after stroke to distributed brain networks

Understanding the relationships between brain organization and behavior is a central goal of neuroscience. Traditional teaching emphasizes that the human cerebrum includes many distinct areas for which damage or dysfunction would lead to a unique and specific behavioral syndrome. This teaching implies that brain areas correspond to encapsulated modules that are specialized for specific cognitive operations. However, empirically, local damage from stroke more often produces one of a small number of clusters of deficits and disrupts brain-wide connectivity in a small number of predictable ways (relative to the vast complexity of behavior and brain connectivity). Behaviors that involve shared operations show correlated deficits following a stroke, consistent with a low-dimensional behavioral space. Because of the networked organization of the brain, local damage from a stroke can result in widespread functional abnormalities, matching the low dimensionality of behavioral deficit. In alignment with this, neurological disease, psychiatric disease, and altered brain states produce behavioral changes that are highly correlated across a range of behaviors. We discuss how known structural and functional network priors in addition to graph theoretical concepts such as modularity and entropy have provided inroads to understanding this more complex relationship between brain and behavior. This model for brain disease has important implications for normal brain-behavior relationships and the treatment of neurological and psychiatric diseases.

Getting Fit to Counteract Cognitive Aging: Evidence and Future Directions

Physical activity has shown tremendous promise for counteracting cognitive aging, but also tremendous variability in cognitive benefits. We describe evidence for how exercise affects cognitive and brain aging, and whether cardiorespiratory fitness is a key factor. We highlight a brain network framework as a valuable paradigm for the mechanistic insight needed to tailor physical activity for cognitive benefits.

Simulated Attack Reveals How Lesions Affect Network Properties in Poststroke Aphasia

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.

Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

Most neuroimaging studies of post-stroke recovery rely on analyses derived from standard node-centric functional connectivity to map the distributed effects in stroke patients. Here, given the importance of nonlocal and diffuse damage, we use an edge-centric approach to functional connectivity in order to provide an alternative description of the effects of this disorder. These techniques allow for the rendering of metrics such as normalized entropy, which describes the diversity of edge communities at each node. Moreover, the approach enables the identification of high amplitude co-fluctuations in fMRI time series. We found that normalized entropy is associated with stroke lesion severity and continually increases across the time of patients' recovery. Furthermore, high amplitude co-fluctuations not only relate to the lesion severity but are also associated with patients' level of recovery. The current study is the first edge-centric application for a clinical population in a longitudinal dataset and demonstrates how a different perspective for functional data analysis can further characterize topographic modulations of brain dynamics.

Social cognitive network neuroscience

Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.

Cognitive outcome is related to functional thalamo-cortical connectivity after pediatric stroke

The thalamus has complex connections with the cortex and is involved in various cognitive processes. Despite increasing interest in the thalamus and the underlying thalamo-cortical interaction, little is known about thalamo-cortical connections after pediatric arterial ischemic stroke. Therefore, the aim of this study was to investigate thalamo-cortical connections and their association with cognitive performance after arterial ischemic stroke.

Twenty patients in the chronic phase after pediatric arterial ischemic stroke (≥ 2 years after diagnosis, diagnosed &lt;16 years; aged 5–23 years, mean 15.1 years) and twenty healthy controls matched for age and sex were examined in a cross-sectional study design. Cognitive performance (selective attention, inhibition, working memory, and cognitive flexibility) was evaluated using standardized neuropsychological tests. Resting-state functional magnetic resonance imaging was used to examine functional thalamo-cortical connectivity. Lesion masks were integrated in the preprocessing pipeline to ensure that structurally damaged voxels did not influence functional connectivity analyses.

