Gray Matter Networks Research Articles

Aberrant brain gray matter and functional networks topology in end stage renal disease patients undergoing maintenance hemodialysis with cognitive impairment.

PurposeTo characterize the topological properties of gray matter (GM) and functional networks in end-stage renal disease (ESRD) patients undergoing maintenance hemodialysis to provide insights into the underlying mechanisms of cognitive impairment.Materials and methodsIn total, 45 patients and 37 healthy controls were prospectively enrolled in this study. All subjects completed resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) examinations and a Montreal cognitive assessment scale (MoCA) test. Differences in the properties of GM and functional networks were analyzed, and the relationship between brain properties and MoCA scores was assessed. Cognitive function was predicted based on functional networks by applying the least squares support vector regression machine (LSSVRM) and the whale optimization algorithm (WOA).ResultsWe observed disrupted topological organizations of both functional and GM networks in ESRD patients, as indicated by significantly decreased global measures. Specifically, ESRD patients had impaired nodal efficiency and degree centrality, predominantly within the default mode network, limbic system, frontal lobe, temporal lobe, and occipital lobe. Interestingly, the involved regions were distributed laterally. Furthermore, the MoCA scores significantly correlated with decreased standardized clustering coefficient (γ), standardized characteristic path length (λ), and nodal efficiency of the right insula and the right superior temporal gyrus. Finally, optimized LSSVRM could predict the cognitive scores of ESRD patients with great accuracy.ConclusionDisruption of brain networks may account for the progression of cognitive dysfunction in ESRD patients. Implementation of prediction models based on neuroimaging metrics may provide more objective information to promote early diagnosis and intervention.

Gray Matter Network Associated With Attention in Children With Attention Deficit Hyperactivity Disorder.

BackgroundAttention deficit hyperactivity disorder (ADHD) is one of the most prevalent childhood-onset neurodevelopmental disorders; however, the underlying neural mechanisms for the inattention symptom remain elusive for children with ADHD. At present, the majority of studies have analyzed the structural MRI (sMRI) with the univariate method, which fails to demonstrate the interregional covarying relationship of gray matter (GM) volumes among brain regions. The scaled subprofile model of principal component analysis (SSM-PCA) is a multivariate method, which can detect more robust brain-behavioral phenotype association compared to the univariate analysis method. This study aims to identify the GM network associated with attention in children with ADHD by applying SSM-PCA to the sMRI.MethodsThe sMRI of 209 children with ADHD and 209 typically developing controls (TDCs) aged 7–14 years from the ADHD-200 dataset was used for anatomical computation, and the GM volume in each brain region was acquired. Then, SSM-PCA was applied to the GM volumes of all the subjects to capture the GM network of children with ADHD (i.e., ADHD-related pattern). The relationship between the expression of ADHD-related pattern and inattention symptom was further investigated. Finally, the influence of sample size on the analysis of this study was explored.ResultsThe ADHD-related pattern mainly included putamen, pallium, caudate, thalamus, right accumbens, superior/middle/inferior frontal cortex, superior occipital cortex, superior parietal cortex, and left middle occipital cortex. In addition, the expression of the ADHD-related pattern was related to inattention scores measured by the Conners’ Parent Rating Scale long version (CPRS-LV; r = 0.25, p = 0.0004) and the DuPaul ADHD Rating Scale IV (ADHD-RS; r = 0.18, p = 0.03). Finally, we found that when the sample size was 252, the results of ADHD-related pattern were relatively reliable. Similarly, the sample size needed to be 162 when exploring the relationship between ADHD-related pattern and behavioral indicator measured by CPRS-LV.ConclusionWe captured a GM network associated with attention in children with ADHD, which is different from that in adolescents and adults with ADHD. Our findings may shed light on the diverse neural mechanisms of inattention and provide treatment targets for children with ADHD.

Altered grey matter integrity and network vulnerability relate to epilepsy occurrence in patients with multiple sclerosis.

