White Matter Network Research Articles

Stroke disconnectome decodes reading networks

Cognitive functional neuroimaging has been around for over 30 years and has shed light on the brain areas relevant for reading. However, new methodological developments enable mapping the interaction between functional imaging and the underlying white matter networks. In this study, we used such a novel method, called the disconnectome, to decode the reading circuitry in the brain. We used the resulting disconnection patterns to predict a typical lesion that would lead to reading deficits after brain damage. Our results suggest that white matter connections critical for reading include fronto-parietal U-shaped fibres and the vertical occipital fasciculus (VOF). The lesion most predictive of a reading deficit would impinge on the left temporal, occipital, and inferior parietal gyri. This novel framework can systematically be applied to bridge the gap between the neuropathology of language and cognitive neuroscience.

High cortical iron is associated with the disruption of white matter tracts supporting cognitive function in healthy older adults.

Aging is associated with brain iron accumulation, which has been linked to cognitive decline. However, how brain iron affects the structure and function of cognitive brain networks remains unclear. Here, we explored the possibility that iron load in gray matter is associated with disruption of white matter (WM) microstructure within a network supporting cognitive function, in a cohort of 95 cognitively normal older adults (age range: 60-86). Functional magnetic resonance imaging was used to localize a set of brain regions involved in working memory and diffusion tensor imaging based probabilistic tractography was used to identify a network of WM tracts connecting the functionally defined regions. Brain iron concentration within these regions was evaluated using quantitative susceptibility mapping and microstructural properties were assessed within the identified tracts using neurite orientation dispersion and density imaging. Results indicated that high brain iron concentration was associated with low neurite density (ND) within the task-relevant WM network. Further, regional associations were observed such that brain iron in cortical regions was linked with lower ND in neighboring but not distant WM tracts. Our results provide novel evidence suggesting that age-related increases in brain iron concentration are associated with the disruption of WM tracts supporting cognitive function in normal aging.

P.63 Comparison of brain volume reduction in boys with Duchenne muscular dystrophy treated with different corticosteroid regimes

Recent studies show lower gray matter volumes and altered white matter networks in boys with Duchenne Muscular Dystrophy (DMD) compared with healthy controls, suggesting a lack of dystrophin impacts brain structures. However, the majority of boys with DMD are chronically treated with corticosteroids (CS), which are known to cross the blood-brain barrier and cause brain atrophy. Unraveling the impact of CS treatment on the brain of boys with DMD is key to understand brain involvement in this disease. This study compared two groups of DMD boys treated with a different CS regimen with 2 healthy control groups. 3D T1-weighted images were obtained on a 3T Philips system at two different sites. Scans from 20 DMD patients (13.0±3.1y) on a 10 days on/10 days off prednisolone schedule and 20 age-matched controls (13.2±1.8y) were obtained at the LUMC Leiden, the Netherlands. Additionally scans of 20 age-matched DMD patients (12.8±3.0y) on a daily deflazacort regime and 20 age-matched controls (13.1±3.2y) were acquired at the UZ Leuven, Belgium. All 80 scans were processed using FSL software to calculate intracranial (ICV), total brain (TBV), grey matter (GMV), white matter (WMV) and CSF volumes. In addition, volumes of the hippocampus, amygdala, thalamus, nucleus accumbens, caudate nucleus, globus pallidus and putamen were calculated. To control for intracranial volume differences, relative volumes were compared between controls and DMD per center using univariate analyses of variances in SPSS and considered significant at p<0.05. Both DMD groups showed a significant lower relative GMV, and higher relative CSF volume compared to their control groups. Interestingly, a significantly lower relative WMV (33.81% vs 35.45%, p=.002) and relative volume of the globus pallidus (0.25% vs 0.22%, p<.001) was found in the deflazacort group compared with their control group, but not in the prednisolone group. This study suggest that different CS regimes lead to different regional brain volume reductions in patients with DMD.

An accelerated degeneration of white matter microstructure and networks in the nondemented old-old.

The nondemented old-old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young-old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old-old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old-old compared with young-old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old-old but not in the young-old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.

Brain default mode network mediates the association between negative perfectionism and exercise dependence.

