Changes In Brain Networks Research Articles

Combined Yoga and Transcranial Direct Current Stimulation Increase Functional Connectivity and Synchronization in the Frontal Areas.

Transcranial direct current stimulation (tDCS) is a non-invasive neuro-stimulation technique that can modulate cortical excitability. Similarly, yoga is shown to affect the brain's neural activity and networks. Here, we aimed to investigate the effect of combined yoga and tDCS on brain oscillations and networks using resting-state electroencephalography recordings. In a randomized, cross-over, double-blind design, twenty-two healthy subjects participated in a yoga/active tDCS session (2mA; 20min; anode-F3, cathode F4) or yoga/sham tDCS on 2 separate days. Resting-state EEG data were collected before and after each intervention. Power spectral density (PSD) and functional connectivity, measured by a synchronization measure, phase-locking value, were computed for each condition. There were no significant differences in PSD values among the two interventions. The network-based statistic method was employed for detecting functional connectivity differences between yoga/active and yoga/sham tDCS interventions. Results show that the addition of active tDCS to yoga is associated with increased functional connectivity of the scalp and source EEG data in the frontal area. The changes were widespread, intra-hemispheric, and inter-hemispheric connections, which were mainly between the frontal area to other regions. At the source level, most of the connectivity changes were found in the fronto-parietal network. These findings suggest that combining yoga with tDCS might lead to brain network changes related to the executive and attentional functions.

EEG based functional brain networks analysis in dyslexic children during arithmetic task.

Developmental Dyslexia is a neuro-developmental disorder that often refers to a phonological processing deficit regardless of average IQ. The present study investigated the distinct functional changes in brain networks of dyslexic children during arithmetic task performance using an electroencephalogram. Fifteen dyslexic children and fifteen normally developing children (NDC) were recruited and performed an arithmetic task. Brain functional network measures such as node strength, clustering coefficient, characteristic pathlength and small-world were calculated using graph theory methods for both groups. Task performance showed significantly less performance accuracy in dyslexics against NDC. The neural findings showed increased connectivity in the delta band and reduced connectivity in theta, alpha, and beta band at temporoparietal, and prefrontal regions in dyslexic group while performing the task. The node strengths were found to be significantly high in delta band (T3, O1, F8 regions) and low in theta (T5, P3, Pz regions), beta (Pz) and gamma band (T4 and prefrontal regions) during the task in dyslexics compared to the NDC. The clustering coefficient was found to be significantly low in the dyslexic group (theta and alpha band) and characteristic pathlength was found to be significantly high in the dyslexic group (theta and alpha band) compared to the NDC group while performing task. In conclusion, the present study shows evidence for poor fact-retrieval mechanism and altered network topology in dyslexic brain networks during arithmetic task performance.

The Effect of Music Listening on EEG Functional Connectivity of Brain: A Short-Duration and Long-Duration Study

Music is considered a powerful brain stimulus, as listening to it can activate several brain networks. Music of different kinds and genres may have a different effect on the human brain. The goal of this study is to investigate the change in the brain’s functional connectivity (FC) when music is used as a stimulus. Secondly, the effect of listening to the subject’s favorite music is compared with listening to specifically formulated relaxing music with alpha binaural beats. Finally, the effect of the duration of music listening is studied. Subjects’ electroencephalographic (EEG) signals were captured as they listened to favorite and relaxing music. After preprocessing and artifact removal, the EEG recordings were decomposed into the delta, theta, alpha, and beta frequency bands, and the grand-averaged connectivity matrices were generated using Inter-Site Phase Clustering (ISPC) for each frequency band and each type of music. Furthermore, each lobe of the brain was analyzed separately to understand the effect of music on specific regions of the brain. EEG-FC among different channels was accessed by using graph theory and Network-based Statistics (NBS). To determine the significance of the changes in brain networks after listening to music, statistical analysis was conducted using Analysis of Variance (ANOVA) and t-test. The study of listening to music for a short duration verifies that either favorite or preferred music can affect the FC of the subject and induce a relaxation state. The short duration study also verifies a significant (ANOVA and t-test: p < 0.05) effectiveness of relaxing music over favorite music to induce relaxation and alertness in the subject. In the study of long duration, it is concluded that listening to relaxing music can increase functional connectivity and connections strength in the frontal lobe of the subject. A significant increase (ANOVA and t-test: p < 0.05) in FC in alpha and theta band and a significant decrease (ANOVA and t-test: p < 0.05) in FC in beta band in the frontal and parietal lobe of the brain verifies the hypothesis that the relaxing music can help the subject to achieve relaxation, activeness, and alertness.

Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer's Disease.

Alzheimer’s disease (AD) does not present uniform symptoms or a uniform rate of progression in all cases. The classification of subtypes can be based on clinical symptoms or patterns of pathological brain alterations. Imaging techniques may allow for the identification of AD subtypes and their differentiation from other neurodegenerative diseases already at an early stage. In this review, the strengths and weaknesses of current clinical imaging methods are described. These include positron emission tomography (PET) to image cerebral glucose metabolism and pathological amyloid or tau deposits. Magnetic resonance imaging (MRI) is more widely available than PET. It provides information on structural or functional changes in brain networks and their relation to AD subtypes. Amyloid PET provides a very early marker of AD but does not distinguish between AD subtypes. Regional patterns of pathology related to AD subtypes are observed with tau and glucose PET, and eventually as atrophy patterns on MRI. Structural and functional network changes occur early in AD but have not yet provided diagnostic specificity.

Electrophysiological resting state brain network and episodic memory in healthy aging adults

Recent studies have emphasized the changes in large-scale brain networks related to healthy aging, with the ultimate purpose to aid in differentiating normal neurocognitive aging from neurodegenerative disorders that also arise with age. Emerging evidence from functional Magnetic Resonance Imaging (fMRI) indicates that connectivity patterns within specific brain networks, especially the Default Mode Network (DMN), distinguish those with Alzheimer's disease from healthy individuals. In addition, disruptive alterations in the large-scale brain systems that support high-level cognition are shown to accompany cognitive decline at the behavioral level, which is commonly observed in the aging populations, even in the absence of disease. Although fMRI is useful for assessing functional changes in brain networks, its high costs and limited accessibility discourage studies that need large populations. In this study, we investigated the aging-effect on large-scale networks of the human brain using high-density electroencephalography and electrophysiological source imaging, which is a less costly and more accessible alternative to fMRI. In particular, our study examined a group of healthy subjects in the age range from middle- to older-aged adults, which is an under-studied range in the literature. Employing a high-resolution computation model, our results revealed age associations in the connectivity pattern of DMN in a consistent manner with previous fMRI findings. Particularly, in combination with a standard battery of cognitive tests, our data showed that in the posterior cingulate / precuneus area of DMN higher brain connectivity was associated with lower performance on an episodic memory task. The findings demonstrate the feasibility of using electrophysiological imaging to characterize large-scale brain networks and suggest that changes in network connectivity are associated with normal aging.

Distinguishing patients with temporal lobe epilepsy from normal controls with the directed graph measures of resting-state fMRI

PurposeChanges in the brain networks of patients with temporal lobe epilepsy (TLE) have been extensively explored, but the biological mechanisms underlying these alterations remain unclear. Here, we aim to identify changes in brain networks in patients with TLE and provide an accurate algorithm for distinguishing these patients from normal controls (NC) with graph-theoretical approach and advanced machine learning methods. MethodsDirected network construction was applied to resting-state functional magnetic resonance imaging (rs-fMRI) data from 55 subjects (23 TLE patients and 32 NC), and 13 directed graph measures were calculated. Two-sample t-test selected features were used as inputs to a support vector machine (SVM). The leave-one-out cross-validation method was used in measuring classification performance. ResultsAn accuracy of 94.55% (sensitivity = 91.30%, f1-score = 93.33%, Cohen's kappa = 0.9345) was achieved for the classification of TLE patients and NC with optimal features and SVM classifier. According to the results of the two-sample t-test results, TLE disease impacted several areas of the brain, including the temporal, parietal, occipital, posterior cingulate, angular gyrus, superior frontal gyrus, and cerebellum regions in degree centrality, flow coefficient and node efficiency. There was a significant correlation between performance IQ and the flow coefficient of the left posterior cerebellum lobe in TLE group. ConclusionThe study confirmed the validity of Granger causality analysis in constructing directed brain networks. The proposed machine learning approach based on directed graph measures may serve as a biomarker for the diagnosis of TLE to assist in the early diagnosis of TLE patients and intervention in treatment plans.

Abnormal dynamic brain activity and functional connectivity of primary motor cortex in blepharospasm.