The aim of this study was to investigate the relevance of compartmentalized grey matter (GM) pathology and network reorganization in multiple sclerosis (MS) patients with concomitant epilepsy. From 3-T magnetic resonance imaging scans of 30 MS patients with epilepsy (MSE group; age 41 ± 15 years, 21 females, disease duration 8 ± 6 years, median Expanded Disability Status Scale [EDSS] score 3), 60 MS patients without epilepsy (MS group; age 41 ± 12 years, 35 females, disease duration 6 ± 4 years, EDSS score 2), and 60 healthy subjects (HS group; age 40 ± 13 years, 27 females) the regional volumes of GM lesions and of cortical, subcortical and hippocampal structures were quantified. Network topology and vulnerability were modelled within the graph theoretical framework. Receiver-operating characteristic (ROC) curve analysis was applied to assess the accuracy of GM pathology measures to discriminate between MSE and MS patients. Higher lesion volumes within the hippocampus, mesiotemporal cortex and amygdala were detected in the MSE compared to the MS group (all p < 0.05). The MSE group had lower cortical volumes mainly in temporal and parietal areas compared to the MS and HS groups (all p < 0.05). Lower hippocampal tail and presubiculum volumes were identified in both the MSE and MS groups compared to the HS group (all p < 0.05). Network topology in the MSE group was characterized by higher transitivity and assortativity, and higher vulnerability compared to the MS and HS groups (all p < 0.05). Hippocampal lesion volume yielded the highest accuracy (area under the ROC curve 0.80 [0.67-0.91]) in discriminating between MSE and MS patients. High lesion load, altered integrity of mesiotemporal GM structures, and network reorganization are associated with a greater propensity for epilepsy occurrence in people with MS.

Topologically Disrupted Gray Matter Networks in Drug-Naïve Essential Tremor Patients With Poor Sleep Quality

BackgroundSleep disturbances are widespread among patients with essential tremor (ET) and may have adverse effects on patients' quality of life. However, the pathophysiology underlying poor quality of sleep (QoS) in patients with ET remains unclear. Our study aimed to identify gray matter (GM) network alterations in the topological properties of structural MRI related to QoS in patients with ET.MethodWe enrolled 45 ET patients with poor QoS (SleET), 59 ET patients with normal QoS (NorET), and 66 healthy controls (HC), and they all underwent a three-dimensional T1-weighted MRI scan. We used a graph-theoretical approach to investigate the topological organization of GM morphological networks, and individual morphological brain networks were constructed according to the interregional similarity of GM volume distributions. Furthermore, we performed network-based statistics, and partial correlation analyses between topographic features and clinical characteristics were conducted.ResultsGlobal network organization was disrupted in patients with ET. Compared with the NorET group, the SleET group exhibited disrupted topological GM network organization with a shift toward randomization. Moreover, they showed altered nodal centralities in mainly the frontal, temporal, parietal, and cerebellar lobes. Morphological connection alterations within the default mode network (DMN), salience, and basal ganglia networks were observed in the SleET group and were generally more extensive than those in the NorET and HC groups. Alterations within the cerebello-thalamo-(cortical) network were only detected in the SleET group. The nodal degree of the left thalamus was negatively correlated with the Fahn-Tolosa-Marin Tremor Rating Scale score (r = −0.354, p =0.027).ConclusionOur findings suggest that potential complex interactions underlie tremor and sleep disruptions in patients with ET. Disruptions within the DMN and the cerebello-thalamo-(cortical) network may have a broader impact on sleep quality in patients with ET. Our results offer valuable insight into the neural mechanisms underlying poor QoS in patients with ET.

Differential alterations in brain structural network organization during addiction between adolescents and adults.