Cognitive performance (selective attention, inhibition and working memory) was significantly reduced in patients compared to controls. Network analyses revealed significantly lower thalamo-cortical connectivity for the motor, auditory, visual, default mode network, salience, left/right executive and dorsal attention network in patients compared to controls. Interestingly, analyses revealed as well higher thalamo-cortical connectivity in some subdivisions of the thalamus for the default mode network (medial nuclei), motor (lateral nuclei), dorsal attention (anterior nuclei), and the left executive network (posterior nuclei) in patients compared to controls. Increased and decreased thalamo-cortical connectivity strength within the same networks was, however, found in different thalamic sub-divisions. Thus, alterations in thalamo-cortical connectivity strength after pediatric stroke seem to point in both directions, with stronger as well as weaker thalamo-cortical connectivity in patients compared to controls. Multivariate linear regression, with lesion size and age as covariates, revealed significant correlations between cognitive performance (selective attention, inhibition, and working memory) and the strength of thalamo-cortical connectivity in the motor, auditory, visual, default mode network, posterior default mode network, salience, left/right executive, and dorsal attention network after childhood stroke.

Our data suggest that the interaction between different sub-nuclei of the thalamus and several cortical networks relates to post-stroke cognition. The variability in cognitive outcomes after pediatric stroke might partly be explained by functional thalamo-cortical connectivity strength.

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

Causal mapping of human brain function

Mapping human brain function is a long-standing goal of neuroscience that promises to inform the development of new treatments for brain disorders. Early maps of human brain function were based on locations of brain damage or brain stimulation that caused a functional change. Over time, this approach was largely replaced by technologies such as functional neuroimaging, which identify brain regions in which activity is correlated with behaviours or symptoms. Despite their advantages, these technologies reveal correlations, not causation. This creates challenges for interpreting the data generated from these tools and using them to develop treatments for brain disorders. A return to causal mapping of human brain function based on brain lesions and brain stimulation is underway. New approaches can combine these causal sources of information with modern neuroimaging and electrophysiology techniques to gain new insights into the functions of specific brain areas. In this Review, we provide a definition of causality for translational research, propose a continuum along which to assess the relative strength of causal information from human brain mapping studies and discuss recent advances in causal brain mapping and their relevance for developing treatments.

Enhancing Cortical Network-level Participation Coefficient as a Potential Mechanism for Transfer in Cognitive Training in aMCI

Effective cognitive training must improve cognition beyond the trained domain (show a transfer effect) and be applicable to dementia-risk populations, e.g., amnesic mild cognitive impairment (aMCI). Theories suggest training should target processes that 1) show robust engagement, 2) are domain-general, and 3) reflect long-lasting changes in brain organization. Brain regions that connect to many different networks (i.e., show high participation coefficient; PC) are known to support integration. This capacity is 1) relatively preserved in aMCI, 2) required across a wide range of cognitive domains, and 3) trait-like. In 49 individuals with aMCI that completed a 6-week visual speed of processing training (VSOP) and 28 active controls, enhancement in PC was significantly more related to transfer to working memory at global and network levels in VSOP compared to controls, particularly in networks with many high-PC nodes. This suggests that enhancing brain integration may provide a target for developing effective cognitive training.

Using causal methods to map symptoms to brain circuits in neurodevelopment disorders: moving from identifying correlates to developing treatments

A wide variety of model systems and experimental techniques can provide insight into the structure and function of the human brain in typical development and in neurodevelopmental disorders. Unfortunately, this work, whether based on manipulation of animal models or observational and correlational methods in humans, has a high attrition rate in translating scientific discovery into practicable treatments and therapies for neurodevelopmental disorders.With new computational and neuromodulatory approaches to interrogating brain networks, opportunities exist for "bedside-to bedside-translation" with a potentially shorter path to therapeutic options. Specifically, methods like lesion network mapping can identify brain networks involved in the generation of complex symptomatology, both from acute onset lesion-related symptoms and from focal developmental anomalies. Traditional neuroimaging can examine the generalizability of these findings to idiopathic populations, while non-invasive neuromodulation techniques such as transcranial magnetic stimulation provide the ability to do targeted activation or inhibition of these specific brain regions and networks. In parallel, real-time functional MRI neurofeedback also allow for endogenous neuromodulation of specific targets that may be out of reach for transcranial exogenous methods.Discovery of novel neuroanatomical circuits for transdiagnostic symptoms and neuroimaging-based endophenotypes may now be feasible for neurodevelopmental disorders using data from cohorts with focal brain anomalies. These novel circuits, after validation in large-scale highly characterized research cohorts and tested prospectively using noninvasive neuromodulation and neurofeedback techniques, may represent a new pathway for symptom-based targeted therapy.

