Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants.

Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.

The relationship between pathological brain activity and functional network connectivity in glioma patients.

PURPOSE: Glioma is associated with pathologically high (peri)tumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression. METHODS: We obtained magnetoencephalography in 84 de novo glioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients' regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient (local connectivity) and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between (peri)tumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity. RESULTS: As expected, patients' (peri)tumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with 'normal' activity levels showed high network clustering. CONCLUSION: Our results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.

Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis.

BACKGROUND: Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS). OBJECTIVE: This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS. METHODS: Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time. RESULTS: In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors). CONCLUSION: Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.

Regional healthy brain activity, glioma occurrence and symptomatology.

It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients' tumour locations relates to tumour and patient characteristics. Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients' individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics. As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients' individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients. In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients' individual tumour locations may capture both tumour biology and patients' performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of 'cancer neuroscience'.

Cellular Substrates of Functional Network Integration and Memory in Temporal Lobe Epilepsy.

Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of drug-resistant TLE patients (n = 31), who underwent pseudo resting-state functional magnetic resonance imaging (fMRI), resting-state magnetoencephalography (MEG) and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.

Cortical thickness and neurocognitive performance in former high-level female soccer and non-contact sport athletes.

BACKGROUND: Long-term effects of playing soccer (football) on the brain structure and function of the brain are vividly debated. While some studies showed differences in neurocognitive performance and structural brain changes in retired male players, data on female players are scarce. The present study compares cortical thickness and neurocognitive performance in former high-level female soccer (SOC) and non-contact sport athletes (CON). METHODS: 3 T T1-weighted 3D MPRAGE MRI was performed, and vertex-wise cortical thickness was analyzed using FreeSurfer (v. 6.0.0). Neurocognitive performance in seven domains of SOC and CON was assessed. A multivariate linear model was used to analyze interactions with respect to heading frequency and a history of concussion. RESULTS: SOC (n = 15, mean age 38.3 ± 5.1 years) and CON (n = 16, mean age 36.6 ± 5.8 years) had a similar cortical thickness and performed similarly in the neurocognitive tests except for verbal memory and psychomotor speed, where SOC performed significantly worse than CON. Moderate headers had a significantly larger cortical thickness than rare headers in the right inferior parietal region. Visual memory and cortical thickness were positively correlated in the group of frequent headers and negatively correlated in CON, but not in the other header groups. PERSPECTIVE: In contrast to previous reports in male soccer players, female players did not reveal cortical thinning in comparison with control athletes, whereas neurocognitive profiles of female soccer players might not significantly differ from male athletes. Small sample sizes, subjective header assessment, and the case-control study design require a cautious interpretation.

Connecting Neurobiological Features with Interregional Dysconnectivity in Social-Cognitive Impairments of Schizophrenia.

Schizophrenia (SZ) is a devastating psychiatric disorder affecting about 1% of the world's population. Social-cognitive impairments in SZ prevent positive social interactions and lead to progressive social withdrawal. The neurobiological underpinnings of social-cognitive symptoms remain poorly understood, which hinders the development of novel treatments. At the whole-brain level, an abnormal activation of social brain regions and interregional dysconnectivity within social-cognitive brain networks have been identified as major contributors to these symptoms. At the cellular and subcellular levels, an interplay between oxidative stress, neuroinflammation and N-methyl-D-aspartate receptor hypofunction is thought to underly SZ pathology. However, it is not clear how these molecular processes are linked with interregional dysconnectivity in the genesis of social-cognitive symptoms. Here, we aim to bridge the gap between macroscale (connectivity analyses) and microscale (molecular and cellular mechanistic) knowledge by proposing impaired myelination and the disinhibition of local microcircuits as possible causative biological pathways leading to dysconnectivity and abnormal activity of the social brain. Furthermore, we recommend electroencephalography as a promising translational technique that can foster pre-clinical drug development and discuss attractive drug targets for the treatment of social-cognitive symptoms in SZ.

Effects of a single-dose methylphenidate challenge on resting-state functional connectivity in stimulant-treatment naive children and adults with ADHD.