Perfectionism is correlated with the occurrence of exercise dependence. We aim to reveal the role of functional connectivity (FC) between gray matter (GM) and white matter (WM) networks in the association between perfectionism and exercise dependence. In this cross-sectional study, one hundred ten participants with exercise dependence underwent behavioral evaluation and resting-state functional magnetic resonance imaging. Perfectionism and exercise dependence were quantified using the Frost Multidimensional Perfectionism Scale (FMPS) and Exercise Dependence Scale (EDS). We used a K-means clustering algorithm to identify functional GM and WM networks and obtained the FCs of the GM-GM, GM-WM, and WM-WM networks. Partial correlation and mediation analyses were performed to explore the relationships among FCs, FMPS, and EDS. We identified ten stable GM networks and nine WM networks. Of these, FCs existed between the corona radiata network (WM1) and default mode network (DMN, GM8), WM1 network and WM DMN (WM4), WM1 network and midbrain WM network (WM7), and WM4 network and inferior longitudinal fasciculus network (WM9). The WM1-GM8 and WM1-WM4 FCs were positively correlated with the EDS and negative FMPS. The mediating effects of the WM1-GM8 and WM1-WM4 FCs were established in the association between the negative dimensional FMPS and EDS. The WM1 network anatomically linked the subregions within the GM8 and WM4 networks, and WM1-GM8 and WM1-WM4 FCs mediated the association between negative dimensional FMPS and EDS. These findings indicated that DMN function might be involved in the increased risks of exercise dependence promoted by negative perfectionism.

Regional Disruption of White Matter Integrity and Network Connectivity Are Related to Cognition.

Cognitive impairment is common in the elderly population. Exploring patterns of white matter damage at the microstructural level would give important indications for the underlying mechanisms. To investigate the spatial patterns of white matter microstructure and structural network alternations in relation to different cognition domainsMethods:Participants from the community-based Shunyi Study were included to investigate the association between white matter measurements and cognition cross-sectionally, via both global and local analysis. Cognitive functions were assessed using digit span, trail making test (TMT)-A/B, Fuld object Memory, and 12-Word Philadelphia Verbal Learning Test (PVLT). White matter measurements including fractional anisotropy (FA), mean diffusivity (MD), and structural network parameters were calculated based on diffusion tensor imaging. Of the 943 participants included, the mean (SD) age was 55.8 (9.1) years, and the mean (SD) education level was 6.7 (3.2) years. We found the whole set of cognitive measurements was related to diffused white matter microstructural integrity damage and lower global efficiency. Poor executive functions (TMTA/B complete time) were related to lower FA and higher MD predominantly on the anterior white matter skeleton, while verbal memory loss (PVLT test scores) was related to sub-network dysconnectivity in the midline and the right temporal lobe. The anterior brain is dominantly involved in executive dysfunction, while midline and right temporal brain disconnection are more prominent in verbal memory loss. Global and regional disruption of white matter integrity and network connectivity is the anatomical basis of the cognitive impairment in the aging population.

Large-scale dysfunctional white matter and grey matter networks in patients with social anxiety disorder

SummaryDysfunction of large-scale brain networks has been implicated in social anxiety disorder (SAD); most work has focused on grey matter (GM) functional connectivity (FC) abnormalities, whereas white matter (WM) FC alterations remain unclear. Here, using a K-means clustering algorithm, we obtained 8 GM and 10 WM functional networks from a cohort dataset (48 SAD patients and 48 healthy controls). By calculating and comparing FC matrices between SAD group and healthy controls, we demonstrated disrupted connections between the limbic and dorsal prefrontal, lateral temporal, and sensorimotor networks, and between the visual and sensorimotor networks. Furthermore, there were negative correlations between HAMD scores and limbic-dorsal prefrontal and limbic-sensorimotor networks, and between illness duration and sensorimotor-visual networks. These findings reflect the critical role of limbic network, with its extensive connections to other networks, and the neurobiology of disordered cognition processing and emotional regulation in SAD.

