Organization Of Functional Brain Networks Research Articles

Altered brain network topology during speech tracking in developmental dyslexia

Developmental dyslexia is often accompanied by altered phonological processing of speech. Underlying neural changes have typically been characterized in terms of stimulus- and/or task-related responses within individual brain regions or their functional connectivity. Less is known about potential changes in the more global functional organization of brain networks. Here we recorded electroencephalography (EEG) in typical and dyslexic readers while they listened to (a) a random sequence of syllables and (b) a series of tri-syllabic real words. The network topology of the phase synchronization of evoked cortical oscillations was investigated in four frequency bands (delta, theta, alpha and beta) using minimum spanning tree graphs. We found that, compared to syllable tracking, word tracking triggered a shift toward a more integrated network topology in the theta band in both groups. Importantly, this change was significantly stronger in the dyslexic readers, who also showed increased reliance on a right frontal cluster of electrodes for word tracking. The current findings point towards an altered effect of word-level processing on the functional brain network organization that may be associated with less efficient phonological and reading skills in dyslexia.

Pretreatment topological disruptions of whole-brain networks exist in childhood absence epilepsy: A resting-state EEG-fMRI study

PurposeChildhood absence epilepsy (CAE) is associated with functional changes in specific brain regions and connections. However, little is known about the topological properties of the functional brain connectome in drug naive CAE. MethodsWe adopted the resting-state EEG-fMRI and graph theoretic approach to investigate both local and global brain functional network properties of drug naive CAE during interictal resting state compared with healthy control. In addition, we computed the partial correlation coefficient to estimate the correlation between the functional network metrics and the measured disease duration or the age at seizure onset. ResultsThe functional connectome in drug naive CAE showed decreased small-worldness and normalized clustering coefficient at the global level. At the nodal level, decreased nodal centralities were mainly in bilateral prefrontal-thalamocortical circuit and increased nodal centralities mainly in left hippocampus and right middle temporal gyrus (p < 0.05). In addition, the duration of the epilepsy was significantly correlated with the nodal efficiency in left middle frontal gyrus (r = −0.627, p = 0.012). ConclusionThe pretreatment topological disruptions of whole-brain networks exist in drug naive patients with CAE and the functional impairment mainly involve the prefrontal-thalamocortical circuit. These findings in the homogeneous group of CAE indicate that the aberrant topological organization of functional brain network is an intrinsic feature of CAE and provide topologic insights into understanding the pathophysiological mechanisms of CAE.

Disrupted topological organization of resting-state functional brain networks in cerebral small vessel disease.

We aimed to investigate alterations in functional brain networks and assess the relationship between functional impairment and topological network changes in cerebral small vessel disease (CSVD) patients with and without cerebral microbleeds (CMBs). We constructed individual whole‐brain, region of interest (ROI) level functional connectivity (FC) networks for 24 CSVD patients with CMBs (CSVD‐c), 42 CSVD patients without CMBs (CSVD‐n), and 36 healthy controls (HCs). Then, we used graph theory analysis to investigate the global and nodal topological disruptions between groups and relate network topological alterations to clinical parameters. We found that both the CSVD and control groups showed efficient small‐world organization in FC networks. However, compared to CSVD‐n patients and controls, CSVD‐c patients exhibited a significantly decreased clustering coefficient, global efficiency, and local efficiency and an increased shortest path length, indicating a disrupted balance between local specialization and global integration in FC networks. Although both the CSVD and control groups showed highly similar hub distributions, the CSVD‐c group exhibited significantly altered nodal betweenness centrality (BC), mainly distributed in the default mode network (DMN), attention, and visual functional areas. There were almost no global or regional alterations between CSVD‐n patients and controls. Furthermore, the altered nodal BC of the right anterior/posterior cingulate gyrus and left cuneus were significantly correlated with cognitive parameters in CSVD patients. These results suggest that CSVD patients with and without CMBs had segregated disruptions in the topological organization of the intrinsic functional brain network. This study advances our current understanding of the pathophysiological mechanisms underlying CSVD.

Gamma band functional connectivity reduction in patients with amnestic mild cognitive impairment and epileptiform activity.