Prior studies suggest that methylphenidate, the primary pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD), alters functional brain connectivity. As the neurotransmitter systems targeted by methylphenidate undergo significant alterations throughout development, the effects of methylphenidate on functional connectivity may also be modulated by age. Therefore, we assessed the effects of a single methylphenidate challenge on brain network connectivity in stimulant-treatment naïve children and adults with ADHD. We obtained resting-state functional MRI from 50 boys (10-12 years of age) and 49 men (23-40 years of age) with ADHD (DSM IV, all subtypes), before and after an oral challenge with 0.5 mg/kg methylphenidate; and from 11 boys and 12 men as typically developing controls. Connectivity strength (CS), eigenvector centrality (EC), and betweenness centrality (BC) were calculated for the striatum, thalamus, dorsal anterior cingulate cortex (dACC), and prefrontal cortex (PFC). In line with our hypotheses, we found that methylphenidate decreased measures of connectivity and centrality in the striatum and thalamus in children with ADHD, but increased the same metrics in adults with ADHD. Surprisingly, we found no major effects of methylphenidate in the dACC and PFC in either children or adults. Interestingly, pre-methylphenidate, participants with ADHD showed aberrant connectivity and centrality compared to controls predominantly in frontal regions. Our findings demonstrate that methylphenidate's effects on connectivity of subcortical regions are age-dependent in stimulant-treatment naïve participants with ADHD, likely due to ongoing maturation of dopamine and noradrenaline systems. These findings highlight the importance for future studies to take a developmental perspective when studying the effects of methylphenidate treatment.

Fatigue and resting-state functional brain networks in breast cancer patients treated with chemotherapy.

PURPOSE: This longitudinal study aimed to disentangle the impact of chemotherapy on fatigue and hypothetically associated functional brain network alterations. METHODS: In total, 34 breast cancer patients treated with chemotherapy (BCC +), 32 patients not treated with chemotherapy (BCC -), and 35 non-cancer controls (NC) were included. Fatigue was assessed using the EORTC QLQ-C30 fatigue subscale at two time points: baseline (T1) and six months after completion of chemotherapy or matched intervals (T2). Participants also underwent resting-state functional magnetic resonance imaging (rsfMRI). An atlas spanning 90 cortical and subcortical brain regions was used to extract time series, after which Pearson correlation coefficients were calculated to construct a brain network per participant per timepoint. Network measures of local segregation and global integration were compared between groups and timepoints and correlated with fatigue. RESULTS: As expected, fatigue increased over time in the BCC + group (p = 0.025) leading to higher fatigue compared to NC at T2 (p = 0.023). Meanwhile, fatigue decreased from T1 to T2 in the BCC - group (p = 0.013). The BCC + group had significantly lower local efficiency than NC at T2 (p = 0.033), while a negative correlation was seen between fatigue and local efficiency across timepoints and all participants (T1 rho = - 0.274, p = 0.006; T2 rho = - 0.207, p = 0.039). CONCLUSION: Although greater fatigue and lower local functional network segregation co-occur in breast cancer patients after chemotherapy, the relationship between the two generalized across participant subgroups, suggesting that local efficiency is a general neural correlate of fatigue.

The cerebellum and its network: Disrupted static and dynamic functional connectivity patterns and cognitive impairment in multiple sclerosis.

BACKGROUND: The impact of cerebellar damage and (dys)function on cognition remains understudied in multiple sclerosis. OBJECTIVE: To assess the cognitive relevance of cerebellar structural damage and functional connectivity (FC) in relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS). METHODS: This study included 149 patients with early RRMS, 81 late RRMS, 48 SPMS and 82 controls. Cerebellar cortical imaging included fractional anisotropy, grey matter volume and resting-state functional magnetic resonance imaging (MRI). Cerebellar FC was assessed with literature-based resting-state networks, using static connectivity (that is, conventional correlations), and dynamic connectivity (that is, fluctuations in FC strength). Measures were compared between groups and related to disability and cognition. RESULTS: Cognitive impairment (CI) and cerebellar damage were worst in SPMS. Only SPMS showed cerebellar connectivity changes, compared to early RRMS and controls. Lower static FC was seen in fronto-parietal and default-mode networks. Higher dynamic FC was seen in dorsal and ventral attention, default-mode and deep grey matter networks. Cerebellar atrophy and higher dynamic FC together explained 32% of disability and 24% of cognitive variance. Higher dynamic FC was related to working and verbal memory and to information processing speed. CONCLUSION: Cerebellar damage and cerebellar connectivity changes were most prominent in SPMS and related to worse CI.