Accumulating evidence indicates that dynamic amplitude of low-frequency fluctuations (dALFF) or dynamic functional connectivity (dFC) can provide complementary information, distinct from static amplitude of low-frequency fluctuations (sALFF) or static functional connectivity (sFC), in detecting brain functional abnormalities in brain diseases. We aimed to examine whether dALFF and dFC can offer valuable information for the detection of functional brain abnormalities in patients with blepharospasm. We collected resting-state functional magnetic resonance imaging data from 46 patients each of blepharospasm, hemifacial spasm (HFS), and healthy controls (HCs). We examined intergroup differences in sALFF and dALFF to investigate abnormal regional brain activity in patients with blepharospasm. Based on the dALFF results, we conducted seed-based sFC and dFC analyses to identify static and dynamic connectivity changes in brain networks centered on areas showing abnormal temporal variability of local brain activity in patients with blepharospasm. Compared with HCs, patients with blepharospasm displayed different brain functional change patterns characterized by increased sALFF in the left primary motor cortex (PMC) but increased dALFF variance in the right PMC. However, differences were not found between patients with HFS and HCs. Additionally, patients with blepharospasm exhibited decreased dFC strength, but no change in sFC, between right PMC and ipsilateral cerebellum compared with HCs; these findings were replicated when patients with blepharospasm were compared to those with HFS. Our findings highlight that dALFF and dFC are complementary to sALFF and sFC and can provide valuable information for detecting brain functional abnormalities in blepharospasm. Blepharospasm may be a network disorder involving the cortico-ponto-cerebello-thalamo-cortical circuit.

An EEG Neurofeedback Interactive Model for Emotional Classification of Electronic Music Compositions Considering Multi-Brain Synergistic Brain-Computer Interfaces.

This paper presents an in-depth study and analysis of the emotional classification of EEG neurofeedback interactive electronic music compositions using a multi-brain collaborative brain-computer interface (BCI). Based on previous research, this paper explores the design and performance of sound visualization in an interactive format from the perspective of visual performance design and the psychology of participating users with the help of knowledge from various disciplines such as psychology, acoustics, aesthetics, neurophysiology, and computer science. This paper proposes a specific mapping model for the conversion of sound to visual expression based on people’s perception and aesthetics of sound based on the phenomenon of audiovisual association, which provides a theoretical basis for the subsequent research. Based on the mapping transformation pattern between audio and visual, this paper investigates the realization path of interactive sound visualization, the visual expression form and its formal composition, and the aesthetic style, and forms a design expression method for the visualization of interactive sound, to benefit the practice of interactive sound visualization. In response to the problem of neglecting the real-time and dynamic nature of the brain in traditional brain network research, dynamic brain networks proposed for analyzing the EEG signals induced by long-time music appreciation. During prolonged music appreciation, the connectivity of the brain changes continuously. We used mutual information on different frequency bands of EEG signals to construct dynamic brain networks, observe changes in brain networks over time and use them for emotion recognition. We used the brain network for emotion classification and achieved an emotion recognition rate of 67.3% under four classifications, exceeding the highest recognition rate available.

Brain network segregation and integration during painful thermal stimulation.

The present study aimed to determine changes in brain network integration/segregation during thermal pain using methods optimized for network connectivity events with high temporal resolution. Participants (n = 33) actively judged whether thermal stimuli applied to the volar forearm were painful or not and then rated the warmth/pain intensity after each trial. We show that the temporal evolution of integration/segregation within trials correlates with the subjective ratings of pain. Specifically, the brain shifts from a segregated state to an integrated state when processing painful stimuli. The association with subjective pain ratings occurred at different time points for all networks. However, the degree of association between ratings and integration/segregation vanished for several brain networks when time-varying functional connectivity was measured at lower temporal resolution. Moreover, the increased integration associated with pain is explained to some degree by relative increases in between-network connectivity. Our results highlight the importance of investigating the relationship between pain and brain network connectivity at a single time point scale, since commonly used temporal aggregations of connectivity data may result in that fine-scale changes in network connectivity may go unnoticed. The interplay between integration/segregation reflects shifting demands of information processing between brain networks and this adaptation occurs both for cognitive tasks and nociceptive processing.

