Functional connectivity and graph theory in preclinical Alzheimer's disease

Alzheimer's disease (AD) has a long preclinical phase in which amyloid and tau cerebral pathology accumulate without producing cognitive symptoms. Resting state functional connectivity magnetic resonance imaging has demonstrated that brain networks degrade during symptomatic AD. It is unclear to what extent these degradations exist before symptomatic onset. In this study, we investigated graph theory metrics of functional integration (path length), functional segregation (clustering coefficient), and functional distinctness (modularity) as a function of disease severity. Further, we assessed whether these graph metrics were affected in cognitively normal participants with cerebrospinal fluid evidence of preclinical AD. Clustering coefficient and modularity, but not path length, were reduced in AD. Cognitively normal participants who harbored AD biomarker pathology also showed reduced values in these graph measures, demonstrating brain changes similar to, but smaller than, symptomatic AD. Only modularity was significantly affected by age. We also demonstrate that AD has a particular effect on hub-like regions in the brain. We conclude that AD causes large-scale disconnection that is present before onset of symptoms.     

Extracting biomarkers of autism from MEG resting-state functional connectivity networks

The present study is a preliminary attempt to use graph theory for deriving distinct features of resting-state functional networks in young adults with autism spectrum disorder (ASD). Networks modeled neuromagnetic signal interactions between sensors using three alternative interdependence measures: (a) a non-linear measure of generalized synchronization (robust interdependence measure [RIM]), (b) mutual information (MI), and (c) partial directed coherence (PDC). To summarize the information contained in each network model we employed well-established global graph measures (average strength, assortativity, clustering, and efficiency) as well as graph measures (average strength of edges) tailored to specific hypotheses concerning the spatial distribution of abnormalities in connectivity among individuals with ASD. Graph measures then served as features in leave-one-out classification analyses contrasting control and ASD participants. We found that combinations of regionally constrained graph measures, derived from RIM, performed best, discriminating between the two groups with 93.75% accuracy. Network visualization revealed that ASD participants displayed significantly reduced interdependence strength, both within bilateral frontal and temporal sensors, as well as between temporal sensors and the remaining recording sites, in agreement with previous studies of functional connectivity in this disorder.     

Cortical Network Analysis in Patients Affected by Schizophrenia

In the present study, we studied the structural changes of the brain functional network in a group of schizophrenic (SCHZ) patients during a 2-back working memory task. Cortical signals were obtained from scalp EEG signals through the high-resolution EEG technique, which relies on realistic head models and linear inverse solutions. Functional networks were estimated by computing the spectral coherence—i.e. a measure of synchronization in the frequency domain—between the time series of all the available cortical sources. To analyze those cortical networks we followed a theoretical graph approach by computing the network density as the total number of links and the node degree as the number of links of each cortical source. The major result suggest that in the Alpha2 frequency band (11–13 Hz) the cortical functional networks of the SCHZ patients present the largest differences when compared with those of a group of control (CTRL) subjects. In particular, the structure of the SCHZ network altered radically during the memory task, as the number of links that were different from the REST condition increased sensibly with respect to the CTRL network. In addition, a compensatory mechanism was found in the SCHZ patients during the correct performance of the memory task where the node degree showed a frontal asymmetry with higher activation of the left frontal lobe—i.e. higher number of connections—in the Alpha2 frequency band.     

The meaning of mammalian adult neurogenesis and the function of newly added neurons: the "small-world" network

Adult neurogenesis has been observed in mammalian brain including human but a question remains: how do new neurons become functional in the adult brain? We propose that the random addition of only a few new neurons functions as a maintenance system for the brain's “small-world” networks. In popular parlance, the small-world network phenomenon is described by the concept of “six degrees of separation”, which postulates that everyone in the world is connected to everyone else through a chain of at most six mutual acquaintances. Randomly added to an orderly network, new links enhance signal propagation speed and synchronizability. Newly generated neurons are ideally suited to become such links: they are immature, form more new connections compared to mature ones, and their number but not their precise location may be maintained by continuous proliferation and dying off. Similarly, we envisage that the treatment of brain pathologies by cell transplantation would also create new random links in small-world networks and that even a small number of successfully incorporated new neurons may be functionally important.     

Disrupted brain connectome in semantic variant of primary progressive aphasia

Using resting-state functional magnetic resonance imaging and graph analysis, the topological organization of the functional brain network connectivity was explored in patients with left-sided onset semantic variant (SV) of primary progressive aphasia relative to healthy controls. Functional brain networks in SV patients were characterized by a significantly lower mean network degree, clustering coefficient, and global efficiency, longer characteristic path length and higher assortativity compared with controls. SV patients showed also a strongly left-lateralized loss of hubs, and reduced nodal degree in the inferior and ventral temporal regions and occipital cortices. In SV, the decreased nodal degree extended into the medial and ventral frontal cortex bilaterally, left amygdala and/or hippocampus, and left caudate nucleus. These findings provide evidence that the focal structural degeneration of the inferior temporal, and perysilvian language regions in SV patients ultimately results in a distributed pattern of functional connectivity abnormalities. The local network analysis shows that SV is associated with a functional degradation in the “pan-modal” inferior and/or ventral temporal regions, and the “modality-specific” visual cortical origin of the ventral processing pathway.     