Functional brain network organization measured with magnetoencephalography predicts cognitive decline in multiple sclerosis.

BACKGROUND: Cognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients. OBJECTIVE: To investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology. METHODS: Resting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage. RESULTS: A more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years (Radj2=15%), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band. CONCLUSIONS: The level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS.

Long-range connections are more severely damaged and relevant for cognition in multiple sclerosis.

An efficient network such as the human brain features a combination of global integration of information, driven by long-range connections, and local processing involving short-range connections. Whether these connections are equally damaged in multiple sclerosis is unknown, as is their relevance for cognitive impairment and brain function. Therefore, we cross-sectionally investigated the association between damage to short- and long-range connections with structural network efficiency, the functional connectome and cognition. From the Amsterdam multiple sclerosis cohort, 133 patients (age = 54.2 ± 9.6) with long-standing multiple sclerosis and 48 healthy controls (age = 50.8 ± 7.0) with neuropsychological testing and MRI were included. Structural connectivity was estimated from diffusion tensor images using probabilistic tractography (MRtrix 3.0) between pairs of brain regions. Structural connections were divided into short- (length < quartile 1) and long-range (length > quartile 3) connections, based on the mean distribution of tract lengths in healthy controls. To determine the severity of damage within these connections, (i) fractional anisotropy as a measure for integrity; (ii) total number of fibres; and (iii) percentage of tract affected by lesions were computed for each connecting tract and averaged for short- and long-range connections separately. To investigate the impact of damage in these connections for structural network efficiency, global efficiency was computed. Additionally, resting-state functional connectivity was computed between each pair of brain regions, after artefact removal with FMRIB's ICA-based X-noiseifier. The functional connectivity similarity index was computed by correlating individual functional connectivity matrices with an average healthy control connectivity matrix. Our results showed that the structural network had a reduced efficiency and integrity in multiple sclerosis relative to healthy controls (both P < 0.05). The long-range connections showed the largest reduction in fractional anisotropy (z = -1.03, P < 0.001) and total number of fibres (z = -0.44, P < 0.01), whereas in the short-range connections only fractional anisotropy was affected (z = -0.34, P = 0.03). Long-range connections also demonstrated a higher percentage of tract affected by lesions than short-range connections, independent of tract length (P < 0.001). Damage to long-range connections was more strongly related to structural network efficiency and cognition (fractional anisotropy: r = 0.329 and r = 0.447. number of fibres r = 0.321 and r = 0.278. and percentage of lesions: r = -0.219; r = -0.426, respectively) than damage to short-range connections. Only damage to long-distance connections correlated with a more abnormal functional network (fractional anisotropy: r = 0.226). Our findings indicate that long-range connections are more severely affected by multiple sclerosis-specific damage than short-range connections. Moreover compared to short-range connections, damage to long-range connections better explains network efficiency and cognition.

Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches.

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. â€‹Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks.

Resting-state and task-based centrality of dorsolateral prefrontal cortex predict resilience to 1 Hz repetitive transcranial magnetic stimulation.

Repetitive transcranial magnetic stimulation (rTMS) is used to investigate normal brain function in healthy participants and as a treatment for brain disorders. Various subject factors can influence individual response to rTMS, including brain network properties. A previous study by our group showed that "virtually lesioning" the left dorsolateral prefrontal cortex (dlPFC; important for cognitive flexibility) using 1 Hz rTMS reduced performance on a set-shifting task. We aimed to determine whether this behavioural response was related to topological features of pre-TMS resting-state and task-based functional networks. 1 Hz (inhibitory) rTMS was applied to the left dlPFC in 16 healthy participants, and to the vertex in 17 participants as a control condition. Participants performed a set-shifting task during fMRI at baseline and directly after a single rTMS session 1-2 weeks later. Functional network topology measures were calculated from resting-state and task-based fMRI scans using graph theoretical analysis. The dlPFC-stimulated group, but not the vertex group, showed reduced setshifting performance after rTMS, associated with lower task-based betweenness centrality (BC) of the dlPFC at baseline (p = .030) and a smaller reduction in task-based BC after rTMS (p = .024). Reduced repeat trial accuracy after rTMS was associated with higher baseline resting state node strength of the dlPFC (p = .017). Our results suggest that behavioural response to 1 Hz rTMS to the dlPFC is dependent on baseline functional network features. Individuals with more globally integrated stimulated regions show greater resilience to rTMS effects, while individuals with more locally well-connected regions show greater vulnerability.