Distinguishing major depressive disorder from bipolar disorder in remission: A brain structural network analysis

BackgroundIt is challenging to differentiate major depressive disorder (MDD) from bipolar disorder (BD) in depression and remission. To exclude the potential influence of depressive episodes, we compared the white matter (WM) network between MDD and BD patients in remission to find disease-specific alterations in MDD and BD, and then distinguish these two affective disorders. MethodsWe recruited 33 patients with remitted MDD (rMDD), 54 patients with remitted BD (rBD), and 60 healthy controls (HCs). Diffusion tensor imaging and high-resolution 3D T1-weighted image were acquired. Global and nodal topological parameters were used to depict the alterations of the whole-brain WM network. ResultsWe found that rMDD displayed increased global network efficiency (Eglob) and local network efficiency (Eloc) compared with HCs, whereas we found no significance between rBD and HCs. Compared with rBD and HCs, patients in the rMDD group showed increased nodal degree and nodal efficiency, and decreased nodal shortest path length in the four cerebral regions, including the right calcarine fissure (CAL.R), right cuneus (CUN.R), left lingual gyrus (LING.L), and left middle occipital gyrus (MOG.L). We did not find any rBD specific changes of nodal topological metrics. LimitationsThe main limitation is the possible effects of medication and BD subtypes on the results. ConclusionsOur findings indicate that rMDD exhibited elevated global properties compared with HCs group, and increased nodal properties in the CAL.R, CUN.R, LING.L, and MOG.L specifically compared with rBD and HCs, which may underlie the distinction of the two affective disorders in remission.

Brain morphological network and its applications in human brain development

<p indent="0mm">The human brain is an extremely complex system, where trillions of neurons are connected through synapses, forming a massive micro-scale anatomical network which is considered to be the physiological basis for information processing and cognition. How to depict the connection pattern of the brain has always been a hot topic within the neuroscience community. With the development of non-invasive neuroimaging technology, investigations of the brain network have entered a new era. Brain networks based on magnetic resonance imaging (MRI) provide new insights into the complex anatomical organization and functional mechanism of the human brain. Brain networks can be divided into morphological networks, white matter networks, and functional networks. Brain morphological networks usually refer to the morphological connection networks generated from structural MRI, and have received wide attention because of its advantages of easy image acquisition, stable image quality, and simplicity in analysis. Brain morphological networks can be further divided into across-subjects covariance networks and individual-level networks. Across-subjects covariance networks are constructed by calculating the statistical correlation of morphological features between brain regions from a group of participants, which only reflect the group characteristics of participants but ignore the individual variations. Individual-level brain morphological networks are built using morphological information from the individual brain to describe the cooperative changes between brain regions, which can preserve the individual difference. There are two types of methods to build individual morphological networks, namely, single feature based and multiple features based, which will be described in detail in this review. Graph theory model in combination with brain morphological network enables us to characterize the developmental trajectories of the topology of cortical morphology, from prenatal, early postnatal, childhood to adolescence. Based on the recent studies of brain morphological networks on brain development, we found that brain morphological networks are still in their primitive state during the early second trimester, which become more integrated towards birth and already exhibit the characteristic small-world topology and nonrandom modular organization at birth. Subsequently, brain morphological networks become more separated from birth to early childhood and gradually integrate from late childhood to adolescence. In addition, many studies demonstrated distinct developmental trajectories in different cortical divisions. Specifically, the primary networks are the first to reach maturity, while the higher-order networks continue to develop for a long time. These findings provide important evidence for understanding the reorganization of the cerebral cortex and the emergence and development of brain cognitive function. Although brain morphological networks have undergone ten years of development, problems and challenges remain in this field. Similar to other forms of connectivity analysis, brain morphological networks analysis struggles with the issues of edge and node definition, and further methodological development is needed in the fields of graph theory and statistical comparison between networks. More importantly, despite some studies that attempt to relate brain morphological networks to tractography-based brain networks, functional networks, and gene-specific expression, we are still awaiting a clear interpretation of the biological significance of brain morphological networks. In addition, how to acquire high-quality MRI data for perinatal brains is a major challenge to complete the development trajectories of developing brain morphological networks.

A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis.

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p <10−4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications.

The organization of individually mapped structural and functional semantic networks in aging adults.