The adolescent brain may be susceptible to the influences of illicit drug use. While compensatory network reorganization is a unique developmental characteristic that may restore several brain disorders, its association with methamphetamine (MA) use-induced damage during adolescence is unclear. Using independent component (IC) analysis on structural magnetic resonance imaging data, spatially ICs described as morphometric networks were extracted to examine the effects of MA use on gray matter (GM) volumes and network module connectivity in adolescents (51 MA users v. 60 controls) and adults (54 MA users v. 60 controls). MA use was related to significant GM volume reductions in the default mode, cognitive control, salience, limbic, sensory and visual network modules in adolescents. GM volumes were also reduced in the limbic and visual network modules of the adult MA group as compared to the adult control group. Differential patterns of structural connectivity between the basal ganglia (BG) and network modules were found between the adolescent and adult MA groups. Specifically, adult MA users exhibited significantly reduced connectivity of the BG with the default network modules compared to control adults, while adolescent MA users, despite the greater extent of network GM volume reductions, did not show alterations in network connectivity relative to control adolescents. Our findings suggest the potential of compensatory network reorganization in adolescent brains in response to MA use. The developmental characteristic to compensate for MA-induced brain damage can be considered as an age-specific therapeutic target for adolescent MA users.

Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage.

Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early‐onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early‐onset (EOS) and adult‐onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co‐varying patterns by jointly analyzing three MRI features: fractional amplitude of low‐frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug‐naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub‐cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug‐naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug‐naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.

Can Cognitive training Reignite Compensatory Mechanisms in Advanced Multiple Sclerosis Patients? An Explorative Morphological Network Approach

Multiple Sclerosis (MS) has been shown to significantly impair brain connectivity, as alterations in functional and structural networks have been identified and associated with clinical status, particularly cognitive deficits. We aimed to identify structural connectivity changes in grey matter networks following cognitive rehabilitation (CR) in persons with MS (PwMS). Fifteen long-standing PwMS underwent a 5-week CR-program and five healthy controls (HC) were also investigated. T1-weighted MRI scans and neuropsychological tests were obtained before and after CR. T1-weighted scans were used to examine grey matter networks with graph analytic parameters [betweenness centrality (BC), degree (D), clustering (Cl), path length (PL) and small world properties: connectedness, gamma and lambda values]. Results were analysed at the whole brain level and for each brain lobe. Before CR, PwMS displayed lower values for D in the left parietal lobe (p = 0.009) compared to HC. After CR, significant increases in Cl located in frontal (p = 0.024) and temporal (p = 0.026) regions in PwMS were accompanied by significant decreases in PL located in the right parietal lobe (p = 0.025) and BC globally (p = 0.010). Overall, CR may prevent a network worsening in long-standing PwMS by increasing local efficiency of the brain and therefore facilitating compensation mechanisms.

Altered Intra- and Inter-Network Connectivity in Drug-Naïve Patients With Early Parkinson's Disease.

The aim of our study was to investigate differences in whole brain connectivity at different levels between drug-naïve individuals with early Parkinson’s disease (PD) and healthy controls (HCs). Resting-state functional magnetic resonance imaging data were collected from 47 patients with early-stage, drug-naïve PD and 50 HCs. Functional brain connectivity was analyzed at the integrity, network, and edge levels; UPDRS-III, MMSE, MOCA, HAMA, and HAMD scores, reflecting the symptoms of PD, were collected for further regression analysis. Compared with age-matched HCs, reduced functional connectivity were mainly observed in the visual (VSN), somatomotor (SMN), limbic (LBN), and deep gray matter networks (DGN) at integrity level [p < 0.05, false discovery rate (FDR) corrected]. Intra-network analysis indicated decreased functional connectivity in DGN, SMN, LBN, and ventral attention networks (VAN). Inter-network analysis indicated reduced functional connectivity in nine pairs of resting-state networks. At the edge level, the LBN was the center of abnormal functional connectivity (p < 0.05, FDR corrected). MOCA score was associated with the intra-network functional connectivity strength (FC) of the DGN, and inter-network FC of the DGN-VAN. HAMA and HAMD scores were associated with the FC of the SMN and DGN, and either the LBN or VAN, respectively. We demonstrated variations in whole brain connections of drug-naïve patients with early PD. Major changes involved the SMN, DGN, LBN, and VSN, which may be relevant to symptoms of early PD. Additionally, our results support PD as a disconnection syndrome.

Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline.

Individuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1–42 levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.

Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study.

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

Gray matter network properties show distinct associations with CSF p-tau 181 levels and amyloid status in individuals without dementia.

Gray matter networks are altered with amyloid accumulation in the earliest stage of AD, and are associated with decline throughout the AD spectrum. It remains unclear to what extent gray matter network abnormalities are associated with hyperphosphorylated-tau (p-tau). We studied the relationship of cerebrospinal fluid (CSF) p-tau181 with gray matter networks in non-demented participants from the European Prevention of Alzheimer's Dementia (EPAD) cohort, and studied dependencies on amyloid and cognitive status. Gray matter networks were extracted from baseline structural 3D T1w MRI. P-tau181 and abeta were measured with the Roche cobas Elecsys System. We studied the associations of CSF biomarkers levels with several network's graph properties. We further studied whether the relationships of p-tau 181 and network measures were dependent on amyloid status and cognitive stage (CDR). We repeated these analyses for network properties at a regional level, where we averaged local network values across cubes within each of 116 areas as defined by the automated anatomical labeling (AAL) atlas. Amyloid positivity was associated with higher network size and betweenness centrality, and lower gamma, clustering and small-world coefficients. Higher CSF p-tau 181 levels were related to lower betweenness centrality, path length and lambda coefficients (all p < 0.01). Three-way interactions between p-tau181, amyloid status and CDR were found for path length, lambda and clustering (all p < 0.05): Cognitively unimpaired amyloid-negative participants showed lower path length and lambda values with higher CSF p-tau181 levels. Amyloid-positive participants with impaired cognition demonstrated lower clustering coefficients in association to higher CSF p-tau181 levels. Our results suggest that alterations in gray matter network clustering coefficient is an early and specific event in AD.

Large-Scale Dysfunctional White-Matter and Grey Matter Networks in Patients With Social Anxiety Disorder

Large-Scale Dysfunctional White-Matter and Grey Matter Networks in Patients With Social Anxiety Disorder

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

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

Tau deposition is associated with grey matter network breakdown across different stages of the Alzheimer’s disease continuum

AbstractBackgroundChanges in grey matter covariance networks have been reported in preclinical and clinical stages of Alzheimer’s disease (AD), and have been associated with amyloid‐β (Aβ) deposition and cognitive decline. However, the role of tau pathology on grey matter networks remains unclear. Based on previously reported associations between tau pathology, synaptic density and brain structural measures, tau‐related connectivity changes across different stages of AD might be expected.MethodIn this study, we examined the relationship between tau aggregation (using tau positron emission tomography [tau‐PET]) and grey matter networks (using T1‐weighted images) in 533 individuals (178 Aβ‐negative cognitively unimpaired (CU) subjects, 105 Aβ‐positive CU subjects (preclinical AD), 122 Aβ‐positive patients with mild cognitive impairment (prodromal AD), and 128 patients with AD dementia) from the Swedish BioFINDER‐2 study (Table 1). Single‐subject grey matter networks were extracted and graph theory properties including degree, connectivity density, clustering coefficient, path length, and small world topology were calculated. Associations between tau‐PET values and global and regional network measures were examined using linear regression models adjusted for age, sex, total intracranial volume, and connectivity density.ResultAcross the whole sample, we found that higher tau load in the temporal meta‐ROI was associated with significant changes in degree, connectivity density, clustering, path length, and small world values of the brain network (all p &lt; 0.001; Fig. 1, 2), indicative of a less optimal network organization. Already in early disease stages, higher tau PET signal was associated with lower clustering and path length (Table 2). In more advanced disease stages elevated tau pathology was progressively associated with more advanced brain network abnormalities.ConclusionThese findings suggest that tau pathology is associated with a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions indicative of a more random network topology across different AD stages.

