Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography

Purpose

Brain tumor patients are usually accompanied by impairments in cognitive functions, and these dysfunctions arise from the altered diffusion tensor of water molecules and disrupted neuronal conduction in white matter. Diffusion tensor imaging (DTI) is a powerful noninvasive imaging technique that can reflect diffusion anisotropy of water and brain white matter neural connectivity in vivo. This study was aimed to analyze the topological properties and connection densities of the brain anatomical networks in brain tumor patients based on DTI and provide new insights into the investigation of the structural plasticity and compensatory mechanism of tumor patient’s brain.

Methods

In this study, the brain anatomical networks of tumor patients and healthy controls were constructed using the tracking of white matter fiber bundles based on DTI and the topological properties of these networks were described quantitatively. The statistical comparisons were performed between two groups with six DTI parameters: degree, regional efficiency, local efficiency, clustering coefficient, vulnerability, and betweenness centrality. In order to localize changes in structural connectivity to specific brain regions, a network-based statistic approach was utilized. By comparing the edge connection density of brain network between two groups, the edges with greater difference in connection density were associated with three functional systems.

Results

Compared with controls, tumor patients show a significant increase in small-world feature of cerebral structural network. Two-sample two-tailed t test indicates that the regional properties are altered in 17 regions (\(p<0.05\)). Study reveals that the positive and negative changes in vulnerability take place in the 14 brain areas. In addition, tumor patients lose 3 hub regions and add 2 new hubs when compared to normal controls. Eleven edges show much significantly greater connection density in the patients than in the controls. Most of the edges with greater connection density are linked to regions located in the limbic/subcortical and other systems. Besides, most of the edges connect the two hemispheres of the brains.

Conclusion

The stronger small-world property in the tumor patients proves the existence of compensatory mechanism. The changes in the regional properties, especially the betweenness centrality and vulnerability, aid in understanding the brain structural plasticity. The increased connection density in the tumor group suggests that tumors may induce reorganization in the structural network.

     

Anatomical insights into disrupted small-world networks in schizophrenia

Schizophrenia is characterized by lowered efficiency in distributed information processing, as indicated by research that identified a disrupted small-world functional network. However, whether the dysconnection manifested by the disrupted small-world functional network is reflected in underlying anatomical disruption in schizophrenia remains unresolved. This study examined the topological properties of human brain anatomical networks derived from diffusion tensor imaging in patients with schizophrenia and in healthy controls. We constructed the weighted brain anatomical network for each of 79 schizophrenia patients and for 96 age and gender matched healthy subjects using diffusion tensor tractography and calculated the topological properties of the networks using a graph theoretical method. The topological properties of the patients' anatomical networks were altered, in that global efficiency decreased but local efficiency remained unchanged. The deleterious effects of schizophrenia on network performance appear to be localized as reduced regional efficiency in hubs such as the frontal associative cortices, the paralimbic/limbic regions and a subcortical structure (the left putamen). Additionally, scores on the Positive and Negative Symptom Scale correlated negatively with efficient network properties in schizophrenia. These findings suggest that complex brain network analysis may potentially be used to detect an imaging biomarker for schizophrenia.     

Characteristics and variability of structural networks derived from diffusion tensor imaging

Structural brain networks were constructed based on diffusion tensor imaging (DTI) data of 59 young healthy male adults. The networks had 68 nodes, derived from FreeSurfer parcellation of the cortical surface. By means of streamline tractography, the edge weight was defined as the number of streamlines between two nodes normalized by their mean volume. Specifically, two weighting schemes were adopted by considering various biases from fiber tracking. The weighting schemes were tested for possible bias toward the physical size of the nodes. A novel thresholding method was proposed using the variance of number of streamlines in fiber tracking. The backbone networks were extracted and various network analyses were applied to investigate the features of the binary and weighted backbone networks. For weighted networks, a high correlation was observed between nodal strength and betweenness centrality. Despite similar small-worldness features, binary networks and weighted networks are distinctive in many aspects, such as modularity and nodal betweenness centrality. Inter-subject variability was examined for the weighted networks, along with the test-retest reliability from two repeated scans on 44 of the 59 subjects. The inter-/intra-subject variability of weighted networks was discussed in three levels - edge weights, local metrics, and global metrics. The variance of edge weights can be very large. Although local metrics show less variability than the edge weights, they still have considerable amounts of variability. Weighting scheme one, which scales the number of streamlines by their lengths, demonstrates stable intra-class correlation coefficients against thresholding for global efficiency, clustering coefficient and diversity. The intra-class correlation analysis suggests the current approach of constructing weighted network has a reasonably high reproducibility for most global metrics.     