Which attention-deficit/hyperactivity disorder children will be improved through neurofeedback therapy? A graph theoretical approach to neocortex neuronal network of ADHD

Neurofeedback training is increasingly used for ADHD treatment. However some ADHD patients are not treated through the long-time neurofeedback trainings with common protocols. In this paper a new graph theoretical approach is presented for EEG-based prediction of ADHD patients' responses to a common neurofeedback training: rewarding SMR activity (12-15 Hz) with inhibiting theta activity (4-8 Hz) and beta2 activity (18-25 Hz). Eyes closed EEGs of two groups before and after neurofeedback training were studied: ADHD patients with (15 children) and without (15 children) positive response to neurofeedback training. Employing a recent method to measure synchronization, fuzzy synchronization likelihood, functional connectivity graphs of the patients' brains were constructed in the full-band EEGs and 6 common EEG sub-bands produced by wavelet decomposition. Then, efficiencies of the brain networks in synchronizability and high speed information transmission were computed based on mean path length of the graphs, before and after neurofeedback training. The results were analyzed by ANOVA and showed synchronizability of the neocortex activity network at beta band in ADHDs with positive response is obviously less than that of ADHDs resistant to neurofeedback therapy, before treatment. The accuracy of linear discriminant analysis (LDA) in distinguishing these patients based on this feature is so high (84.2%) that this feature can be considered as reliable characteristics for prediction of responses of ADHDs to the neurofeedback trainings. Also difference between flexibility of the neocortex in beta band before and after treatment is obviously larger in the ADHDs with positive response in comparison to those with negative response which may be a neurophysiologic reason for dissatisfaction of the last group to the neurofeedback therapy.     

认知负载状态下的精神分裂症患者脑电图特异性研究

认知功能损害是精神分裂症的三大原发症状之一,在疾病早期发现和高危人群风险预警等方面具有重要价值。为了研究精神分裂症患者在认知负载状态下的脑电图特异性,本试验收集17例精神分裂症患者和19例健康受试者的脑电信号作为对照,基于小波变换提取各频段信号,计算非线性动力学及脑功能网络属性等特征,并利用机器学习算法将两类人群进行自动分类分析。试验结果表明,两组受试者在认知负载状态下,Fp1和Fp2导联在α、β、θ、γ这4个频带的关联维数和样本熵的差异均具有统计学意义,提示大脑额叶功能损伤是精神分裂症认知功能损害的重要原因。进一步基于机器学习的自动分类分析结果表明,将非线性动力学与脑功能网络属性相结合作为分类器的输入特征,所得分类效果最优,其结果显示准确率为76.77%、敏感度为72.09%、特异性为80.36%。本研究结果表明,脑电信号的非线性动力学和脑功能网络属性等特征,或可作为精神分裂症早期筛查和辅助诊断的潜在生物标记物。     

A quantitative neural network approach to understanding aging phenotypes

Basic research on neurocognitive aging has traditionally adopted a reductionist approach in the search for the basis of cognitive preservation versus decline. However, increasing evidence suggests that a network level understanding of the brain can provide additional novel insight into the structural and functional organization from which complex behavior and dysfunction emerge. Using graph theory as a mathematical framework to characterize neural networks, recent data suggest that alterations in structural and functional networks may contribute to individual differences in cognitive phenotypes in advanced aging. This paper reviews literature that defines network changes in healthy and pathological aging phenotypes, while highlighting the substantial overlap in key features and patterns observed across aging phenotypes. Consistent with current efforts in this area, here we outline one analytic strategy that attempts to quantify graph theory metrics more precisely, with the goal of improving diagnostic sensitivity and predictive accuracy for differential trajectories in neurocognitive aging. Ultimately, such an approach may yield useful measures for gauging the efficacy of potential preventative interventions and disease modifying treatments early in the course of aging.     