Network controllability mediates the relationship between rigid structure and flexible dynamics

Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid interareal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain's structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region's functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes' boundary controllability, suggesting that a region's strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.

Selectively disrupted sensorimotor circuits in chronic stroke with hand dysfunction

Aim

To investigate the directional and selective disconnection of the sensorimotor cortex (SMC) subregions in chronic stroke patients with hand dysfunction.

Methods

We mapped the resting‐state fMRI effective connectivity (EC) patterns for seven SMC subregions in each hemisphere of 65 chronic stroke patients and 40 healthy participants and correlated these patterns with paretic hand performance.

Results

Compared with controls, patients demonstrated disrupted EC in the ipsilesional primary motor cortex_4p, ipsilesional primary somatosensory cortex_2 (PSC_2), and contralesional PSC_3a. Moreover, we found that EC values of the contralesional PSC_1 to contralesional precuneus, the ipsilesional inferior temporal gyrus to ipsilesional PSC_1, and the ipsilesional PSC_1 to contralesional postcentral gyrus were correlated with paretic hand performance across all patients. We further divided patients into partially (PPH) and completely (CPH) paretic hand subgroups. Compared with CPH patients, PPH patients demonstrated decreased EC in the ipsilesional premotor_6 and ipsilesional PSC_1. Interestingly, we found that paretic hand performance was positively correlated with seven sensorimotor circuits in PPH patients, while it was negatively correlated with five sensorimotor circuits in CPH patients.

Conclusion

SMC neurocircuitry was selectively disrupted after chronic stroke and associated with diverse hand outcomes, which deepens the understanding of SMC reorganization.

Loosening of Associations in Chronic Schizophrenia: Intersectionality of Verbal Learning, Negative Symptoms, and Brain Structure

In 1908, Bleuler proposed a unitary theory of schizophrenia, hypothesizing a “loosening of associations” as the central mechanism underlying disturbances in thinking, motivation, and affective expression. Here, we test Bleuler’s model in an archival sample of 79 healthy controls and 76 patients with chronic schizophrenia who had completed neuropsychological tests, including a measure of learning of novel word pairs, which was specifically selected to probe the structure and formation of new verbal associations. The patients also had positive and negative symptoms ratings, including measures of flat affect, anhedonia, and thought disorder. A subset of patients and controls (n = 39) had available prior archival 3-T magnetic resonance imaging (MRI) measures of prefrontal cortex (PFC) gray matter volumes. In relation to controls, patients showed evidence of a selective impairment in associative learning, independent of their overall reduced neuropsychological functioning. This neuropsychological impairment, in turn, correlated significantly with overall levels of negative but not positive symptoms, with the data showing an especially strong contribution of flattened emotional expression to verbal associate learning deficits in this patient sample. Moreover, the archival MRI data were consistent with prior research pointing to an important role of the PFC in supporting verbal associate learning and memory in patients and controls. Taken together, the current results provided evidence of a selective impairment in schizophrenia on a PFC-supported verbal associate learning and memory task, which was accompanied by negative symptoms in general, and flattened emotional expression, in particular.

Static and Dynamic Topological Organizations of the Costly Punishment Network Predict Individual Differences in Punishment Propensity

Human costly punishment plays a vital role in maintaining social norms. Recently, a brain network model is conceptually proposed indicating that the implement of costly punishment depends on a subset of nodes in three high-level networks. This model, however, has not yet been empirically examined from an integrated perspective of large-scale brain networks. Here, we conducted comprehensive graph-based network analyses of resting-state functional magnetic resonance imaging data to explore system-level characteristics of intrinsic functional connectivity among 18 regions related to costly punishment. Nontrivial organizations (small-worldness, connector hubs, and high flexibility) were found that were qualitatively stable across participants and over time but quantitatively exhibited low test-retest reliability. The organizations were predictive of individual costly punishment propensities, which was reproducible on independent samples and robust against different analytical strategies and parameter settings. Moreover, the prediction was specific to system-level network organizations (rather than interregional functional connectivity) derived from positive (rather than negative or combined) connections among the specific (rather than randomly chosen) subset of regions from the three high-order (rather than primary) networks. Collectively, these findings suggest that human costly punishment emerges from integrative behaviors among specific regions in certain functional networks, lending support to the brain network model for costly punishment.

