Changes In Functional Networks Research Articles

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.

A study of brain functional network and alertness changes in temporal lobe epilepsy with and without focal to bilateral tonic\u2013clonic seizures

BackgroundTemporal lobe epilepsy (TLE) is commonly refractory. Epilepsy surgery is an effective treatment strategy for refractory epilepsy, but patients with a history of focal to bilateral tonic-clonic seizures (FBTCS) have poor outcomes. Previous network studies on epilepsy have found that TLE and idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTCS) showed altered global and nodal topological properties. Alertness deficits also were found in TLE. However, FBTCS is a common type of seizure in TLE, and the implications for alertness as well as the topological rearrangements associated with this seizure type are not well understood.MethodsWe obtained rs-fMRI data and collected the neuropsychological assessment data from 21 TLE patients with FBTCS (TLE- FBTCS), 18 TLE patients without FBTCS (TLE-non- FBTCS) and 22 controls, and constructed their respective functional brain networks. The topological properties were analyzed using the graph theoretical approach and correlations between altered topological properties and alertness were analyzed.ResultsWe found that TLE-FBTCS patients showed more serious impairment in alertness effect, intrinsic alertness and phasic alertness than the patients with TLE-non-FBTCS. They also showed significantly higher small-worldness, normalized clustering coefficient (γ) and a trend of higher global network efficiency (gE) compared to TLE-non-FBTCS patients. The gE showed a significant negative correlation with intrinsic alertness for TLE-non-FBTCS patients.ConclusionOur findings show different impairments in brain network information integration, segregation and alertness between the patients with TLE-FBTCS and TLE-non-FBTCS, demonstrating that impairments of the brain network may underlie the disruptions in alertness functions.

The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics

Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a “network collapse”. After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.

Frequency-dependent white-matter functional network changes associated with cognitive deficits in subcortical vascular cognitive impairment

Vascular cognitive impairment (VCI) refers to all forms of cognitive decline associated with cerebrovascular diseases, in which white matter (WM) is highly vulnerable. Although previous studies have shown that blood oxygen level-dependent (BOLD) signals inside WM can effectively reflect neural activities, whether WM BOLD signal alterations are present and their roles underlying cognitive impairment in VCI remain largely unknown. In this study, 36 subcortical VCI (SVCI) patients and 36 healthy controls were enrolled to evaluate WM dysfunction. Specifically, fourteen distinct WM networks were identified from resting-state functional MRI using K-means clustering analysis. Subsequently, between-network functional connectivity (FC) and within-network BOLD signal amplitude of WM networks were calculated in three frequency bands (band A: 0.01–0.15 Hz, band B: 0.08–0.15 Hz, and band C: 0.01–0.08 Hz). Patients with SVCI manifested decreased FC mainly in bilateral parietal WM regions, forceps major, superior and inferior longitudinal fasciculi. These connections extensively linked with distinct WM networks and with gray-matter networks such as frontoparietal control, dorsal and ventral attention networks, which exhibited frequency-specific alterations in SVCI. Additionally, extensive amplitude reductions were found in SVCI, showing frequency-dependent properties in parietal, anterior corona radiate, pre/post central, superior and inferior longitudinal fasciculus networks. Furthermore, these decreased FC and amplitudes showed significant positive correlations with cognitive performances in SVCI, and high diagnostic performances for SVCI especially combining all bands. Our study indicated that VCI-related cognitive deficits were characterized by frequency-dependent WM functional abnormalities, which offered novel applicable neuromarkers for VCI.

A tractometry principal component analysis of white matter tract network structure and relationships with cognitive function in relapsing-remitting multiple sclerosis

Understanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis (MS) to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts have network structure it would be expected that tracts within a network share susceptibility to MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in white matter metrics across white matter (WM) tracts, and assessed how such patterns relate to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract co-variance patterns to predict test performance across cognitive domains. We found that a single co-variance pattern which captured microstructure across all tracts explained the most variance (65% variance explained) and that there was little evidence for separate, smaller network patterns of pathology. Variance in this pattern was explained by effects related to lesions, but one main co-variance pattern persisted after this effect was regressed out. This main WM tract co-variance pattern contributed to explaining a modest degree of variance in one of our four cognitive domains in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.

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.

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.

Task effects on functional connectivity measures after stroke.