Language function in the brain, once thought to be highly localized, is now appreciated as relying on a connected but distributed network. The semantic system is of particular interest in the language domain because of its hypothesized integration of information across multiple cortical regions. Previous work in healthy individuals has focused on group-level functional connectivity (FC) analyses of the semantic system, which may obscure interindividual differences driving variance in performance. These studies also overlook the contributions of white matter networks to semantic function. Here, we identified semantic network nodes at the individual level with a semantic decision fMRI task in 53 typically aging adults, characterized network organization using structural connectivity (SC), and quantified the segregation and integration of the network using FC. Hub regions were identified in left inferior frontal gyrus. The individualized semantic network was composed of three interacting modules: (1) default-mode module characterized by bilateral medial prefrontal and posterior cingulate regions and also including right-hemisphere homotopes of language regions; (2) left frontal module extending dorsally from inferior frontal gyrus to pre-motor area; and (3) left temporoparietal module extending from temporal pole to inferior parietal lobule. FC within Module3 and integration of the entire network related to a semantic verbal fluency task, but not a matched phonological task. These results support and extend the tri-network semantic model (Xu in Front Psychol 8: 1538 1538, 2017) and the controlled semantic cognition model (Chiou in Cortex 103: 100 116, 2018) of semantic function.

Increased functional connectivity of white-matter in myotonic dystrophy type 1.

BackgroundMyotonic dystrophy type 1 (DM1) is the most common and dominant inherited neuromuscular dystrophy disease in adults, involving multiple organs, including the brain. Although structural measurements showed that DM1 is predominantly associated with white-matter damage, they failed to reveal the dysfunction of the white-matter. Recent studies have demonstrated that the functional activity of white-matter is of great significance and has given us insights into revealing the mechanisms of brain disorders.Materials and methodsUsing resting-state fMRI data, we adopted a clustering analysis to identify the white-matter functional networks and calculated functional connectivity between these networks in 16 DM1 patients and 18 healthy controls (HCs). A two-sample t-test was conducted between the two groups. Partial correlation analyzes were performed between the altered white-matter FC and clinical MMSE or HAMD scores.ResultsWe identified 13 white-matter functional networks by clustering analysis. These white-matter functional networks can be divided into a three-layer network (superficial, middle, and deep) according to their spatial distribution. Compared to HCs, DM1 patients showed increased FC within intra-layer white-matter and inter-layer white-matter networks. For intra-layer networks, the increased FC was mainly located in the inferior longitudinal fasciculus, prefrontal cortex, and corpus callosum networks. For inter-layer networks, the increased FC of DM1 patients is mainly located in the superior corona radiata and deep networks.ConclusionResults demonstrated the abnormalities of white-matter functional connectivity in DM1 located in both intra-layer and inter-layer white-matter networks and suggested that the pathophysiology mechanism of DM1 may be related to the white-matter functional dysconnectivity. Furthermore, it may facilitate the treatment development of DM1.

Disrupted Topological Organization of White Matter Network in Angelman Syndrome.

Angelman syndrome (AS) is a genetic disorder that affects neurodevelopment. The investigation of changes in the brain white matter network, which would contribute to a better understanding of the pathogenesis of AS brain, was lacking. To investigate both local and global alterations of white matter in patients with AS. Prospective. A total of 29 AS patients (6.6 ± 1.4 years, 15 [52%] females) and 19 age-matched healthy controls (HC) (7.0 ± 1.5 years, 10 [53%] females). A 3-T, three-dimensional (3D) T1-weighted imaging by using gradient-echo-based sequence, single shell diffusion tensor imaging by using spin-echo-based echo-planar imaging. Network metrics including global efficiency (Eg ), local efficiency (Eloc ), small world coefficient (Swc), rich-club coefficient (Φ), and nodal degree (ND) were estimated from diffusion MR (dMR) data. Connections among highly connected (hub) regions and less connected (peripheral) regions were also assessed. Correlation between the topological parameters and age for each group was also calculated to assess the development of the brain. Linear regression model, permutation test. P values estimated from the regression model for each brain region were adjusted by false discovery rate (FDR) correction. AS patients showed significantly lower Eg and higher swc compared to HC. Φn significantly increased at higher k-levels in AS patients. In addition, the connections among hub regions and peripheral regions were significantly interrupted in AS patients. The AS brain showed diminished connectivity, reflected by reduced network efficiency compared to HC. Compared to densely connected regions, less connected regions were more vulnerable in AS. 2 TECHNICAL EFFICACY: Stage 3.