Effects of network resolution on topological properties of human neocortex

Graph theoretical analyses applied to neuroimaging datasets have provided valuable insights into the large-scale anatomical organization of the human neocortex. Most of these studies were performed with different cortical scales leading to cortical networks with different levels of small-world organization. The present study investigates how resolution of thickness-based cortical scales impacts on topological properties of human anatomical cortical networks. To this end, we designed a novel approach aimed at determining the best trade-off between small-world attributes of anatomical cortical networks and the number of cortical regions included in the scale. Results revealed that schemes comprising 540-599 regions (surface areas spanning between 250 and 275 mm²) at sparsities below 10% showed a superior balance between small-world organization and the size of the cortical scale employed. Furthermore, we found that the cortical scale representing the best trade-off (599 regions) was more resilient to targeted attacks than atlas-based schemes (Desikan-Killiany atlas, 66 regions) and, most importantly, it did not differ that much from the finest cortical scale tested in the present study (1494 regions). In summary, our study confirms that topological organization of anatomical cortical networks varies with both sparsity and resolution of cortical scale, and it further provides a novel methodological framework aimed at identifying cortical schemes that maximize small-worldness with the lowest scale resolution possible.     

静息态fMRI观察孤独症大脑功能网络

目的 观察孤独症患者和正常人全脑功能网络的差异。方法 将30例孤独症患者和30名正常人分为两组,行静息态fMRI采集,数据进行预处理,利用自动解剖标记模板分割大脑区域,提取时间序列信号,计算脑区间的相关性。基于复杂网络理论计算各种图论指标。对两组指标进行统计分析。结果 孤独症患者全脑功能网络拓扑属性显著改变,聚类性显著降低,最短路径长度缩短;同时整体效率显著升高,局部效率/模块化和中介中心性降低。结论 与正常人相比,孤独症患者全脑网络功能整合能力升高,而功能分离能力所下降;全脑网络异常有助于理解孤独症患者脑功能障碍。     

Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis

Purpose

Diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging-based biomarkers that reflect the microstructural characteristics of neuronal pathways in the brain. While tractography-based approaches typically analyze specific structures of interest, a graph-based large-scale network analysis of the connectome can yield comprehensive measures of larger-scale architectural patterns in the brain. Commonly applied global network indices, however, do not provide any specificity with respect to functional areas or anatomical structures. Aim of this work was to assess the concept of network centrality as a tool to perform locally specific analysis without disregarding the global network architecture and compare it to other popular network indices.

Methods

We create connectome networks from fiber tractographies and parcellations of the human brain and compute global network indices as well as local indices for Wernicke’s Area, Broca’s Area and the Motor Cortex. Our approach was evaluated on 18 children suffering from ASD and 18 typically developed controls using magnetic resonance imaging-based cortical parcellations in combination with diffusion tensor imaging tractography.

Results

We show that the network centrality of Wernicke’s area is significantly (p  $<$  0.001) reduced in ASD, while the motor cortex, which was used as a control region, did not show significant alterations. This could reflect the reduced capacity for comprehension of language in ASD.

Conclusions

The betweenness centrality could potentially be an important metric in the development of future diagnostic tools in the clinical context of ASD diagnosis. Our results further demonstrate the applicability of large-scale network analysis tools in the domain of region-specific analysis with a potential application in many different psychological disorders.     

Identification of information flow-modulating drug targets: a novel bridging paradigm for drug discovery

Our objective in this study was to identify novel metrics for efficient identification of drug targets using biological network topology data. We developed a novel paradigm and metric, namely, bridging centrality, capable of identifying nodes critically involved in connecting or bridging modular subregions of a network. The topological and biological characteristics of bridging nodes were delineated in a diverse group of published yeast networks and in three human networks: those involved in cardiac arrest, C21-steroid hormone biosynthesis, and steroid biosynthesis. The bridging centrality metric was highly selective for bridging nodes. Bridging nodes differed distinctively from nodes with high degree and betweenness centrality. Bridging nodes had lower lethality, and their gene expression was consistent with independent regulation. Analysis of biological correlates indicated that bridging nodes are promising drug targets from the standpoints of efficacy and side effects. The bridging centrality method is a promising computational systems biology tool to aid target identification in drug discovery.     