Added Value of Cognition in the Prediction of Survival in Low and High Grade Glioma

Background: Diffuse gliomas, which are at WHO grade II-IV, are progressive primary brain tumors with great variability in prognosis. Our aim was to investigate whether pre-operative cognitive functioning is of added value in survival prediction in these patients.

Methods: In a retrospective cohort study of patients undergoing awake craniotomy between 2010 and 2019 we performed pre-operative neuropsychological assessments in five cognitive domains. Their added prognostic value on top of known prognostic factors was assessed in two patient groups [low- (LGG) and high-grade gliomas (HGG]). We compared Cox proportional hazards regression models with and without the cognitive domain by means of loglikelihood ratios tests (LRT), discriminative performance measures (by AUC), and risk classification [by Integrated Discrimination Index (IDI)].

Results: We included 109 LGG and 145 HGG patients with a median survival time of 1,490 and 511 days, respectively. The domain memory had a significant added prognostic value in HGG as indicated by an LRT (p-value = 0.018). The cumulative AUC for HGG with memory included was.78 (SD = 0.017) and without cognition 0.77 (SD = 0.018), IDI was 0.043 (0.000–0.102). In LGG none of the cognitive domains added prognostic value.

Conclusions: Our findings indicated that memory deficits, which were revealed with the neuropsychological examination, were of additional prognostic value in HGG to other well-known predictors of survival.

Cortical network modularity changes along the course of frontotemporal and Alzheimer's dementing diseases

Cortical network modularity underpins cognitive functions, so we hypothesized its progressive derangement along the course of frontotemporal (FTD) and Alzheimer's (AD) dementing diseases. EEG was recorded in 18 FTD, 18 AD, and 20 healthy controls (HC). In the FTD and AD patients, the EEG recordings were performed at the prodromal stage of dementia, at the onset of dementia, and three years after the onset of dementia. HC underwent three EEG recordings at 2-3-year time interval. Information flows underlying EEG activity recorded at electrode pairs were estimated by means of Mutual Information (MI) analysis. The functional organization of the cortical network was modelled by means of the Graph theory analysis on MI adjacency matrices. Graph theory analysis showed that the main hub of HC (Parietal area) was lost in FTD patients at onset of dementia, substituted by provincial hubs in frontal leads. No changes in global network organization were found in AD. Despite a progressive cognitive impairment during the FTD and AD progression, only the FTD patients showed a derangement in the cortical network modularity, possibly due to dysfunctions in frontal functional connectivity.

Cortical autonomic network connectivity predicts symptoms in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) represents a significant public health challenge given the presence of many unexplained patient symptoms. Research has shown that many features in ME/CFS may result from a dysfunctional autonomic nervous system (ANS). We explored the role of the cortical autonomic network (CAN) involved in higher-order control of ANS functioning in 34 patients with ME/CFS and 34 healthy controls under task-free conditions. All participants underwent resting-state quantitative electroencephalographic (qEEG) scalp recordings during an eyes-closed condition. Source analysis was performed using exact low-resolution electromagnetic tomography (eLORETA), and lagged coherence was used to estimate intrinsic functional connectivity between each node across 7 frequency bands: delta (1-3 Hz), theta (4-7 Hz), alpha-1 (8-10 Hz), alpha-2 (10-12 Hz), beta-1 (13-18 Hz), beta-2 (19-21 Hz), and beta-3 (22-30 Hz). Symptom ratings were measured using the DePaul Symptom Questionnaire and the Short Form (SF-36) health survey. Graph theoretical analysis of weighted, undirected connections revealed significant group differences in baseline CAN organization. Regression results showed that cognitive, affective, and somatomotor symptom cluster ratings were associated with alteration to CAN topology in patients, depending on the frequency band. These findings provide evidence for reduced higher-order homeostatic regulation and adaptability in ME/CFS. If confirmed, these findings address the CAN as a potential therapeutic target for managing patient symptoms.