Disrupted Brain Structural Network Connection in de novo Parkinson's Disease With Rapid Eye Movement Sleep Behavior Disorder.

ObjectiveTo explore alterations in white matter network topology in de novo Parkinson's disease (PD) patients with rapid eye movement sleep behavior disorder (RBD).Materials and MethodsThis study included 171 de novo PD patients and 73 healthy controls (HC) recruited from the Parkinson's Progression Markers Initiative (PPMI) database. The patients were divided into two groups, PD with probable RBD (PD-pRBD, n = 74) and PD without probable RBD (PD-npRBD, N = 97), according to the RBD screening questionnaire (RBDSQ). Individual structural network of brain was constructed based on deterministic fiber tracking and analyses were performed using graph theory. Differences in global and nodal topological properties were analyzed among the three groups. After that, post hoc analyses were performed to explore further differences. Finally, correlations between significant different properties and RBDSQ scores were analyzed in PD-pRBD group.ResultsAll three groups presented small-world organization. PD-pRBD patients exhibited diminished global efficiency and increased shortest path length compared with PD-npRBD patients and HCs. In nodal property analyses, compared with HCs, the brain regions of the PD-pRBD group with changed nodal efficiency (Ne) were widely distributed mainly in neocortical and paralimbic regions. While compared with PD-npRBD group, only increased Ne in right insula, left middle frontal gyrus, and decreased Ne in left temporal pole were discovered. In addition, significant correlations between Ne in related brain regions and RDBSQ scores were detected in PD-pRBD patients.ConclusionsPD-pRBD patients showed disrupted topological organization of white matter in the whole brain. The altered Ne of right insula, left temporal pole and left middle frontal gyrus may play a key role in the pathogenesis of PD-RBD.

Extensive Connections of the Canine Olfactory Pathway Revealed by Tractography and Dissection.

The olfactory sense of the domestic dog is widely recognized as being highly sensitive with a diverse function; however, little is known about the structure of its olfactory system. This study examined a cohort of mixed-sex mesaticephalic canines and used diffusion tensor imaging (DTI), an MRI technique, to map connections from the olfactory bulb to other cortical regions of the brain. The results were validated using the Klingler dissection method. An extensive pathway composed of five white matter tracts connecting to the occipital lobe, cortical spinal tract, limbic system, piriform lobe, and entorhinal pathway was identified. This is the first documentation of a direct connection between the olfactory bulb and occipital lobe in any species and is a step toward further understanding how the dog integrates olfactory stimuli into their cognitive function.SIGNIFICANCE STATEMENT The highly sensitive olfactory system of the domestic dog is largely unexplored. We applied diffusion tractography and dissection techniques to evaluate the white matter connections associated with the olfactory system in a large cohort of dogs. We discovered an extensive white matter network extending from the olfactory bulb to form novel connections directly to other cortices of the brain. This is the first documentation of these novel olfactory connections and provides new insight into how dogs integrate olfactory stimuli in their cognitive functioning.

Neural dissociation of visual attention span and phonological deficits in developmental dyslexia: A hub-based white matter network analysis.

It has been suggested that developmental dyslexia may have two dissociable causes-a phonological deficit and a visual attention span (VAS) deficit. Yet, neural evidence for such a dissociation is still lacking. This study adopted a data-driven approach to white matter network analysis to explore hubs and hub-related networks corresponding to VAS and phonological accuracy in a group of French dyslexic children aged from 9 to 14 years. A double dissociation in brain-behavior relations was observed. Structural connectivity of the occipital-parietal network surrounding the left superior occipital gyrus hub accounted for individual differences in dyslexic children's VAS, but not in phonological processing accuracy. In contrast, structural connectivity of two networks: the temporal-parietal-occipital network surrounding the left middle temporal gyrus hub and the frontal network surrounding the left medial orbital superior frontal gyrus hub, accounted for individual differences in dyslexic children's phonological processing accuracy, but not in VAS. Our findings provide evidence in favor of distinct neural circuits corresponding to VAS and phonological deficits in developmental dyslexia. The study points to connectivity-constrained white matter subnetwork dysfunction as a key principle for understanding individual differences of cognitive deficits in developmental dyslexia.