There is growing evidence for neuronal hyperexcitability in Alzheimer’s disease. Hyperexcitability is associated with an increase in epileptiform activity and the disruption of inhibitory activity of interneurons. Interneurons fire at a high rate and are frequently associated with high-frequency oscillations in the gamma frequency band (30–150 Hz). It is unclear how hyperexcitability affects the organization of functional brain networks. A sample of 63 amnestic mild cognitive impairment patients underwent a magnetoencephalography resting-state recording with eyes closed. Twenty (31.75%) mild cognitive impairment patients had epileptiform activity. A cluster-based analysis of the magnetoencephalography functional connectivity revealed a region within the right temporal cortex whose global connectivity in the gamma frequency band was significantly reduced in patients with epileptiform activity relative to those without epileptiform activity. A subsequent seed-based analysis showed that this was largely due to weaker gamma band connectivity of this region with ipsilateral frontal and medial regions, and the upper precuneus area. In addition, this reduced functional connectivity was associated with higher grey matter atrophy across several cortical regions in the patients with epileptiform activity. These functional network disruptions and changes in brain physiology and morphology have important clinical implications as they may contribute to cognitive decline in mild cognitive impairment and Alzheimer’s disease.

Low-frequency rTMS treatment alters the topographical organization of functional brain networks in schizophrenia patients with auditory verbal hallucination

Auditory verbal hallucinations (AVH) are an important characteristic of schizophrenia. Repeated transcranial magnetic stimulation (rTMS) has been evidence to be effective in treating AVH. We evaluated the topological properties of resting-state functional brain networks in schizophrenia patients with AVH (n = 32) who received 1-Hz rTMS treatment and matched healthy controls (n = 33). The results showed that the psychotic symptoms and certain neurocognitive performances in patients were improved by rTMS treatment. Furthermore, the pretreatment patients showed abnormal global topological metrics compared with the controls, including lower global efficiency (Eglob, represents the relative quality of information transmission between all nodes in the network) and higher characteristic path length (Lp, characterizes the mean shortest distance between any two nodes in the network). The pretreament patients also showed decreased local topological metrics relative to the controls, including the nodal shortest path (NLp, quantifies the mean distance between the given node and the other nodes in the network) and nodal efficiency (Ne, measures the information interchange among the neighbor nodes when one node is removed), mainly located in the prefrontal cortex, occipital cortex, and subcortical regions. While the abnormal global and local topological patterns were normalized in patients after rTMS treatment. The multiple linear regression analysis indicated that the baseline topological metrics could be associated with the clinical responses after treatment in the patient group. The results suggested that the topological organization of the functional brain network was globally and regionally altered in schizophrenia patients with AVH after rTMS treatment and may be a potential therapeutic effect for AVH in schizophrenia.

Altered Topological Organization of Functional Brain Networks in Betel Quid Dependence: A Resting-State Functional MRI Study.

Background: Betel quid dependence (BQD) is associated with abnormalities in the widespread inter-regional functional connectivity of the brain. However, no studies focused on the abnormalities in the topological organization of brain functional networks in chewers in Mainland China.Methods: In the current study, resting-state functional magnetic resonance images were acquired from 53 BQD individuals and 37 gender- and age-matched healthy controls (HCs). A functional network was constructed by calculating the Pearson correlation coefficients among 90 subregions in the human Brainnetome Atlas. The topological parameters were compared between BQD individuals and HCs.Results: The results showed that BQD individuals presented a small-world topology, but the normalized characteristic path length (λ) increased compared with HCs (0.563 ± 0.030 vs. 0.550 ± 0.027). Compared to HCs, BQ chewers showed increased betweenness centrality (Be) in the right supplementary motor area, right medial superior frontal gyrus, right paracentral lobule, right insula, left posterior cingulate gyrus, right hippocampus, right post-central gyrus, right superior parietal gyrus, and right supramarginal gyrus, while decreased Be was found in the orbitofrontal area and temporal area, which is associated with reward network, cognitive system, and default mode network. The area under the curve (AUC) value of λ displayed a positive correlation with the duration of BQ chewing (r = 0.410, p = 0.002).Conclusions: The present study revealed the disruption of functional connectome in brain areas of BQD individuals. The findings may improve our understanding of the neural mechanism of BQD from a brain functional network topological organization perspective.