Neuropsychological evidence of multi-domain network hubs in the human thalamus

Hubs in the human brain support behaviors that arise from brain network interactions. Previous studies have identified hub regions in the human thalamus that are connected with multiple functional networks. However, the behavioral significance of thalamic hubs has yet to be established. Our framework predicts that thalamic subregions with strong hub properties are broadly involved in functions across multiple cognitive domains. To test this prediction, we studied human patients with focal thalamic lesions in conjunction with network analyses of the human thalamocortical functional connectome. In support of our prediction, lesions to thalamic subregions with stronger hub properties were associated with widespread deficits in executive, language, and memory functions, whereas lesions to thalamic subregions with weaker hub properties were associated with more limited deficits. These results highlight how a large-scale network model can broaden our understanding of thalamic function for human cognition.

Long-term health-related quality of life and neurocognitive functioning after treatment in skull base meningioma patients

OBJECTIVE

Patients with skull base meningioma (SBM) often require complex surgery around critical neurovascular structures, placing them at high risk of poor health-related quality of life (HRQOL) and possibly neurocognitive dysfunction. As the survival of meningioma patients is near normal, long-term neurocognitive and HRQOL outcomes are important to evaluate, including evaluation of the impact of specific tumor location and treatment modalities on these outcomes.

METHODS

In this multicenter cross-sectional study including patients 5 years or more after their last tumor intervention, Short-Form Health Survey (SF-36) and European Organisation for Research and Treatment of Cancer (EORTC) QLQ-BN20 questionnaires were used to assess generic and disease-specific HRQOL. Neurocognitive functioning was assessed with standardized neuropsychological assessment. SBM patient assessments were compared with those of 1) informal caregivers of SBM patients who served as controls and 2) convexity meningioma patients. In addition, the authors compared anterior/middle SBM patients with posterior SBM patients and anterior/middle and posterior SBM patients separately with controls. Multivariable and propensity score regression analyses were performed to correct for possible confounders.

RESULTS

Patients with SBM (n = 89) with a median follow-up of 9 years after the last intervention did not significantly differ from controls (n = 65) or convexity meningioma patients (n = 84) on generic HRQOL assessment. Statistically significantly but not clinically relevantly better disease-specific HRQOL was found for SBM patients compared with convexity meningioma patients. Anterior/middle SBM patients (n = 62) had significantly and clinically relevantly better HRQOL in SF-36 and EORTC QLQ-BN20 scores than posterior SBM patients (n = 27): physical role functioning (corrected difference 17.1, 95% CI 0.2-34.0), motor dysfunction (-10.1, 95% CI -17.5 to -2.7), communication deficit (-14.2, 95% CI -22.7 to -5.6), and weakness in both legs (-10.1, 95% CI -18.8 to -1.5). SBM patients whose primary treatment was radiotherapy had lower HRQOL scores compared with SBM patients who underwent surgery on two domains: bodily pain (-33.0, 95% CI -55.2 to -10.9) and vitality (-18.9. 95% CI -33.7 to -4.1). Tumor location and treatment modality did not result in significant differences in neurocognitive functioning, although 44% of SBM patients had deficits in at least one domain.

CONCLUSIONS

In the long term, SBM patients do not experience significantly more sequelae in HRQOL and neurocognitive functioning than do controls or patients with convexity meningioma. Patients with posterior SBM had poorer HRQOL than anterior/middle SBM patients, and primary treatment with radiotherapy was associated with worse HRQOL. Neurocognitive functioning was not affected by tumor location or treatment modality.