Voxel‐based correlation of 18F‐THK5351 accumulation with gray matter structural networks in cognitively normal older adults

AbstractBackgroundAlthough gray matter (GM) structural networks based on intracortical similarities have been proposed, no studies have yet examined the correlations between GM network measures and tau. Here, we investigated the voxel‐wise correlations of GM network measures and 18F‐THK5351 accumulation in cognitively normal (CN) older adults.MethodWe recruited 47 amyloid‐negative CN older adults (65.0 ± 7.9 y age, 26 female, 14.3 ± 2.4 y education, Mini‐Mental State Examination score 29.3 ± 1.1, Wechsler Memory Scale‐Revised Logical Memory II score 13.4 ± 2.9). All participants underwent 3D T1‐weighted MRI, 11C‐PiB and 18F‐THK5351 PET scans. Single‐subject GM networks extracted from T1‐weighted MRI data based on intracortical similarities were analyzed using the graph theoretical approach. Correlations between four local network measures (betweenness centrality, clustering coefficient, characteristic path length, and degree) and 18F‐THK5351 standardized uptake value ratio (SUVR) images were evaluated at a voxel level using biological parametric mapping (BPM) toolbox in SPM. The results with the following criteria were deemed significant: a height threshold of p &lt; .05 (family‐wise error corrected) with an extent threshold of 100 voxels.ResultSignificant positive correlations between four local network measures and 18F‐THK5351 SUVR images were detected in the bilateral caudate.ConclusionUsing voxel‐based analysis, we detected significant positive correlations between local network measures and 18F‐THK5351 accumulation in the bilateral caudate in CN older adults. Our findings suggest that tau and neuroinflammation in CN elderly may influence local GM network in the caudate.

Grey matter connectome abnormalities and age-related effects in antipsychotic-naive schizophrenia.

BackgroundConvergent evidence is increasing to indicate progressive brain abnormalities in schizophrenia. Knowing the brain network features over the illness course in schizophrenia, independent of effects of antipsychotic medications, would extend our sight on this question.MethodsWe recruited 237 antipsychotic-naive patients with schizophrenia range from 16 to 73 years old, and 254 healthy controls. High-resolution T1 weighted images were obtained with a 3.0T MR scanner. Grey matter networks were constructed individually based on the similarities of regional grey matter measurements. Network metrics were compared between patient groups and healthy controls, and regression analyses with age were conducted to determine potential differential rate of age-related changes between them.FindingsNodal centrality abnormalities were observed in patients with untreated schizophrenia, particularly in the central executive, default mode and salience networks. Accelerated age-related declines and illness duration-related declines were observed in global assortativity, and in nodal metrics of left superior temporal pole in schizophrenia patients. Although no significant intergroup differences in age-related regression were observed, the pattern of network metric alternation of left thalamus indicated higher nodal properties in early course patients, which decreased in long-term ill patients.InterpretationsGlobal and nodal alterations in the grey matter connectome related to age and duration of illness in antipsychotic-naive patients, indicating potentially progressive network organizations mainly involving temporal regions and thalamus in schizophrenia independent from medication effects.FundingThe National Natural Science Foundation of China, Sichuan Science and Technology Program, the Fundamental Research Funds for the Central Universities, Post-Doctor Research Project, West China Hospital, Sichuan University , the Science and Technology Project of the Health Planning Committee of Sichuan, Postdoctoral Interdisciplinary Research Project of Sichuan University and 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University.

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

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

Altered single-subject gray matter structural networks in drug-naïve attention deficit hyperactivity disorder children.