Altered brain network topology in children with auditory processing disorder: A resting-state multi-echo fMRI study

Children with auditory processing disorder (APD) experience hearing difficulties, particularly in the presence of competing sounds, despite having normal audiograms. There is considerable debate on whether APD symptoms originate from bottom-up (e.g., auditory sensory processing) and/or top-down processing (e.g., cognitive, language, memory). A related issue is that little is known about whether functional brain network topology is altered in APD. Therefore, we used resting-state functional magnetic resonance imaging data to investigate the functional brain network organization of 57 children from 8 to 14 years old, diagnosed with APD (n = 28) and without hearing difficulties (healthy control, HC; n = 29). We applied complex network analysis using graph theory to assess the whole-brain integration and segregation of functional networks and brain hub architecture. Our results showed children with APD and HC have similar global network properties –i.e., an average of all brain regions– and modular organization. Still, the APD group showed different hub architecture in default mode-ventral attention, somatomotor and frontoparietal-dorsal attention modules. At the nodal level –i.e., single-brain regions–, we observed decreased participation coefficient (PC – a measure quantifying the diversity of between-network connectivity) in auditory cortical regions in APD, including bilateral superior temporal gyrus and left middle temporal gyrus. Beyond auditory regions, PC was also decreased in APD in bilateral posterior temporo-occipital cortices, left intraparietal sulcus, and right posterior insular cortex. Correlation analysis suggested a positive association between PC in the left parahippocampal gyrus and the listening-in-spatialized-noise -sentences task where APD children were engaged in auditory perception. In conclusion, our findings provide evidence of altered brain network organization in children with APD, specific to auditory networks, and shed new light on the neural systems underlying children's listening difficulties.

Biomarkers Derived from Alterations in Overlapping Community Structure of Resting-state Brain Functional Networks for Detecting Alzheimer’s Disease

Recent studies show that overlapping community structure is an important feature of the brain functional network. However, alterations in such overlapping community structure in Alzheimer’s disease (AD) patients have not been examined yet. In this study, we investigate the overlapping community structure in AD by using resting-state functional magnetic resonance imaging (rs-fMRI) data. The collective sparse symmetric non-negative matrix factorization (cssNMF) is adopted to detect the overlapping community structure. Experimental results on 28 AD patients and 32 normal controls (NCs) from the ADNI2 dataset show that the two groups have remarkable differences in terms of the optimal number of communities, the hierarchy of communities detected at different scales, network functional segregation, and nodal functional diversity. In particular, the frontal-parietal and basal ganglia networks exhibit significant differences between the two groups. A machine learning framework proposed in this paper for AD detection achieved an accuracy of 76.7% when using the detected community strengths of the frontal-parietal and basal ganglia networks only as input features. These findings provide novel insights into the understanding of pathological changes in the brain functional network organization of AD and show the potential of the community structure-related features for AD detection.

A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls.

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.

Selective attention involves a feature-specific sequential release from inhibitory gating

Selective attention is a fundamental cognitive mechanism that allows our brain to preferentially process relevant sensory information, while filtering out distracting information. Attention is thought to flexibly gate the communication of irrelevant information through top-down alpha-rhythmic (8–12 Hz) functional connections, which influence early visual processing. However, the dynamic effects of top-down influence on downstream visual processing remain unknown. Here, we used electroencephalography to investigate local and network effects of selective attention while subjects attended to distinct features of identical stimuli. We found that attention-related changes in the functional brain network organization emerge shortly after stimulus onset, accompanied by an overall decrease of functional connectivity. Signatures of attentional selection were evident from a sequential release from alpha-band parietal gating in feature-selective areas. The directed connectivity paths and temporal evolution of this release from gating were consistent with the sensory effect of each feature, providing a neural basis for how visual processing quickly prioritizes relevant information in functionally specialized areas.

Abstract 9301: The Association of Pulse Pressure With Brain Structure and Function in Healthy Individuals