Understanding the effect of task compared to rest on detecting stroke-related network abnormalities will inform efforts to optimize detection of such abnormalities. The goal of this work was to determine whether connectivity measures obtained during an overt task are more effective than connectivity obtained during a "resting" state for detecting stroke-related changes in network function of the brain. This study examined working memory, discrete pedaling, continuous pedaling and language tasks. Functional magnetic resonance imaging was used to examine regional and inter-regional brain network function in 14 stroke and 16 control participants. Independent component analysis was used to identify 149 regions of interest (ROI). Using the inter-regional connectivity measurements, the weighted sum was calculated across only regions associated with a given task. Both inter-regional connectivity and regional connectivity were greater during each of the tasks as compared to the resting state. The working memory and discrete pedaling tasks allowed for detection of stroke-related decreases in inter-regional connectivity, while the continuous pedaling and language tasks allowed for detection of stroke-related enhancements in regional connectivity. These observations illustrate that task-based functional connectivity allows for detection of stroke-related changes not seen during resting states. In addition, this work provides evidence that tasks emphasizing different cognitive domains reveal different aspects of stroke-related reorganization. We also illustrate that within the motor domain, different tasks can reveal inter-regional or regional stroke-related changes, in this case suggesting that discrete pedaling required more central drive than continuous pedaling.

Age‐dependent functional connectivity changes across large‐scale brain networks disrupt memory consolidation processes

AbstractBackgroundEpisodic memory decline characterizes “normal” aging but is also a core symptom of Alzheimer’s disease. Therefore, considerable efforts have been undertaken to study this memory domain and its neural substrates. Recent studies have mainly focused on encoding‐ and retrieval‐ related processes. However, some findings suggest that episodic memory deficits are at least in part due to impaired consolidation processes.MethodsWe investigated age effects on consolidation‐related functional network changes and their vulnerability to cognitive interference, using fMRI data of 13 young (20–30 years) and 16 senior (49–75 years) participants. FMRI scanning was performed prior to and during a spatial memory paradigm, consisting of encoding, consolidation, and retrieval phases. To ensure that the observed connectivity changes were due to consolidation processes, the consolidation phase was manipulated over two sessions: (1) no manipulation and (2) repeated stimulus‐presentation with interfering spatial context information.ResultsWe compared the fractional amplitude of low‐frequency fluctuations (fALFF) to analyze spontaneous brain activity changes from pre‐ to post‐encoding rest. In the control condition, fALFF decreased within left supramarginal gyrus (SMG), right middle temporal gyrus (MTL), and left precuneus (Pc), whereas, fALFF increased within parts of the occipital and inferior temporal cortex. To evaluate the integration in large‐scale brain networks, connectivity analyses between the fALFF‐derived seeds and network ROIs were performed. We observed significant coupling between consolidation‐related brain regions and large‐scale brain networks during consolidation, with connectivity between the SMG and large parts of the memory network modulated by aging. There was also a disruptive effect of interference on connectivity between the Pc and parts of the salience network. Of particular importance for successful consolidation, however, is the connectivity between parts of the occipital lobe, the inferior and middle temporal gyrus. Connectivity within this network, which corresponds to the ventral visual stream, may predict subsequent memory performance. The predictive value appears to be higher in young than in seniors.ConclusionOur results support the assumption that age‐related memory decline is at least partly caused by impaired consolidation processes. We showed that consolidation requires a complex interplay between large‐scale brain networks, which is partially disrupted by aging and thus becomes insufficient.

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.

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.

Functional Neural Networks in Writer's Cramp as Determined by Graph-Theoretical Analysis.

Dystonia, a debilitating neurological movement disorder, is characterized by involuntary muscle contractions and develops from a complex pathophysiology. Graph theoretical analysis approaches have been employed to investigate functional network changes in patients with different forms of dystonia. In this study, we aimed to characterize the abnormal brain connectivity underlying writer's cramp, a focal hand dystonia. To this end, we examined functional magnetic resonance scans of 20 writer's cramp patients (11 females/nine males) and 26 healthy controls (10 females/16 males) performing a sequential finger tapping task with their non-dominant (and for patients non-dystonic) hand. Functional connectivity matrices were used to determine group averaged brain networks. Our data suggest that in their neuronal network writer's cramp patients recruited fewer regions that were functionally more segregated. However, this did not impair the network's efficiency for information transfer. A hub analysis revealed alterations in communication patterns of the primary motor cortex, the thalamus and the cerebellum. As we did not observe any differences in motor outcome between groups, we assume that these network changes constitute compensatory rerouting within the patient network. In a secondary analysis, we compared patients with simple writer's cramp (only affecting the hand while writing) and those with complex writer's cramp (affecting the hand also during other fine motor tasks). We found abnormal cerebellar connectivity in the simple writer's cramp group, which was less prominent in complex writer's cramp. Our preliminary findings suggest that longitudinal research concerning cerebellar connectivity during WC progression could provide insight on early compensatory mechanisms in WC.

Characteristics of Neural Network Changes in Normal Aging and Early Dementia.