Contralateral Preoperative Resting-State Functional MRI Network Integration Is Associated with Surgical Outcome in Temporal Lobe Epilepsy.

Background Although most patients with medically refractory temporal lobe epilepsy (TLE) experience seizure freedom after anterior temporal lobectomy, approximately 40% may continue to have seizures. Functional network integration, as measured with preoperative resting-state functional MRI, may help stratify patients who are more likely to experience postoperative seizure freedom. Purpose To relate preoperative resting-state functional MRI and surgical outcome in patients with medically refractory TLE. Materials and Methods Data from patients with medically intractable TLE were retrospectively analyzed. Patients underwent preoperative resting-state functional MRI between March 2010 and April 2013 and subsequent unilateral anterior temporal lobectomy. Postoperative seizure-free status was categorized using the Engel Epilepsy Surgery Outcome Scale. Global and regional resting-state functional MRI network properties on preoperative functional MRI scans related to integration were calculated and statistically compared between patients who experienced complete postoperative seizure freedom (Engel class IA) and all others (Engel class IB to class IV) using t tests and multiple logistic regression. Results Forty patients (mean age, 34 years ± 15 [standard deviation]; 21 female) were evaluated. Preoperative global network integration was different (P = .01) between patients who experienced seizure freedom after surgery and all other patients, with 9% lower leaf fraction and 10% lower tree hierarchy in patients with ongoing seizures. Preoperative regional network integration in the contralateral temporoinsular region was different (P = .04) between patients in these two groups. Specifically, the group-level leaf proportion was 59% lower in the entorhinal cortex, 73% lower in the inferior temporal gyrus, 43% lower in the temporal pole, and 69% lower in the insula in patients with ongoing seizures after surgery. When using multivariate regression, contralateral temporoinsular leaf proportion (P = .002) and epilepsy duration (P = .04) were predictive of postoperative seizure freedom, while age (P > .70) and age at seizure onset (P > .50) were not. Conclusion Lower network integration globally and involving the contralateral temporoinsular cortex on preoperative resting-state functional MRI scans is associated with ongoing postoperative seizures in patients with temporal lobe epilepsy. © RSNA, 2020.

Postoperative oscillatory brain activity as an add-on prognostic marker in diffuse glioma.

INTRODUCTION: Progression-free survival (PFS) in glioma patients varies widely, even when stratifying for known predictors (i.e. age, molecular tumor subtype, presence of epilepsy, tumor grade and Karnofsky performance status). Neuronal activity has been shown to accelerate tumor growth in an animal model, suggesting that brain activity may be valuable as a PFS predictor. We investigated whether postoperative oscillatory brain activity, assessed by resting-state magnetoencephalography is of additional value when predicting PFS in glioma patients. METHODS: We included 27 patients with grade II-IV gliomas. Each patient's oscillatory brain activity was estimated by calculating broadband power (0.5-48 Hz) in 56 epochs of 3.27 s and averaged over 78 cortical regions of the Automated Anatomical Labeling atlas. Cox proportional hazard analysis was performed to test the predictive value of broadband power towards PFS, adjusting for known predictors by backward elimination. RESULTS: Higher broadband power predicted shorter PFS after adjusting for known prognostic factors (n = 27; HR 2.56 (95% confidence interval (CI) 1.15-5.70); p = 0.022). Post-hoc univariate analysis showed that higher broadband power also predicted shorter overall survival (OS; n = 38; HR 1.88 (95% CI 1.00-3.54); p = 0.038). CONCLUSIONS: Our findings suggest that postoperative broadband power is of additional value in predicting PFS beyond already known predictors.

Dynamic connectivity modulates local activity in the core regions of the default-mode network.

Segregation and integration are distinctive features of large-scale brain activity. Although neuroimaging studies have been unraveling their neural correlates, how integration takes place over segregated modules remains elusive. Central to this problem is the mechanism by which a brain region adjusts its activity according to the influence it receives from other regions. In this study, we explore how dynamic connectivity between two regions affects the neural activity within a participating region. Combining functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) in the same group of subjects, we analyzed resting-state data from the core of the default-mode network. We observed directed influence from the posterior cingulate cortex (PCC) to the anterior cingulate cortex (ACC) in the 10-Hz range. This time-varying influence was associated with the power alteration in the ACC: strong influence corresponded with a decrease of power around 13-16 Hz and an increase of power in the lower (1-7 Hz) and higher (30-55 Hz) ends of the spectrum. We also found that the amplitude of the 30- to 55-Hz activity was coupled to the phase of the 3- to 4-Hz activity in the ACC. These results characterized the local spectral changes associated with network interactions. The specific spectral information both highlights the functional roles of PCC-ACC connectivity in the resting state and provides insights into the dynamic relationship between local activity and coupling dynamics of a network.