Introduction: Hypertension is associated to changes of structural and functional brain networks that lead to impairment of cognitive performance. Recently, pulse pressure (PP) has been suggested as cardiovascular risk factor in hypertension. Hypothesis: In this study, we tested whether elevated PP is accompanied by brain structural and functional changes in healthy individuals without hypertension. Methods: We investigated 115 healthy subjects (41±17 years, 55% females) without cardiovascular, neurological or psychiatric disorders. Structural and functional brain imaging was performed on a 3 Tesla Siemens Prisma. We assessed cognitive function using the MATRICS Consensus Cognitive Battery. Participants were categorized by PP in normal and high PP (PP&gt;50 mmHg) according to resting blood pressure measurements in the laboratory. The normal PP group was reduced to match the high PP group with respect to age, gender, and education level. From resting functional brain images, we extracted whole-brain network connectivity between 260 independent brain regions to estimate global graph-theoretical features for integration (efficiency), resilience (assortativity), and segregation (clustering coefficient). Cortical thickness and volume were extracted from structural images using the Freesurfer image analysis suite. Results: While subgroups did not differ in terms of heart rate variability, baroreflex sensitivity was decreased in the high PP group. Furthermore, subjects with high PP performed worse in the trail-making task that primarily assesses processing speed (p&lt;0.001, FDR-corr.). Graph theoretical analyses revealed that functional brain networks in subjects with high PP were less efficient, clustered and resilient than normal PP (all p&lt;0.001, FDR-corr.). There were no differences in brain structure between groups. However, we found a negative correlation of PP with cortical thickness in the lateral prefrontal cortex over all subjects, suggesting that high PP is associated with cortical thinning in this area (corrected for age, gender and education). Conclusion: Elevated PP is associated with an altered organization of functional brain networks, lower cortical thickness and decreased processing speed in healthy subjects.

Long-term prognosis and educational determinants of brain network decline in older adult individuals

Older adults with lower education are at greater risk for dementia. It is unclear which brain changes lead to these outcomes. Longitudinal imaging-based measures of brain structure and function were examined in adult individuals (baseline age, 45–86 years; two to five visits per participant over 1–9 years). College degree completion differentiates individual-based and neighborhood-based measures of socioeconomic status and disadvantage. Older adults (~65 years and over) without a college degree exhibit a pattern of declining large-scale functional brain network organization (resting-state system segregation) that is less evident in their college-educated peers. Declining brain system segregation predicts impending changes in dementia severity, measured up to 10 years past the last scan date. The prognostic value of brain network change is independent of Alzheimer’s disease (AD)-related genetic risk (APOE status), the presence of AD-associated pathology (cerebrospinal fluid phosphorylated tau, cortical amyloid) and cortical thinning. These results demonstrate that the trajectory of an individual’s brain network organization varies in relation to their educational attainment and, more broadly, is a unique indicator of individual brain health during older age.

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study.

Growing evidence indicates that disruptions in the brain’s functional connectivity play an important role in the pathophysiology of ADHD. The present study investigates alterations in resting-state EEG source connectivity and rich-club organization in children with inattentive (ADHDI) and combined (ADHDC) ADHD compared with typically developing children (TD) under the eyes-closed condition. EEG source analysis was performed by eLORETA in different frequency bands. The lagged phase synchronization (LPS) and graph theoretical metrics were then used to examine group differences in the topological properties and rich-club organization of functional networks. Compared with the TD children, the ADHDI children were characterized by a widespread significant decrease in delta and beta LPS, as well as increased theta and alpha LPS in the left frontal and right occipital regions. The ADHDC children displayed significant increases in LPS in the central, temporal and posterior areas. Both ADHD groups showed small-worldness properties with significant increases and decreases in the network degree in the θ and β bands, respectively. Both subtypes also displayed reduced levels of network segregation. Group differences in rich-club distribution were found in the central and posterior areas. Our findings suggest that resting-state EEG source connectivity analysis can better characterize alterations in the rich-club organization of functional brain networks in ADHD patients.

Game-theoretical mapping of fundamental brain functions based on lesion deficits in acute stroke.

Lesion analysis is a fundamental and classical approach for inferring the causal contributions of brain regions to brain function. However, many studies have been limited by the shortcomings of methodology or clinical data. Aiming to overcome these limitations, we here use an objective multivariate approach based on game theory, Multi-perturbation Shapley value Analysis, in conjunction with data from a large cohort of 394 acute stroke patients, to derive causal contributions of brain regions to four principal functional components of the widely used National Institutes of Health Stroke Score measure. The analysis was based on a high-resolution parcellation of the brain into 294 grey and white matter regions. Through initial lesion symptom mapping for identifying all potential candidate regions and repeated iterations of the game-theoretical approach to remove non-significant contributions, the analysis derived the smallest sets of regions contributing to each of the four principal functional components as well as functional interactions among the regions. Specifically, the factor ‘language and consciousness’ was related to contributions of cortical regions in the left hemisphere, including the prefrontal gyrus, the middle frontal gyrus, the ventromedial putamen and the inferior frontal gyrus. Right and left motor functions were associated with contributions of the left and right dorsolateral putamen and the posterior limb of the internal capsule, correspondingly. Moreover, the superior corona radiata and the paracentral lobe of the right hemisphere as well as the right caudal area 23 of the cingulate gyrus were mainly related to left motor function, while the prefrontal gyrus, the external capsule and the sagittal stratum fasciculi of the left hemisphere contributed to right motor function.Our approach demonstrates a practically feasible strategy for applying an objective lesion inference method to a high-resolution map of the human brain and distilling a small, characteristic set of grey and white matter structures contributing to fundamental brain functions. In addition, we present novel findings of synergistic interactions between brain regions that provide insight into the functional organization of brain networks.