To understand the mechanisms underlying preserved and impaired cognitive function in healthy aging and dementia, respectively, the spatial relationships of brain networks and mechanisms of their resilience should be understood. The hub regions of the brain, such as the multisensory integration and default mode networks, are critical for within- and between-network communication, remain well-preserved during aging, and play an essential role in compensatory processes. On the other hand, these brain hubs are the preferred sites for lesions in neurodegenerative dementias, such as Alzheimer’s disease. Disrupted primary information processing networks, such as the auditory, visual, and sensorimotor networks, may lead to overactivity of the multisensory integration networks and accumulation of pathological proteins that cause dementia. At the cellular level, the brain hub regions contain many synapses and require a large amount of energy. These regions are rich in ATP-related gene expression and had high glucose metabolism as demonstrated on positron emission tomography (PET). Importantly, the number and function of mitochondria, which are the center of ATP production, decline by about 8% every 10 years. Dementia patients often have dysfunction of the ubiquitin-proteasome and autophagy-lysosome systems, which require large amounts of ATP. If there is low energy supply but the demand is high, the risk of disease can be high. Imbalance between energy supply and demand may cause accumulation of pathological proteins and play an important role in the development of dementia. This energy imbalance may explain why brain hub regions are vulnerable to damage in different dementias. Here, we review (1) the characteristics of gray matter network, white matter network, and resting state functional network changes related to resilience in healthy aging, (2) the mode of resting state functional network disruption in neurodegenerative dementia, and (3) the cellular mechanisms associated with the disruption.

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.

Resting-State Functional Magnetic Resonance Imaging of Interhemispheric Functional Connectivity in Experimental Traumatic Brain Injury.

Although resting-state functional magnetic resonance imaging (rsfMRI) has the potential to offer insights into changes in functional connectivity networks after traumatic brain injury (TBI), there are few studies that examine the effects of moderate TBI for monitoring functional recovery in experimental TBI, and thus the neural correlates of brain recovery from moderate TBI remain incompletely understood. Non-invasive rsfMRI was used to longitudinally investigate changes in interhemispheric functional connectivity (IFC) after a moderate TBI to the unilateral sensorimotor cortex in rats (n = 9) up to 14 days. Independent component analysis of the rsfMRI data was performed. Correlations of rsfMRI sensorimotor networks were made with changes in behavioral scores, lesion volume, and T2- and diffusion-weighted images across time. TBI animals showed less localized rsfMRI patterns in the sensorimotor network compared to sham (n = 6) and normal (n = 5) animals. rsfMRI clusters in the sensorimotor network showed less bilateral symmetry compared to sham and normal animals, indicative of IFC disruption. With time after injury, many of the rsfMRI patterns in the sensorimotor network showed more bilateral symmetry, indicative of IFC recovery. The disrupted IFC in the sensorimotor and subsequent partial recovery showed a positive correlation with changes in behavioral scores. Overall, rsfMRI detected widespread disruption and subsequent recovery of IFC within the sensorimotor networks post-TBI, which correlated with behavioral changes. Therefore, rsfMRI offers the means to probe functional brain reorganization and thus has the potential to serve as an imaging marker to longitudinally stage TBI and monitor for novel treatments.

Changes of Structural and Functional Attention Control Networks in Subclinical Hypothyroidism

Objective: This study aimed to explore the structural changes in patients with subclinical hypothyroidism (SCH) using voxel-based morphometry (VBM) and to investigate the altered attentional control networks using functional MRI (fMRI) during the performance of a modified Stroop task with Chinese characters.Methods: High-resolution three-dimensional (3D) T1-weighted images and an fMRI scan were taken from 18 patients with SCH and 18 matched control subjects. The Montreal Cognitive Assessment Chinese-revised (MoCA-CR) and the Stroop task were used to evaluate the cognitive and attention control of the participants.Results: Compared to controls, the VBM results showed decreased gray matter volumes (GMVs) in bilateral prefrontal cortices (PFCs, including middle, medial, and inferior frontal gyri), cingulate gyrus, precuneus, left middle temporal gyrus, and insula in patients with SCH. The fMRI results showed a distributed network of brain regions in both groups, consisting of PFCs (including superior and middle and inferior frontal cortices), anterior cingulate cortex (ACC), posterior cingulate cortex, and precuneus, as well as the insula and caudate nucleus. Compared to controls, the SCH group had lower activation of the above brain areas, especially during the color-naming task. In addition, the normalized GMV (nGMV) was negatively correlated with thyroid-stimulating hormone (TSH) level (r = −0.722, p < 0.001).Conclusion: Results indicate that patients with SCH exhibit reduced GMVs, altered BOLD signals, and activation in regions associated with attention control, which further suggest that patients with SCH may have attentional control deficiency, and the weakened PFC–ACC–precuneus brain network might be one of the neural mechanisms. Negative correlations between nGMV and TSH suggest that TSH elevation may induce abnormalities in the cortex.

Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence.

Patients with multiple sclerosis (MS) have heterogeneous clinical presentations, symptoms, and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using MRI. First, development of validated MS-specific image analysis methods can be boosted by verified reference, test, and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic, and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy, or functional network changes) to large multidomain datasets (imaging, cognition, clinical disability, genetics). After reviewing data sharing and artificial intelligence, we highlight 3 areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging, and the understanding of MS.

Pregnancy leads to changes in the brain functional network: a connectome analysis.

Pregnancy leads to long-lasting changes in human brain structure; however, little is known regarding alterations in the topological organization of functional networks. In this study, we investigated the effect of pregnancy on human brain function networks. Resting-state fMRI data was collected from eighteen primiparous mothers and twenty-four nulliparous control women of similar age, education level and body mass index (BMI). The functional brain network and topological properties were calculated by using GRETNA toolbox. The demographic data differences between two groups were computed by the independent two sample t-test. We tested group differences in network metrics' area under curve (AUC) using non-parametric permutation test of 1,000 permutations and corrected for false discovery rate (FDR). Differences in regional networks between groups were evaluated using non-parametric permutation tests by network-based statistical analysis (NBS). Compared with the nulliparous control women, a hub node changed from left inferior temporal gyrus to right precentral gyrus in primiparous mothers, while primiparous mothers showed enhanced network global efficiency (p = 0.247), enhanced local efficiency (p = 0.410), larger clustering coefficient (p = 0.410), but shorter characteristic path length (p = 0.247), smaller normalized clustering coefficient (p = 0.111), and shorter normalized characteristic path length (p = 0.705). Although both groups of functional networks have small-world property (σ > 1), the σ values of primiparous mothers were decreased significantly. NBS evaluation showed the majority of altered connected sub-network in the primiparous mothers occurred in the bilateral frontal lobe gyrus (p < 0.05). Altered functional network metrics and an abnormal sub-network were found in primiparous mothers, suggested that pregnancy may lead to changes in the brain functional network.

Controlling extended criticality via modular connectivity

Criticality has been conjectured as an integral part of neuronal network dynamics. Operating at a critical threshold requires precise parameter tuning and a corresponding mechanism remains an open question. Recent studies have suggested that topological features observed in brain networks give rise to a Griffiths phase, leading to power-law scaling in brain activity dynamics and the operational benefits of criticality in an extended parameter region. Motivated by growing evidence of neural correlates of different states of consciousness, we investigate how topological changes affect the expression of a Griffiths phase. We analyze the activity decay in modular networks using a susceptible-infected-susceptible propagation model and find that we can control the extension of the Griffiths phase by altering intra- and intermodular connectivity. We find that by adjusting system parameters, we can counteract changes in critical behavior and maintain a stable critical region despite changes in network topology. Our results give insight into how structural network properties affect the emergence of a Griffiths phase and how its features are linked to established topological network metrics. We discuss how those findings could contribute to an understanding of the changes in functional brain networks.

Altered cerebrocerebellar functional connectivity in patients with obstructive sleep apnea and its association with cognitive function.

Previous functional MRI studies have reported altered brain networks in patients with obstructive sleep apnea (OSA). However, the extent and pattern of abnormal connectivity were inconsistent across studies, and cerebrocerebellar connections have been rarely assessed. We investigated functional network changes in cerebral and cerebellar cortices of OSA patients. Resting-state functional MRI, polysomnography, and neuropsychological (NP) test data were acquired from 74 OSA patients (age: 45.8 ± 10.7 years) and 33 healthy subjects (39.6 ± 9.3 years). Connectivity matrices were extracted by computing correlation coefficients from various regions of interest, and Fisher r-to-z transformations. In the functional connections that showed significant group differences, linear regression was conducted to examine the association between connectivity and clinical characteristics. Patients with OSA showed reduced functional connectivity (FC) in cerebrocerebellar connections linking different functional networks, and greater FC in cortical between-network connections in prefrontal regions involving the default mode network (DMN) and the control network. For OSA group, we found no correlation between FC and sleep parameters including lowest SaO2 and arousal index in the connections where significant associations were observed in healthy subjects. FC changes in DMN areas were related to reduced verbal fluency in OSA. Lower local efficiency and lower clustering coefficient of the salience network in the left cerebellum were also observed in OSA. OSA affects mainly the cerebrocerebellar pathway. The disruption of function in these connections are related to sleep fragmentation and hypoxia during sleep. These abnormal network functions, especially DMN, are suggested to participate in cognitive decline of OSA.