Monthlong Intubated Patient with Life-Threatening COVID-19 and Cerebral Microbleeds Suffers Only Mild Cognitive Sequelae at 8-Month Follow-up: A Case Report

OBJECTIVE

To elaborate on possible cognitive sequelae related to COVID-19, associated cerebrovascular injuries as well as the general consequences from intensive care. COVID-19 is known to have several, serious CNS-related consequences, but neuropsychological studies of severe COVID-19 are still rare.

METHODS

M., a 45-year-old man, who survived a severe COVID-19 disease course including Acute Respiratory Distress Syndrome (ARDS), cerebral microbleeds, and 35 days of mechanical ventilation, is described. We elaborate on M's recovery and rehabilitation process from onset to the 8-month follow-up. The cognitive functions were evaluated with a comprehensive screening battery at 4 weeks after extubation and at the 8-month follow-up.

RESULTS

Following extubation, M. was delirious, reported visual hallucinations, and had severe sleeping difficulties. At about 3 months after COVID-19 onset, M. showed mild to moderate deficits on tests measuring processing speed, working memory, and attention. At assessments at 8 months, M. performed better, with results above average on tests measuring learning, memory, word fluency, and visuospatial functions. Minor deficits were still found regarding logical reasoning, attention, executive functioning, and processing speed. There were no lingering psychiatric symptoms. While M. had returned to a part-time job, he was not able to resume previous work-tasks.

CONCLUSION

This case-study demonstrates possible cognitive deficits after severe COVID-19 and emphasizes the need of a neuropsychological follow-up, with tests sensitive to minor deficits. The main findings of this report provide some support that the long-term prognosis for cognition in severe COVID-19 may be hopeful.

Long-term brain network reorganization predicts responsive neurostimulation outcomes for focal epilepsy

Responsive neurostimulation (RNS) devices, able to detect imminent seizures and to rapidly deliver electrical stimulation to the brain, are effective in reducing seizures in some patients with focal epilepsy. However, therapeutic response to RNS is often slow, is highly variable, and defies prognostication based on clinical factors. A prevailing view holds that RNS efficacy is primarily mediated by acute seizure termination; yet, stimulations greatly outnumber seizures and occur mostly in the interictal state, suggesting chronic modulation of brain networks that generate seizures. Here, using years-long intracranial neural recordings collected during RNS therapy, we found that patients with the greatest therapeutic benefit undergo progressive, frequency-dependent reorganization of interictal functional connectivity. The extent of this reorganization scales directly with seizure reduction and emerges within the first year of RNS treatment, enabling potential early prediction of therapeutic response. Our findings reveal a mechanism for RNS that involves network plasticity and may inform development of next-generation devices for epilepsy.

Improved neuropathological identification of traumatic brain injury through quantitative neuroimaging and neural network analyses: Some practical approaches for the neurorehabilitation clinician

BACKGROUND

Quantitative neuroimaging analyses have the potential to provide additional information about the neuropathology of traumatic brain injury (TBI) that more thoroughly informs the neurorehabilitation clinician.

OBJECTIVE

Quantitative neuroimaging is typically not covered in the standard radiological report, but often can be extracted via post-processing of clinical neuroimaging studies, provided that the proper volume acquisition sequences were originally obtained.

METHODS

Research and commercially available quantitative neuroimaging methods provide region of interest (ROI) quantification metrics, lesion burden volumetrics and cortical thickness measures, degree of focal encephalomalacia, white matter (WM) abnormalities and residual hemorrhagic pathology. If present, diffusion tensor imaging (DTI) provides a variety of techniques that aid in evaluating WM integrity. Using quantitatively identified structural and ROI neuropathological changes are most informative when done from a neural network approach.

RESULTS

Viewing quantitatively identifiable damage from a neural network perspective provides the neurorehabilitation clinician with an additional tool for linking brain pathology to understand symptoms, problems and deficits as well as aid neuropsychological test interpretation. All of these analyses can be displayed in graphic form, including3-D image analysis. A case study approach is used to demonstrate the utility of quantitative neuroimaging and network analyses in TBI.

CONCLUSIONS

Quantitative neuroimaging may provide additional useful information for the neurorehabilitation clinician.