Increased integration between default mode and task-relevant networks in children with ADHD is associated with impaired response control

Default mode network (DMN) dysfunction is theorized to play a role in attention lapses and task errors in children with attention-deficit/hyperactivity disorder (ADHD). In ADHD, the DMN is hyperconnected to task-relevant networks, and both increased functional connectivity and reduced activation are related to poor task performance. The current study extends existing literature by considering interactions between the DMN and task-relevant networks from a brain network perspective and by assessing how these interactions relate to response control. We characterized both static and time-varying functional brain network organization during the resting state in 43 children with ADHD and 43 age-matched typically developing (TD) children. We then related aspects of network integration to go/no-go performance. We calculated participation coefficient (PC), a measure of a region’s inter-network connections, for regions of the DMN, canonical cognitive control networks (fronto-parietal, salience/cingulo-opercular), and motor-related networks (somatomotor, subcortical). Mean PC was higher in children with ADHD as compared to TD children, indicating greater integration across networks. Further, higher and less variable PC was related to greater commission error rate in children with ADHD. Together, these results inform our understanding of the role of the DMN and its interactions with task-relevant networks in response control deficits in ADHD.

Spatiotemporally flexible subnetworks reveal the quasi-cyclic nature of integration and segregation in the human brain

Though the organization of functional brain networks is modular at its core, modularity does not capture the full range of dynamic interactions between individual brain areas nor at the level of subnetworks. In this paper we present a hierarchical model that represents both flexible and modular aspects of intrinsic brain organization across time by constructing spatiotemporally flexible subnetworks. We also demonstrate that segregation and integration are complementary and simultaneous events. The method is based on combining the instantaneous phase synchrony analysis (IPSA) framework with community detection to identify a small, yet representative set of subnetwork components at the finest level of spatial granularity. At the next level, subnetwork components are combined into spatiotemporally flexibly subnetworks where temporal lag in the recruitment of areas within subnetworks is captured. Since individual brain areas are permitted to be part of multiple interleaved subnetworks, both modularity as well as more flexible tendencies of connectivity are accommodated for in the model. Importantly, we show that assignment of subnetworks to the same community (integration) corresponds to positive phase coherence within and between subnetworks, while assignment to different communities (segregation) corresponds to negative phase coherence or orthogonality. Together with disintegration, i.e. the breakdown of internal coupling within subnetwork components, orthogonality facilitates reorganization between subnetworks. In addition, we show that the duration of periods of integration is a function of the coupling strength within subnetworks and subnetwork components which indicates an underlying metastable dynamical regime. Based on the main tendencies for either integration or segregation, subnetworks are further clustered into larger meta-networks that are shown to correspond to combinations of core resting-state networks. We also demonstrate that subnetworks and meta-networks are coarse graining strategies that captures the quasi-cyclic recurrence of global patterns of integration and segregation in the brain. Finally, the method allows us to estimate in broad terms the spectrum of flexible and/or modular tendencies for individual brain areas.

Functional Brain Connectivity Patterns Associated with Visual Hallucinations in Dementia with Lewy Bodies.

Background:The presence of recurrent, complex visual hallucinations (VH) is among the core clinical features of dementia with Lewy bodies (DLB). It has been proposed that VH arise from a disrupted organization of functional brain networks. However, studies are still limited, especially investigating the resting-state functional brain features underpinning VH in patients with dementia.Objective:The aim of the present pilot study was to investigate whether there were any alterations in functional connectivity associated with VH in DLB.Methods:Seed-based analyses and independent component analysis (ICA) of resting-state fMRI scans were carried out to explore differences in functional connectivity between DLB patients with and without VH.Results:Seed-based analyses reported decreased connectivity of the lateral geniculate nucleus, the superior parietal lobule and the putamen with the medial frontal gyrus in DLB patients with VH. Visual areas showed a pattern of both decreased and increased functional connectivity. ICA revealed between-group differences in the default mode network (DMN).Conclusion:Functional connectivity analyses suggest dysfunctional top-down and bottom-up processes and DMN-related alterations in DLB patients with VH. This impairment might foster the generation of false visual images that are misinterpreted, ultimately resulting in VH.