Disrupted cortical network as a vulnerability marker for obsessive–compulsive disorder

Morphological alterations of brain structure are generally assumed to be involved in the pathophysiology of obsessive–compulsive disorder (OCD). Yet, little is known about the morphological connectivity properties of structural brain networks in OCD or about the heritability of those morphological connectivity properties. To better understand these properties, we conducted a study that defined three different groups: OCD group with 30 subjects, siblings group with 19 subjects, and matched controls group with 30 subjects. A structural brain network was constructed using 68 cortical regions of each subject within their respective group (i.e., one brain network for each group). Both small-worldness and modularity were measured to reflect the morphological connectivity properties of each constructed structural brain network. When compared to the matched controls, the structural brain networks of patients with OCD indeed exhibited atypical small-worldness and modularity. Specifically, small-worldness showed decreased local efficiency, and modularity showed reduced intra-connectivity in Module III (default mode network) and increased interconnectivity between Module I (executive function) and Module II (cognitive control/spatial). Intriguingly, the structured brain networks of the unaffected siblings showed similar small-worldness and modularity as OCD patients. Based on the atypical structural brain networks observed in OCD patients and their unaffected siblings, abnormal small-worldness and modularity may indicate a candidate endophenotype for OCD.     

颞叶癫痫致痫侧静息态脑功能网络拓扑的特异性研究

癫痫是世界常见的典型脑内异常神经放电导致中枢认知功能网络失调的神经疾病.作为当今先进技术的静息态功能磁共振成像(rfMRI),其功能连接(fMRI-FC)可为评估脑功能提供科学的检测指标.在此提出参照健康人的癫痫脑功能网络多节点指标融合的特异性模型,提取致痫侧脑功能网络拓扑属性,试图在高阶计算fMRI-FC,以提高其检...     

Aberrant brain activation following motor skill learning in schizophrenic patients as shown by functional magnetic resonance imaging

BACKGROUND: Motor skill learning may be impaired in schizophrenia. While functional brain imaging studies have shown reduced activation during motor task performance in schizophrenic patients, brain activity changes with motor skill learning in these patients have not been studied by functional imaging. METHODS: A sequential complex motor task involving the right hand was performed by nine medicated schizophrenic patients and 10 age-matched healthy controls. Functional magnetic resonance images were obtained using a gradient echo, echoplanar imaging (EPI) pulse sequence before and after 1 week of training in performing the task. RESULTS: Bilaterally, patients showed significantly less blood oxygenation level-dependent (BOLD) signal response in the premotor area (PMA) before beginning motor training than controls. BOLD signal response increased in the left PMA of schizophrenic patients after 1 week of motor training; in contrast, the signal decreased in the left PMA of control subjects. Training effects concerning the number of finger movement sequences achieved did not differ between groups. Daily neuroleptic dose did not significantly affect changes with training in BOLD signal response in the PMA. CONCLUSIONS: These preliminary results suggest that schizophrenic patients have dysfunction of neural networks in areas including the PMA that are involved in executing a complex motor task. In terms of brain activity, motor learning may be less efficient or slower in the patients than in healthy subjects.     

抑郁症的脑复杂网络研究进展

抑郁症是一种伴有情绪和认知功能损害、临床表现多样的常见精神性疾病。随着影像技术的发展,抑郁症伴随着大脑特定区域的结构和功能网络异常已被得到广泛证实。基于图论的脑复杂网络分析提示了抑郁症中大规模功能性和结构性脑网络的拓扑结构紊乱,这为早期发现抑郁症提供了潜在的有价值的生物标记物。本文综述近年来抑郁症的脑复杂网络研究进展,进而分析现有研究中基本结果和潜在不足,最后展望了未来可能的发展方向。     

Schizophrenic speech as a disordered trajectory in a collapsed cognitive "Small-World"

New theoretical instruments, as goal-directed neural networks models and geometric representations based on semantic graphs, open new approaches for our understanding of the schizophrenic speech. The neuropathologic disorders of the schizophrenia can be simulated using neural models, and these models can eventually explain the origin of goal confusion and incoherence in the schizophrenic discourse trajectory. Moreover, these models are useful to evaluate the different hypothesis about the pathogenic mechanisms of the disease. At the same time, a geometric representation of the trajectory of the speech can be obtained from real data. Our conjecture is that a context-dependent graph can be constructed in order to explore if, when the disease became more severe, a transition from a quasi ordered graph to a nearly completely random graph occurs. Plausibly, there exists a wide region where the graph has the properties of a "small-world". This kind of analyses could be potentially carried out using data coming from the spontaneous speech of schizophrenic patients, and can help to evaluate the progress of the disease. At the same time, these geometrical representations could help to evaluate the effect of treatments.     

The older adult brain is less modular,more integrated,and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.     

Altered structural networks and executive deficits in traumatic brain injury patients

Recent research on traumatic brain injury (TBI) has shown that impairments in cognitive and executive control functions are accompanied by a disrupted neural connectivity characterized by white matter damage. We constructed binary and weighted brain structural networks in 21 patients with chronic TBI and 17 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Executive function was assessed with the local global task and the trail making task, requiring inhibition, updating, and switching. The results revealed that TBI patients were less successful than controls on the executive tasks, as shown by the higher reaction times, higher switch costs, and lower accuracy rates. Moreover, both TBI patients and controls exhibited a small world topology in their white matter networks. More importantly, the TBI patients demonstrated increased shortest path length and decreased global efficiency of the structural network. These findings suggest that TBI patients have a weaker globally integrated structural brain network, resulting in a limited capacity to integrate information across brain regions. Furthermore, we showed that the white matter networks of both groups contained highly connected hub regions that were predominately located in the parietal cortex, frontal cortex, and basal ganglia. Finally, we showed significant correlations between switching performance and network property metrics within the TBI group. Specifically, lower scores on the switching tasks corresponded to a lower global efficiency. We conclude that analyzing the structural brain network connectivity provides new insights into understanding cognitive control changes following brain injury.     