Reduced Network Dynamics on Functional MRI Signals Cognitive Impairment in Multiple Sclerosis.

Background Previous studies have demonstrated extensive functional network disturbances in patients with multiple sclerosis (MS), showing a less efficient brain network. Recent studies indicate that the dynamic properties of the brain network show a strong correlation with cognitive function. Purpose To investigate network dynamics on functional MRI in cognitively impaired patients with MS. Materials and Methods In secondary analysis of prospectively acquired data, with imaging performed between 2008 and 2012, differences in regional functional network dynamics (ie, eigenvector centrality dynamics) between cognitively impaired and cognitively preserved participants with MS were investigated. Functional network dynamics were computed on images from functional MRI (3 T) by using a sliding-window approach. Cognitively impaired and preserved groups were compared by using a clusterwise permutation-based method. Results The study included 96 healthy control subjects and 332 participants with MS (including 226 women and 106 men; median age, 48.1 years ± 11.0). Among the 332 participants with MS, 87 were cognitively impaired and 180 had preserved cognitive function; mildly impaired patients (n = 65) were excluded. The cognitively impaired group included a higher proportion of men compared with the cognitively preserved group (35 of 87 [40%] vs 48 of 180 [27%], respectively; P = .02) and had a higher mean age (51.1 years vs 46.3 years, respectively; P < .01). The clusterwise permutation-based comparison at P less than .05 showed reduced centrality dynamics in default-mode, frontoparietal, and visual network regions on functional MRI in cognitively impaired participants versus cognitively preserved participants. A subsequent correlation and hierarchical clustering analysis revealed that the default-mode and visual networks normally demonstrate negatively correlated fluctuations in functional importance (r = -0.23 in healthy control subjects), with an almost complete loss of this negative correlation in cognitively impaired participants compared with cognitively preserved participants (r = -0.04 vs r = -0.14; corrected P = .02). Conclusion As shown on functional MRI, cognitively impaired patients with multiple sclerosis not only demonstrate reduced dynamics in default-mode, frontoparietal, and visual networks, but also show a loss of interplay between default-mode and visual networks. © RSNA, 2019 Online supplemental material is available for this article. See also the article by Eijlers et al and the editorial by Zivadinov and Dwyer in this issue.

Association between tumor location and neurocognitive functioning using tumor localization maps.

INTRODUCTION: Patients with diffuse glioma often experience neurocognitive impairment already prior to surgery. Pertinent information on whether damage to a specific brain region due to tumor activity results in neurocognitive impairment or not, is relevant in clinical decision-making, and at the same time renders unique information on brain lesion location and functioning relationships. To examine the impact of tumor location on preoperative neurocognitive functioning (NCF), we performed MRI based lesion-symptom mapping. METHODS: Seventy-two patients (mean age 40 years) with a radiologically suspected glioma were recruited preoperatively. For each of the six cognitive domains tested, we used tumor localization maps and voxel-based lesion-symptom mapping analyses to identify cortical and subcortical regions associated with NCF impairment. RESULTS: Compared to healthy controls, preoperative NCF was significantly impaired in all cognitive domains. Most frequently affected were attention (30% of patients) and working memory (20% of patients). Deficits in attention were significantly associated with regions in the left frontal and parietal cortex, including the precentral and parietal-opercular cortex, and in left-sided subcortical fiber tracts, including the arcuate fasciculus and corticospinal tract. Surprisingly, no regions could be related to working memory capacity. For the other neurocognitive domains, impairments were mainly associated with regions in the left hemisphere. CONCLUSIONS: Prior to treatment, patients with diffuse glioma in the left hemisphere run the highest risk to have NCF deficits. Identification of a left frontoparietal network involved in NCF not only may optimize surgical procedures but may also be integrated in counseling and cognitive rehabilitation for these patients.