静息态fMRI评价阿尔茨海默患者大脑功能网络效率变化

目的 采用图论方法探讨阿尔茨海默病(AD)对大脑功能网络效率的影响.方法 对33例AD患者(AD组)和20名健康老年志愿者,以简易智能状态量表(MMSE)和Mattis痴呆评定量表(DRS)评估认知水平;采集静息态BOLD-fMRI的数据,应用解剖学自动标记模板把大脑分为90个区域,提取每个区域内所有体素的BOLD信号平均值,计算每两个区域间的相关系数,构建功能网络.利用图论方法检验两组人群脑功能网络的小世界属性,计算网络的效率,评价AD组脑网络效率的特征性变化.结果 AD组MMSE和DRS平均分值均显著低于正常对照组(P<0.01).以连接矩阵的稀疏度(Sparsity)为阈值,在0.1~0.4范围内,两组受试者的全局效率低于相应的随机网络,高于规则网络;局部效率高于随机网络,低于规则网络,都具有小世界属性.与正常对照组比较,AD组全局效率显著降低,局部效率显著增高(P<0.05).结论 AD患者的大脑功能网络仍具有小世界属性,但其全局效率显著降低,局部效率显著增高,提示AD患者脑功能网络的信息传递能力和效率受损.     

青年和老年人群认知联系网络的局部结构特征分析*

目的：从词汇联想网络角度比较青年和老年人认知联系结构的局部特征。方法：健康青年和老年人各50名。刺激词为Kent-Rosanoff中文对等词。反应词之间通过共享刺激词而构建“反应词-反应词网络”（RRN）：青年组RR网络（YRRN）和老年组RR网络（ORRN）。采用Pajek1.14和Matlab7.0进行网络分析和可视化。局部变量包括点度中心性、中间中心性、接近中心性、首层云集系数（CC1）和次层云集系数（CC2）。结果：在不同年龄组之间,各局部变量相关系数均<0.6。各组点度中心性与中间中心性、接近中心性和CC2线性正相关,而与CC1之间缺乏明显线性关系。点度中心性或CC1数值排位前十位的词汇在两组间均重复很少。对YRRN和ORRN的交集和差集的比较显示在各年龄组均存在各自特有的重要词汇及其联系。结论：同一个词汇在不同年龄人群认知联系结构中的重要性不同;不同的局部变量可以反映词汇间联系的特征;点度和首层云集系数可以为认知康复研究提供方法学依据。     

Graph properties of synchronized cortical networks during visual working memory maintenance

Oscillatory synchronization facilitates communication in neuronal networks and is intimately associated with human cognition. Neuronal activity in the human brain can be non-invasively imaged with magneto- (MEG) and electroencephalography (EEG), but the large-scale structure of synchronized cortical networks supporting cognitive processing has remained uncharacterized. We combined simultaneous MEG and EEG (MEEG) recordings with minimum-norm-estimate-based inverse modeling to investigate the structure of oscillatory phase synchronized networks that were active during visual working memory (VWM) maintenance. Inter-areal phase-synchrony was quantified as a function of time and frequency by single-trial phase-difference estimates of cortical patches covering the entire cortical surfaces. The resulting networks were characterized with a number of network metrics that were then compared between delta/theta- (3–6 Hz), alpha- (7–13 Hz), beta- (16–25 Hz), and gamma- (30–80 Hz) frequency bands. We found several salient differences between frequency bands. Alpha- and beta-band networks were more clustered and small-world like but had smaller global efficiency than the networks in the delta/theta and gamma bands. Alpha- and beta-band networks also had truncated-power-law degree distributions and high k-core numbers. The data converge on showing that during the VWM-retention period, human cortical alpha- and beta-band networks have a memory-load dependent, scale-free small-world structure with densely connected core-like structures. These data further show that synchronized dynamic networks underlying a specific cognitive state can exhibit distinct frequency-dependent network structures that could support distinct functional roles.     

早产儿与足月儿脑结构网络属性与模块化特征对比研究

目的探讨早产与足月儿脑结构网络属性与模块化特点,并进行对比分析。材料与方法对20例早产儿(胎龄,30~37孕周)与22例足月儿(胎龄,37~42孕周)进行3D磁共振T1加权成像扫描,利用新生儿脑解剖学图谱将整个大脑划分为122个区域,对其中64个灰质脑区进行脑容积二值网络构建,计算并比较网络属性与模块化特征。结果两组脑网络属性均具有小世界属性;相较足月儿,早产儿集群系数与局部效率显著降低(P0.05),且模块化结构不规则。结论早产儿与足月儿脑结构网络具有小世界属性,但早产儿脑局部信息传递效率低,模块化结构分散,提示其脑区间信息整合能力发育迟缓或异常。     

Changes in the brain intrinsic organization in both on-task state and post-task resting state