Oscillatory brain network changes after transcranial magnetic stimulation treatment in patients with major depressive disorder

• Oscillatory network changes during rTMS in MDD were evaluated by clinical EEG. • Functional connectivity changes were observed in MDD after rTMS. • Improved theta-band functional connectivity was associated with improved cognition. : Repetitive transcranial magnetic stimulation (rTMS) has attracted attention for treating treatment-resistant major depressive disorder (MDD) because of its effectiveness and low invasiveness. Recent neuroimaging studies suggest that the effects of rTMS in MDD may be based on improvements in abnormal brain networks. However, oscillatory network changes after rTMS remain unclear. This study aimed to detect oscillatory electroencephalography (EEG) markers that reflect the therapeutic effects of rTMS. : We treated 15 treatment-resistant MDD patients with high-frequency (10 Hz) rTMS over the left prefrontal cortex for 6 weeks. The power spectral density at each electrode and the phase lag index between all electrode pairs were compared before and after rTMS using clinical EEG data. The relationships between EEG indices, depressive symptoms, and cognitive function were also examined. : Depressive symptoms were significantly improved after rTMS. Additionally, functional connectivity between the left frontal–temporal and occipital regions in the theta band and gamma power in the anterior–central area were increased in both hemispheres after rTMS. Increased functional connectivity in the theta band was correlated with improved cognitive function, while increased gamma power was correlated with worsened depressive symptoms. : The sample size was relatively small. : Our results demonstrated oscillatory changes in theta-band functional connectivity after rTMS in MDD patients. These findings, obtained using clinical EEG methods, provide evidence of the underlying neurophysiological effects of rTMS on MDD within the context of functional brain network changes, and may constitute a biomarker for evaluating MDD treatments.

Investigation of structural brain changes in Charles Bonnet Syndrome

Background and objectivesIn Charles Bonnet Syndrome (CBS), visual hallucinations (VH) are experienced by people with sight loss due to eye disease or lesional damage to early visual pathways. The aim of this cross-sectional study was to investigate structural brain changes using magnetic resonance imaging (MRI) in CBS. MethodsSixteen CBS patients, 17 with eye disease but no VH, and 19 normally sighted people took part. Participants were imaged on a 3T scanner, with 1 mm resolution T1 weighted structural imaging, and diffusion tensor imaging with 64 diffusion directions. ResultsThe three groups were well matched for age, sex and cognitive scores (MMSE). The two eye disease groups were matched on visual acuity. Compared to the sighted controls, we found reduced grey matter in the occipital cortex in both eye disease groups. We also found reductions of fractional anisotropy and increased diffusivity in widespread areas, including occipital tracts, the corpus callosum, and the anterior thalamic radiation. We did not find any significant differences between the eye disease participants with VH versus without VH, but did observe a negative association between hippocampal volume and VH severity in the CBS group. DiscussionOur findings suggest that although there are cortical and subcortical effects associated with sight loss, structural changes do not explain the occurrence of VHs. CBS may relate instead to connectivity or excitability changes in brain networks linked to vision.

Topological changes of brain network during mindfulness meditation: an exploratory source level magnetoencephalographic study.

We have previously evidenced that Mindfulness Meditation (MM) in experienced meditators (EMs) is associated with long-lasting topological changes in resting state condition. However, what occurs during the meditative phase is still debated.Utilizing magnetoencephalography (MEG), the present study is aimed at comparing the topological features of the brain network in a group of EMs (n = 26) during the meditative phase with those of individuals who had no previous experience of any type of meditation (NM group, n = 29). A wide range of topological changes in the EM group as compared to the NM group has been shown. Specifically, in EMs, we have observed increased betweenness centrality in delta, alpha, and beta bands in both cortical (left medial orbital cortex, left postcentral area, and right visual primary cortex) and subcortical (left caudate nucleus and thalamus) areas. Furthermore, the degree of beta band in parietal and occipital areas of EMs was increased too.Our exploratory study suggests that the MM can change the functional brain network and provides an explanatory hypothesis on the brain circuits characterizing the meditative process.

The longitudinal neural dynamics changes of whole brain connectome during natural recovery from poststroke aphasia

Poststroke aphasia is one of the most dramatic functional deficits that results from direct damage of focal brain regions and dysfunction of large-scale brain networks. The reconstruction of language function depends on the hierarchical whole-brain dynamic reorganization. However, investigations into the longitudinal neural changes of large-scale brain networks for poststroke aphasia remain scarce. Here we characterize large-scale brain dynamics in left-frontal-stroke aphasia through energy landscape analysis. Using fMRI during an auditory comprehension task, we find that aphasia patients suffer serious whole-brain dynamics perturbation in the acute and subacute stages after stroke, in which the brains were restricted into two major activity patterns. Following spontaneous recovery process, the brain flexibility improved in the chronic stage. Critically, we demonstrated that the abnormal neural dynamics are correlated with the aberrant brain network coordination. Taken together, the energy landscape analysis exhibited that the acute poststroke aphasia has a constrained, low dimensional brain dynamics, which were replaced by less constrained and high dimensional dynamics at chronic aphasia. Our study provides a new perspective to profoundly understand the pathological mechanisms of poststroke aphasia.