Altered topological organization of brain structural covariance networks has been observed in attention deficit hyperactivity disorder (ADHD). However, results have been inconsistent, potentially related to confounding medication effects. In addition, since structural networks are traditionally constructed at the group level, variabilities in individual structural features remain to be well characterized. Structural brain imaging with MRI was performed on 84 drug‐naïve children with ADHD and 83 age‐matched healthy controls. Single‐subject gray matter (GM) networks were obtained based on areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared using nonparametric permutation testing. Compared with healthy subjects, GM networks in ADHD patients demonstrated significantly altered topological characteristics, including higher global and local efficiency and clustering coefficient, and shorter path length. In addition, ADHD patients exhibited abnormal centrality in corticostriatal circuitry including the superior frontal gyrus, orbitofrontal gyrus, medial superior frontal gyrus, precentral gyrus, middle temporal gyrus, and pallidum (all p < .05, false discovery rate [FDR] corrected). Altered global and nodal topological efficiencies were associated with the severity of hyperactivity symptoms and the performance on the Stroop and Wisconsin Card Sorting Test tests (all p < .05, FDR corrected). ADHD combined and inattention subtypes were differentiated by nodal attributes of amygdala (p < .05, FDR corrected). Alterations in GM network topologies were observed in drug‐naïve ADHD patients, in particular in frontostriatal loops and amygdala. These alterations may contribute to impaired cognitive functioning and impulsive behavior in ADHD.

Altered brain network in first-episode, drug-naive patients with major depressive disorder

BackgroundResting-state functional magnetic resonance imaging has been widely used for the assessment of brain functional network, yet with inconsistent results. The present study aimed to investigate intranetwork and internetwork connectivity differences between patients with major depressive disorder (MDD) and healthy controls at the integrity, network and edge levels of 8 well-defined resting state networks. MethodsThirty patients with MDD and sixty-three healthy control subjects were recruited in this study. ResultsCompared with healthy controls, patients with MDD showed increased node degree in the right amygdala and putamen, increased connectivity strength in the deep gray matter network (DGN) and increased functional connectivity in intranetwork and internetwork. Meanwhile, MDD showed decreased connectivity strength in visual network-DGN pair. LimitationsThe sample size was small, and all patients in this study were of Asian ethnicity, especially Han individuals. ConclusionsThese findings demonstrate that MDD cases and healthy controls may have divergent intranetwork and internetwork connectivity at an early stage without confounding influence of medication. These differences may underlie cognitive and behavioral alterations in patients with MDD. And these differences may help with the discrimination of patients and healthy people at an early stage of MDD. More studies in the future are warranted to assist in the diagnosis of this burdensome disease.

Modular-level alterations of single-subject gray matter networks in schizophrenia.

Schizophrenia is often regarded as a psychiatric disorder caused by disrupted connections in the brain. Evidence suggests that the gray matter of schizophrenia patients is damaged in a modular pattern. Recently, abnormal topological organization was observed in the gray matter networks of patients with schizophrenia. However, the modular-level alteration of gray matter networks in schizophrenia remains unclear. In this study, single-subject gray matter networks were constructed for a total of 217 subjects (116 patients with schizophrenia and 101 controls). We analyzed the topological characteristics of the brain network and the strengths of connections between and within modules. Compared with the outcomes in the control group, the global efficiency and participation coefficient values of the single-subject gray matter networks in schizophrenic patients were significantly reduced. The nodal participation coefficient of the regions involving the frontoparietal attention network, default mode network and subcortical network were significantly decreased in subjects with schizophrenia. The intermodule connections between the frontoparietal attention network and visual network and between the default mode network and subcortical network, in the frontoparietal attention network were significantly reduced in the patient group. In the frontoparietal attention network, the intramodule nodal connection strength of the left orbital inferior frontal gyrus and right inferior parietal gyrus was significantly decreased in schizophrenia patients. Reduced intermodule nodal connection strength between the frontoparietal attention network and visual network was associated with the severity of schizophrenia symptoms. These findings suggest that abnormal intramodule and intermodule connections in the structural brain network may a biomarker of schizophrenia symptoms.