The dynamic and robust characteristics of intrinsic functional connectivity of coherent spontaneous activity are critical for the brain functional stability and flexibility. Studies have demonstrated modulation of intrinsic connectivity within local spatial patterns during or after task performance, such as the default mode network (DMN) and task-specific networks. Moreover, recent studies have compared the global spatial pattern in different tasks or over time. However, it is still unclear how the large-scale intrinsic connectivity varies during and after a task. To better understand this issue, we conducted a functional MRI experiment over three sequential periods: an active semantic-matching task period and two rest periods, before and after the task respectively (namely, on-task state and pre-/post-task resting states), to detect task-driven effect on the dynamic large-scale intrinsic organization in both on-task state and post-task resting state. Three hierarchical levels were investigated, including (a) the whole brain small-world topology, (b) the whole pairwise functional connectivity patterns both within the DMN and between the DMN and other regions (i.e., the global/full DMN topography), and (c) the DMN nodal graph properties. The major findings are: (1) The large-scale small-world configuration of brain functional organization is robust, regardless of the behavioral state changing, while it varies adaptively with significantly higher local efficiency and lower global efficiency during the on-task state (P<0.05, Monte-Carlo corrected); (2) The DMN may be essentially engaged during both task and post-task processes with adaptively varied spatial patterns and nodal graph properties. The present study provides further insights into the robustness and plasticity of the brain intrinsic organization over states, which may be the basis of memory and learning in the brain.     

Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [<Superscript>18</Superscript>F]FDG-PET of Rats

Purpose

Recent researches have demonstrated the value of using 2-deoxy-2-[¹⁸F]fluoro-d-glucose ([¹⁸F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom.

Procedures

For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups.

Results

We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism.

Conclusion

Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [¹⁸F]FDG-PET images and facilitates future study on human subjects.

     

Characterizing brain anatomical connections using diffusion weighted MRI and graph theory

A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers. This probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function, obtained from anatomical MRI and DW-MRI, respectively. A new tractography algorithm for finding white matter routes is also introduced. This algorithm solves the most probable path problem between any two nodes, leading to the assessment of probabilistic brain anatomical connection maps. In a second step, for assessing anatomical connectivity between K gray matter structures, the previous graph is redefined as a K+1 partite graph by partitioning the initial nodes set in K non-overlapped gray matter subsets and one subset clustering the remaining nodes. Three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets: Anatomical Connection Strength (ACS), Anatomical Connection Density (ACD) and Anatomical Connection Probability (ACP). This methodology was applied to both artificial and actual human data. Results show that nervous fiber pathways between some regions of interest were reconstructed correctly. Additionally, mean connectivity maps of ACS, ACD and ACP between 71 gray matter structures for five healthy subjects are presented.     

Altered structural connectivity in neonates at genetic risk for schizophrenia: a combined study using morphological and white matter networks

Recently, an increasing body of evidence suggests that developmental abnormalities related to schizophrenia may occur as early as the neonatal stage. Impairments of brain gray matter and wiring problems of axonal fibers are commonly suspected to be responsible for the disconnection hypothesis in schizophrenia adults, but significantly less is known in neonates. In this study, we investigated 26 neonates who were at genetic risk for schizophrenia and 26 demographically matched healthy neonates using both morphological and white matter networks to examine possible brain connectivity abnormalities. The results showed that both populations exhibited small-world network topology. Morphological network analysis indicated that the brain structural associations of the high-risk neonates tended to have globally lower efficiency, longer connection distance, and less number of hub nodes and edges with relatively higher betweenness. Subgroup analysis showed that male neonates were significantly disease-affected, while the female neonates were not. White matter network analysis, however, showed that the fiber networks were globally unaffected, although several subcortical-cortical connections had significantly less number of fibers in high-risk neonates. This study provides new lines of evidence in support of the disconnection hypothesis, reinforcing the notion that the genetic risk of schizophrenia induces alterations in both gray matter structural associations and white matter connectivity.     

Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain

The brain is a complex dynamic system of functionally connected regions. Graph theory has been successfully used to describe the organization of such dynamic systems. Recent resting-state fMRI studies have suggested that inter-regional functional connectivity shows a small-world topology, indicating an organization of the brain in highly clustered sub-networks, combined with a high level of global connectivity. In addition, a few studies have investigated a possible scale-free topology of the human brain, but the results of these studies have been inconclusive. These studies have mainly focused on inter-regional connectivity, representing the brain as a network of brain regions, requiring an arbitrary definition of such regions. However, using a voxel-wise approach allows for the model-free examination of both inter-regional as well as intra-regional connectivity and might reveal new information on network organization. Especially, a voxel-based study could give information about a possible scale-free organization of functional connectivity in the human brain. Resting-state 3 Tesla fMRI recordings of 28 healthy subjects were acquired and individual connectivity graphs were formed out of all cortical and sub-cortical voxels with connections reflecting inter-voxel functional connectivity. Graph characteristics from these connectivity networks were computed. The clustering-coefficient of these networks turned out to be much higher than the clustering-coefficient of comparable random graphs, together with a short average path length, indicating a small-world organization. Furthermore, the connectivity distribution of the number of inter-voxel connections followed a power-law scaling with an exponent close to 2, suggesting a scale-free network topology. Our findings suggest a combined small-world and scale-free organization of the functionally connected human brain. The results are interpreted as evidence for a highly efficient organization of the functionally connected brain, in which voxels are mostly connected with their direct neighbors forming clustered sub-networks, which are held together by a small number of highly connected hub-voxels that ensure a high level of overall connectivity.     