Changes in sensory-related brain networks of patients with moyamoya disease with limb paresthesia: A resting-state fMRI-based functional connectivity analysis

This study’s aim was to investigate functional brain connectivity changes among patients with moyamoya disease (MMD) with limb paresthesia, using functional connectivity analysis based on resting-state functional magnetic resonance imaging (rs-fMRI). A total of 181 patients with MMD were enrolled, including 57 with left limb paresthesia (MLP group), 61 with right limb paresthesia (MRP group), and 63 without paresthesia (MWP group). Encephaloduroarteriosynangiosis (EDAS) was performed in 20 of the 57 patients with left limb paresthesia and 15 of the 61 patients with right limb paresthesia. Twenty-nine age- and sex-matched healthy controls (HC group) were recruited during the same period. All participants underwent rs-fMRI examination, and the patients treated with EDAS were re-examined 3–4 months after the surgery. After data preprocessing, we selected Brodmann area 3 on each side of the brain as the seed region to construct a functional connectivity network of the whole brain, and then we analyzed the differences in functional connectivity between the HC group, MWP group, MLP group, and MRP group. The functional connectivity of Brodmann area 3 (on either side) with the ipsilateral frontal (superior frontal gyrus, middle frontal gyrus, and inferior frontal gyrus) and parietal (supramarginal gyrus, angular gyrus, and superior parietal lobule) cortices was increased among patients with MMD. The functional connectivity enhancement in these brain regions was broader and greater in patients with contralateral limb paresthesia than in patients without paresthesia, and the regions with functional connectivity changes were roughly distributed symmetrically among the MLP group and the MRP group. There were no changes 3–4 months after EDAS in the increased functional connectivity between the frontal and parietal cortices and Brodmann area 3. Limb paresthesia in patients with MMD may be driven by abnormal functional connectivity in the frontal and parietal cortices. Functional changes in associated brain regions may be a target for evaluating the severity of MMD and its response to treatment.

Five weeks of intermittent transcutaneous vagus nerve stimulation shape neural networks: a machine learning approach

Invasive and transcutaneous vagus nerve stimulation [(t)-VNS] have been used to treat epilepsy, depression and migraine and has also shown effects on metabolism and body weight. To what extent this treatment shapes neural networks and how such network changes might be related to treatment effects is currently unclear. Using a pre-post mixed study design, we applied either a tVNS or sham stimulation (5 h/week) in 34 overweight male participants in the context of a study designed to assess effects of tVNS on body weight and metabolic and cognitive parameters resting state (rs) fMRI was measured about 12 h after the last stimulation period. Support vector machine (SVM) classification was applied to fractional amplitude low-frequency fluctuations (fALFF) on established rs-networks. All classification results were controlled for random effects and overfitting. Finally, we calculated multiple regressions between the classification results and reported food craving. We found a classification accuracy (CA) of 79 % in a subset of four brainstem regions suggesting that tVNS leads to lasting changes in brain networks. Five of eight salience network regions yielded 76,5 % CA. Our study shows tVNS’ post-stimulation effects on fALFF in the salience rs-network. More detailed investigations of this effect and their relationship with food intake seem reasonable for future studies.

Associations of the Disrupted Functional Brain Network and Cognitive Function in End-Stage Renal Disease Patients on Maintenance Hemodialysis: A Graph Theory-Based Study of Resting-State Functional Magnetic Resonance Imaging.