Identifying Emergent Mesoscopic-Macroscopic Functional Brain Network Dynamics in Infants at Term-Equivalent Age with Electric Source Neuroimaging.

Aim: To identify and characterize the functional brain networks at the time when the brain is yet to develop higher order functions in term-born and preterm infants at term-equivalent age. Introduction: Although functional magnetic resonance imaging (fMRI) data have revealed the existence of spatially structured resting-state brain activity in infants, the temporal information of fMRI data limits the characterization of fast timescale brain oscillations. In this study, we use infants' high-density electroencephalography (EEG) to characterize spatiotemporal and spectral functional organizations of brain network dynamics. Methods: We used source-reconstructed EEG and graph theoretical analyses in 100 infants (84 preterm, 16 term born) to identify the rich-club topological organization, temporal dynamics, and spectral fingerprints of dynamic functional brain networks. Results: Five dynamic functional brain networks are identified, which have rich-club topological organizations, distinctive spectral fingerprints (in the delta and low-alpha frequency), and scale-invariant temporal dynamics (<0.1 Hz): The default mode, primary sensory-limbic system, thalamo-frontal, thalamo-sensorimotor, and visual-limbic system. The temporal dynamics of these networks are correlated in a hierarchically leading-following organization, showing that infant brain networks arise from long-range synchronization of band-limited cortical oscillation based on interacting fast- and slow-coherent cortical oscillations. Conclusion: Dynamic functional brain networks do not solely depend on the maturation of cognitive networks; instead, the brain network dynamics exist in infants at term age well before the childhood and adulthood, and hence, it offers a quantitative measurement of neurotypical development in infants. Clinical Trial Registration Number: ACTRN12615000591550. Impact statement Our work offers novel functional insights into the brain network characterization in infants, providing a new functional basis for future deployable prognostication approaches.

Characterizing changes in network connectivity following chronic head trauma in special forces military personnel: a combined resting-fMRI and DTI study

ABSTRACT Background Soldiers are exposed to significant repetitive head trauma, which may disrupt functional and structural brain connectivity patterns. Purpose/hypothesis Integrate resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) to characterize changes in connectivity biomarkers within Canadian Special Operations Forces (CANSOF), hypothesizing that alterations in architectural organization of cortical hubs may follow chronic repetitive head trauma. Methods Fifteen CANSOFs with a history of chronic exposure to sub-concussive head trauma and concussive injuries (1.9 ± 2.0 concussions (range: [0–6])), as well as an equal age-matched cohort of controls (CTLs) were recruited. BOLD-based rs-fMRI was combined with DTI to reconstruct functional and structural networks using independent component analyses and probabilistic tractography. Connectivity markers were computed based on the distance between functional seeds to assess for possible differences in injury susceptibility of short- and long-range connections. Results/discussion Significant hyper- and hypo-connectivity differences in cortical connections were observed suggesting that chronic head trauma may predispose soldiers to changes in the functional organization of brain networks. Significant structural alterations in axonal fibers directly connecting disrupted functional nodes were specific to hyper-connected long-range connections, suggesting a potential relationship between axonal injury and increases in neural recruitment following repetitive head trauma from high-exposure military duties.

Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation.

Direct electrical stimulation can modulate the activity of brain networks for the treatment of several neurological and neuropsychiatric disorders and for restoring lost function. However, precise neuromodulation in an individual requires the accurate modelling and prediction of the effects of stimulation on the activity of their large-scale brain networks. Here, we report the development of dynamic input-output models that predict multiregional dynamics of brain networks in response to temporally varying patterns of ongoing microstimulation. In experiments with two awake rhesus macaques, we show that the activities of brain networks are modulated by changes in both stimulation amplitude and frequency, that they exhibit damping and oscillatory response dynamics, and that variabilities in prediction accuracy and in estimated response strength across brain regions can be explained by an at-rest functional connectivity measure computed without stimulation. Input-output models of brain dynamics may enable precise neuromodulation for the treatment of disease and facilitate the investigation of the functional organization of large-scale brain networks.