Statistical parametric network analysis of functional connectivity dynamics during a working memory task

Network analysis has become a tool of choice for the study of functional and structural Magnetic Resonance Imaging (MRI) data. Little research, however, has investigated connectivity dynamics in relation to varying cognitive load. In fMRI, correlations among slow (<0.1 Hz) fluctuations of blood oxygen level dependent (BOLD) signal can be used to construct functional connectivity networks. Using an anatomical parcellation scheme, we produced undirected weighted graphs linking 90 regions of the brain representing major cortical gyri and subcortical nuclei, in a population of healthy adults (n=43). Topological changes in these networks were investigated under different conditions of a classical working memory task - the N-back paradigm. A mass-univariate approach was adopted to construct statistical parametric networks (SPNs) that reflect significant modifications in functional connectivity between N-back conditions. Our proposed method allowed the extraction of 'lost' and 'gained' functional networks, providing concise graphical summaries of whole-brain network topological changes. Robust estimates of functional networks are obtained by pooling information about edges and vertices over subjects. Graph thresholding is therefore here supplanted by inference. The analysis proceeds by firstly considering changes in weighted cost (i.e. mean between-region correlation) over the different N-back conditions and secondly comparing small-world topological measures integrated over network cost, thereby controlling for differences in mean correlation between conditions. The results are threefold: (i) functional networks in the four conditions were all found to satisfy the small-world property and cost-integrated global and local efficiency levels were approximately preserved across the different experimental conditions; (ii) weighted cost considerably decreased as working memory load increased; and (iii) subject-specific weighted costs significantly predicted behavioral performances on the N-back task (Wald F=13.39,df(1)=1,df(2)=83,p<0.001), and therefore conferred predictive validity to functional connectivity strength, as measured by weighted cost. The results were found to be highly sensitive to the frequency band used for the computation of the between-region correlations, with the relationship between weighted cost and behavioral performance being most salient at very low frequencies (0.01-0.03 Hz). These findings are discussed in relation to the integration/specialization functional dichotomy. The pruning of functional networks under increasing cognitive load may permit greater modular specialization, thereby enhancing performance.     

局灶性脑损害患者视觉注意环路的小世界网络属性分析

摘要 目的：探讨局灶性脑损害患者视觉注意网络的连通性及其小世界特征。 方法：对2例局灶性后顶叶损害、2例背外侧前额叶损害、2例颞叶损害（其中1例为手术前后）和1例锥体束损害患者进行静息态fMRI检测,然后进行功能网络建立和小世界属性分析。 结果：所有局灶性脑损害患者的脑功能网络在给定阈值范围（0.05—0.5）内都具有小世界属性。局灶性后顶叶、背外侧前额叶、颞叶和锥体束损害患者之间,Gamma系数差异（P<0.05）和Sigma系数差异（P<0.01）均具有显著性意义。 结论：小世界网络分析为视觉注意网络的连通性研究提供了一个非常有价值的方法。我们推测,小世界网络检测方法应该可以作为局灶性脑损害神经功能损害的影像学生物标记。     

Weight-conserving characterization of complex functional brain networks

Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.     

Enhanced functional networks in absolute pitch

Functional networks in the human brain give rise to complex cognitive and perceptual abilities. While the decrease of functional connectivity is linked to neurological and psychiatric disorders, less is known about the consequences of increased functional connectivity. One population that has exceptionally enhanced perceptual abilities is people with absolute pitch (AP) - an ability to categorize tones into pitch classes without reference. AP has been linked to exceptional talent as well as to psychiatric and neurological conditions. Here we show that AP possessors have increased functional activation during music listening, as well as increased degrees, clustering, and local efficiency of functional correlations, with the difference being highest around the left superior temporal gyrus. Our results provide the first evidence that increased functional connectivity in a small-world brain network is related to exceptional perceptual abilities in a healthy population.