Objective: Cognitive impairment (CI) is a common neurological complication in patients with end-stage renal disease undergoing maintenance hemodialysis (MHD). Brain network analysis based on graph theory is a promising tool for studying CI. Therefore, the purpose of this study was to analyze the changes of functional brain networks in patients on MHD with and without CI by using graph theory and further explore the underlying neuropathological mechanism of CI in these patients.Methods: A total of 39 patients on MHD (19 cases with CI and 20 without) and 25 healthy controls (HCs) matched for age, sex, and years of education were enrolled in the study. Resting-state functional magnetic resonance imaging (rs-fMRI) and T1-weighted high-resolution anatomical data were obtained, and functional brain networks for each subject were constructed. The brain network parameters at the global and regional levels were calculated, and a one-way analysis of covariance was used to compare the differences across the three groups. The associations between the changed graph-theory parameters and cognitive function scores in patients on MHD were evaluated using Spearman correlation analysis.Results: Compared with HCs, the global parameters [sigma, gamma, and local efficiency (Eloc)] in both patient groups decreased significantly (p < 0.05, Bonferroni corrected). The clustering coefficient (Cp) in patients with CI was significantly lower than that in the other two groups (p < 0.05, Bonferroni corrected). The regional parameters were significantly lower in the right superior frontal gyrus, dorsolateral (SFGdor) and gyrus rectus (REC) of patients with CI than those of patients without CI; however the nodal local efficiency in the left amygdala was significantly increased (all p < 0.05, Bonferroni corrected). The global Cp and regional parameters in the three brain regions (right SFGdor, REC, and left amygdala) were significantly correlated with the cognitive function scores (all FDR q < 0.05).Conclusion: This study confirmed that the topology of the functional brain network was disrupted in patients on MHD with and without CI and the disruption of brain network was more severe in patients with CI. The abnormal brain network parameters are closely related to cognitive function in patients on MHD.

Functional connectivity is altered by APOE4 carrier status in cognitively healthy older adults

AbstractBackgroundResting‐state connectivity allows for investigating brain network changes in pathologic states of aging, such as Alzheimer’s Disease (AD). Recently, brain network changes have been associated with AD‐related risk factors in asymptomatic individuals. We explored how brain network connectivity is affected by one such risk factor, apolipoprotein e4 (APOE4) carrier status.MethodsIn a sample of cognitively healthy older adults (n=102), we acquired demographic information, APOE4 carrier status, and resting‐state fMRI scans. Individuals were designated as “APOE4 carrier” by the presence of at least one APOE4 allele. No APOE2 carriers were included in the analyses. Using independent component analysis approaches for both individual de‐noising and group‐level analysis, we identified key brain networks traditionally associated with AD‐related decline. Utilizing nonparametric permutation inference, we tested for differences in connectivity to these networks based on APOE4 carrier status.ResultsWe identified multiple networks with altered functional connectivity across groups, including networks associated with AD‐related decline. One key region, the anterior cingulate cortex, showed differences in connectivity with multiple resting state networks. Functional connectivity in these networks was decreased in APOE4 carriers compared to non‐carriers.ConclusionWe highlight changes in functional connectivity dependent upon genetic risk. Our results add to a growing body of evidence that APOE4 carrier status, along with other risk factors for AD, alter large‐scale functional networks. Additional characterization of the interaction of APOE4 status and other AD‐related biomarkers on functional connectivity is warranted.

Subthreshold amyloid pathology and changes in functional brain networks in patients with delayed cognitive decline after stroke

AbstractBackgroundIn clinical practice, some of the stroke survivors who have normal cognitive function until three to six months after index stroke, develop cognitive decline gradually after then. Although amyloid deposits and advanced small vessel disease are reported to be the possible predictors, pathogenesis for delayed cognitive decline has not been identified yet. Thus, we aimed to explore the brain network characteristics in those with delayed cognitive decline after stroke compared to those without.MethodUsing a prospective stroke registry, we identified patients with ischemic stroke who had undergone the cognitive evaluations including mini‐mental state examination (MMSE) twice or more after index stroke. We included patients whose baseline cognitive evaluation (between 1 and 12 months after stroke) was normal and who had one or more follow‐up evaluations after more than 6 months from the baseline evaluation. Among 222 eligible patients, decliners were defined as having a decline in 3 or more per year or an absolute decline of 5 or more between baseline and follow‐up MMSE, while non‐decliners as having a decline of 1 point per year or less and an absolute decline of less than 3 points. Brain network attributes were analyzed using resting‐state functional MRI, and superimposed amyloid positivity was assessed using 18F‐florbetaben PET.ResultEleven patients (5%, 11/222) met the criteria for decliners, and ten matched non‐decliners were enrolled. For about 5.7 years of follow‐up, the mean change of MMSE scores was ‐9.1 points in the decliners, while ‐0.2 point in the non‐decliners. Ten (10/11) decliners were amyloid‐negative on the amyloid PET, and the APOE e4 allele was identified in one decliner. However, the global SUVR value showed a negative correlation with the final MMSE scores as well as the amount of MMSE change. Normalized transitivity was lower in decliners compared to non‐decliners. Mean connectivity strengths were averaged based on the seven canonical functional networks, and the intra‐network connections within the frontoparietal network showed the considerable differences between groups. (Figure)ConclusionThis study suggests that alteration of brain networks may explain the pathogenesis of the delayed post‐stroke cognitive decline independent of amyloid PET positivity and APOE genotype.