Age-related changes in the topological architecture of the brain during hand grip

Neuroanatomical changes in the aging brain are widely distributed rather than focal. We investigated age-related changes in large-scale functional brain networks by applying graph theory to functional magnetic resonance imaging data acquired during a simple grip task with either dominant or nondominant hand. We measured the effect of age on efficiency of information transfer within a series of hierarchical functional networks composed of the whole brain or component parts of the whole brain. Global efficiency was maintained with aging during dominant hand use, primarily due to increased efficiency in parietal-occipital-cerebellar-related networks. During nondominant hand use, global efficiency, as well as efficiency within ipsilateral hemisphere and between hemispheres declined with age. This was attributable largely to frontal-temporal-limbic-cerebellar-related networks. Increased efficiency with age was seen in networks involving parietal-occipital regions, but unlike for dominant hand use, this topological reconfiguration could not maintain the level of global efficiency. Here, graph theoretical approaches have demonstrated both compensatory and noncompensatory changes in topological configuration of large-scale networks during aging depending on the task.     

Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques

Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-d-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n?=?18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p?<?0.00001); however, community structure is similar at network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.     

Deficits in task-set maintenance and execution networks in Parkinson’s disease

Patients with Parkinson’s disease have difficulties with self-initiating a task and maintaining a steady task performance. We hypothesized that these difficulties relate to reorganization in the sensorimotor execution, cingulo-opercular task-set maintenance, and frontoparietal adaptive control networks. We tested this hypothesis using graph theory-based network analysis of a composite network including a total of 86 nodes, derived from the three networks of interest. Resting-state functional magnetic resonance images were collected from 30 patients with Parkinson’s disease (age 42–75 years, 11 females; Hoehn and Yahr score 2–3, average 2.4 ± 0.4) in their off-medication state and 30 matched control subjects (age 44–75 years, 10 females). For each node, we calculated strength as a general measure of connectivity, global efficiency and betweenness centrality as measures of functional integration, and clustering coefficient and local efficiency as measures of functional segregation. We found reduced node strength, clustering, and local efficiency in sensorimotor and posterior temporal nodes. There was also reduced node strength and betweenness centrality in the dorsal anterior insula and temporoparietal junction nodes of the cingulo-opercular network. These nodes are involved in integrating multimodal information, specifically related to self-awareness, sense of agency, and ultimately to intact perception of self-in-action. Moreover, we observed significant correlations between global disease severity and averaged graph metrics of the whole network. In addition to the well-known task-related frontostriatal mechanisms, we propose that the resting-state reorganization in the composite network can contribute to problems with self-initiation and task-set maintenance in Parkinson’s disease.     

基于DTI复杂网络的两性大脑差异性研究

为解释男女大脑差异是如何影响人们的思维和行为的,我们利用DTI复杂网络技术对男女两性大脑的差异性进行了定量分析。将44例健康志愿者分成男女两组,利用磁共振弥散张量成像对志愿者进行扫描和数据采集,通过构建脑白质纤维结构网络,并结合图论知识和复杂网络特征度量参数,对男女大脑结构差异及其在功能上的体现进行分析。对两组志愿者的脑结构网络分析发现,男女两组志愿者均具有小世界属性。结构网络局部参数分析显示,男女在涉及语言、情感、运动、方向感等的脑区具有显著差异。男女两性大脑生理结构的差别确实会导致大脑在功能上出现差别,两性大脑在功能上的差别可认为是两性在负责相关功能的脑区所具有的差异所导致。     

Combining structural and functional neuroimaging data for studying brain connectivity: a review

Different brain areas are thought to be integrated into large-scale networks to support cognitive function. Recent approaches for investigating structural organization and functional coordination within these networks involve measures of connectivity among brain areas. We review studies combining in vivo structural and functional brain connectivity data, where (a) structural connectivity analysis, mostly based on diffusion tensor imaging is paired with voxel-wise analysis of functional neuroimaging data or (b) the measurement of functional connectivity based on covariance analysis is guided/aided by structural connectivity data. These studies provide insights into the relationships between brain structure and function. Promising trends involve (a) studies where both functional and anatomical connectivity data are collected using high-resolution neuroimaging methods and (b) the development of advanced quantitative models of integration.