Topologic Efficiency Abnormalities of the Connectome in Asymptomatic Patients with Leukoaraiosis.

Leukoaraiosis (LA) is commonly found in aging healthy people but its pathophysiological mechanism is not entirely known. Furthermore, there is still a lack of effective pathological biomarkers that can be used to identify the early stage of LA. Our aim was to investigate the white matter structural network in asymptomatic patients with the early stage of LA. Tractography data of 35 asymptomatic patients and 20 matched healthy controls (HCs) based on diffusion kurtosis imaging (DKI) were analysed by using graph theory approaches and tract-based spatial statistics (TBSS). Diffusion parameters measured within the ALAs and HCs were compared. Decreased clustering coefficient and local efficiency values of the overall topological white matter network were observed in the ALAs compared with those of the HCs. Participants in the asymptomatic group also had lower nodal efficiency in the left triangular part of the inferior frontal gyrus, left parahippocampal gyrus, right calcarine fissure and surrounding cortex, right temporal pole of the superior temporal gyrus and left middle temporal gyrus compared to the ALAs. Moreover, similar hub distributions were found within participants in the two groups. In this study, our data demonstrated a topologic efficiency abnormalities of the structural network in asymptomatic patients with leukoaraiosis. The structural connectome provides potential connectome-based measures that may be helpful for detecting leukoaraiosis before clinical symptoms evolve.

Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network.

BackgroundDetection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.MethodsOur study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.Results(1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.ConclusionFeature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

Negative symptoms are associated with modularity and thalamic connectivity in schizophrenia.

Negative symptoms, including avolition, anhedonia, asociality, blunted affect and alogia are associated with poor long-term outcome and functioning. However, treatment options for negative symptoms are limited and neurobiological mechanisms underlying negative symptoms in schizophrenia are still poorly understood. Diffusion-weighted magnetic resonance imaging scans were acquired from 64 patients diagnosed with schizophrenia and 35 controls. Global and regional network properties and rich club organization were investigated using graph analytical methods. We found that the schizophrenia group had higher modularity, clustering coefficient and characteristic path length, and lower rich connections compared to controls, suggesting highly connected nodes within modules but less integrated with nodes in other modules in schizophrenia. We also found a lower nodal degree in the left thalamus and left putamen in schizophrenia relative to the control group. Importantly, higher modularity was associated with greater negative symptoms but not with cognitive deficits in patients diagnosed with schizophrenia suggesting an alteration in modularity might be specific to overall negative symptoms. The nodal degree of the left thalamus was associated with both negative and cognitive symptoms. Our findings are important for improving our understanding of abnormal white-matter network topology underlying negative symptoms in schizophrenia.

Alterations in white matter network dynamics in patients with schizophrenia and bipolar disorder.

Emerging evidence suggests white matter network abnormalities in patients with schizophrenia (SZ) and bipolar disorder (BD), but the alterations in dynamics of the white matter network in patients with SZ and BD are largely unknown. The white matter network of patients with SZ (n = 45) and BD (n = 47) and that of healthy controls (HC, n = 105) were constructed. We used dynamics network control theory to quantify the dynamics metrics of the network, including controllability and synchronizability, to measure the ability to transfer between different states. Experiments show that the patients with SZ and BD showed decreasing modal controllability and synchronizability and increasing average controllability. The correlations between the average controllability and synchronizability of patients were broken, especially for those with SZ. The patients also showed alterations in brain regions with supercontroller roles and their distribution in the cognitive system. Finally, we were able to accurately discriminate and predict patients with SZ and BD. Our findings provide novel dynamic metrics evidence that patients with SZ and BD are characterized by a selective disruption of brain network controllability, potentially leading to reduced brain state transfer capacity, and offer new guidance for the clinical diagnosis of mental illness.