Effect of proprioceptive stimulation using a soft robotic glove on motor activation and brain connectivity in stroke survivors

Objective. Soft-robotic-assisted training may improve motor function during post-stroke recovery, but the underlying physiological changes are not clearly understood. We applied a single-session of intensive proprioceptive stimulation to stroke survivors using a soft robotic glove to delineate its short-term influence on brain functional activity and connectivity. Approach. In this study, we utilized task-based and resting-state functional magnetic resonance imaging (fMRI) to characterize the changes in different brain networks following a soft robotic intervention. Nine stroke patients with hemiplegic upper limb engaged in resting-state and motor-task fMRI. The motor tasks comprised two conditions: active movement of fingers (active task) and glove-assisted active movement using a robotic glove (glove-assisted task), both with visual instruction. Each task was performed using bilateral hands simultaneously or the affected hand only. The same set of experiments was repeated following a 30 min treatment of continuous passive motion (CPM) using a robotic glove. Main results. On simultaneous bimanual movement, increased activation of supplementary motor area (SMA) and primary motor area (M1) were observed after CPM treatment compared to the pre-treatment condition, both in active and glove-assisted task. However, when performing the tasks solely using the affected hand, the phenomena of increased activity were not observed either in active or glove-assisted task. The comparison of the resting-state fMRI between before and after CPM showed the connectivity of the supramarginal gyrus and SMA was increased in the somatosensory network and salience network. Significance. This study demonstrates how passive motion exercise activates M1 and SMA in the post-stroke brain. The effective proprioceptive motor integration seen in bimanual exercise in contrast to the unilateral affected hand exercise suggests that the unaffected hemisphere might reconfigure connectivity to supplement damaged neural networks in the affected hemisphere. The somatosensory modulation rendered by the intense proprioceptive stimulation would affect the motor learning process in stroke survivors.

Evaluating the properties of intrinsic functional brain networks vulnerable to Alzheimer’s disease during development through connectomics analysis: Preliminary findings from the PRANK study

AbstractBackgroundThe organization of the brain’s intrinsic functional networks changes during healthy lifespan development, but pathologies such as Alzheimer’s disease (AD) alter the normative trends in functional network organization. Similarly, cognitive abilities such as memory change across the lifespan, but AD pathology causes memory deficits relative to normative expectations. While AD‐related changes in memory and brain networks are often observed in older adults, it is possible that vulnerability to AD is established through differences in early‐life brain and cognitive development including the periadolescent epoch. Here, we used a cross‐sectional approach to study the organization of AD‐vulnerable brain networks and memory in healthy periadolescent children. Data were drawn from the Polygenic Risk of Alzheimer’s Disease in Nebraska Kids (PRANK) study. The goal of our research is to evaluate whether early‐life functional brain development may create vulnerabilities to late‐onset AD.MethodTo better understand the relationship between network properties of the brain and hippocampal‐dependent memory, we studied cognitive and brain measures in a preliminary sample of periadolescent children (N=40) and healthy young adults (N=10) from the PRANK study. Cognitive assessment included a standardized paired associates learning task (CANTAB), a task that requires hippocampal‐dependent relational memory. Brain structure and function were assessed with MRI protocols adapted from the Human Connectome Project. Resting‐state fMRI data was used to measure resting state functional connectivity which supported our investigation of brain network properties. Analysis focused primarily on the measurement of whole‐brain network properties.ResultWe observed differences in brain variables between the PRANK participants and the dataset of healthy young adults. Between‐group differences were observed in modularity scores and other graph theoretical measures of network organization. We also observed relationship between the functional network properties of periadolescent children and their cognitive performance on the CANTAB task.ConclusionOur ongoing study measures the association of brain variables, cognitive performance, and AD polygenic risk score in periadolescent children. Our preliminary analysis of brain and cognitive measures illustrate functional network properties of the periadolescent brain with a focus on associations with cognitive performance. Future directions include examining the association between AD polygenic risk score and the same network properties.