scholarly journals Brain gray matter network organization in psychotic disorders

2019 ◽  
Vol 45 (4) ◽  
pp. 666-674 ◽  
Author(s):  
Wenjing Zhang ◽  
Du Lei ◽  
Sarah K. Keedy ◽  
Elena I. Ivleva ◽  
Seenae Eum ◽  
...  
Keyword(s):  
Frontal Lobe ◽  
Gray Matter ◽  
Psychotic Disorders ◽  
Brain Networks ◽  
Single Subject ◽  
Healthy Controls ◽  
Whole Brain ◽  
Intermediate Phenotypes ◽  
Centrality Metrics ◽  
Subcortical Regions

AbstractAbnormal neuroanatomic brain networks have been reported in schizophrenia, but their characterization across patients with psychotic disorders, and their potential alterations in nonpsychotic relatives, remain to be clarified. Participants recruited by the Bipolar and Schizophrenia Network for Intermediate Phenotypes consortium included 326 probands with psychotic disorders (107 with schizophrenia (SZ), 87 with schizoaffective disorder (SAD), 132 with psychotic bipolar disorder (BD)), 315 of their nonpsychotic first-degree relatives and 202 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the participant groups. Compared with healthy controls, psychotic probands showed decreased nodal efficiency mainly in bilateral superior temporal regions. These regions had altered morphological relationships primarily with frontal lobe regions, and their network-level alterations were associated with positive symptoms of psychosis. Nonpsychotic relatives showed lower nodal centrality metrics in the prefrontal cortex and subcortical regions, and higher nodal centrality metrics in the left cingulate cortex and left thalamus. Diagnosis-specific analysis indicated that individuals with SZ had lower nodal efficiency in bilateral superior temporal regions than controls, probands with SAD only exhibited lower nodal efficiency in the left superior and middle temporal gyrus, and individuals with psychotic BD did not show significant differences from healthy controls. Our findings provide novel evidence of clinically relevant disruptions in the anatomic association of the superior temporal lobe with other regions of whole-brain networks in patients with psychotic disorders, but not in their unaffected relatives, suggesting that it is a disease-related trait. Network disorganization primarily involving frontal lobe and subcortical regions in nonpsychotic relatives may be related to familial illness risk.

scholarly journals Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

2019 ◽  
Author(s):  
Valeria Kebets ◽  
Avram J. Holmes ◽  
Csaba Orban ◽  
Siyi Tang ◽  
Jingwei Li ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Brain Networks ◽  
Higher Order ◽  
Large Set ◽  
Imaging Data ◽  
Multiple Control ◽  
Whole Brain ◽  
Diagnostic Categories ◽  
Intermediate Phenotypes ◽  
Latent Components

AbstractBackgroundThere is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thus neglecting the potential influence of huge swaths of the brain.MethodsA multivariate data-driven approach (partial least squares; PLS) was utilized to identify latent components linking a large set of clinical, cognitive and personality measures to whole-brain resting-state functional connectivity (RSFC) patterns across 224 participants. The participants were either healthy (N=110) or diagnosed with bipolar disorder (N=40), attention-deficit/hyperactivity disorder (N=37), schizophrenia (N=29) or schizoaffective disorder (N=8). In contrast to traditional case-control analyses, the diagnostic categories were not utilized in the PLS analysis, but were helpful for interpreting the components.ResultsOur analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction and impulsivity. Each component was associated with a unique whole-brain RSFC signature and shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network, and its connectivity with subcortical structures and cortical executive networks.ConclusionsWe identified three distinct dimensions with dissociable (but overlapping) whole-brain RSFC signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span across diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

scholarly journals Working memory networks and activation patterns in schizophrenia and bipolar disorder: comparison with healthy controls

2014 ◽  
Vol 204 (4) ◽  
pp. 290-298 ◽  
Author(s):  
Christine Lycke Brandt ◽  
Tom Eichele ◽  
Ingrid Melle ◽  
Kjetil Sundet ◽  
Andrés Server ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Working Memory ◽  
Large Scale ◽  
Psychotic Disorders ◽  
Brain Networks ◽  
Memory Task ◽  
Control Group ◽  
Healthy Controls ◽  
Activation Patterns ◽  
Patient Groups

BackgroundSchizophrenia and bipolar disorder are severe mental disorders with overlapping genetic and clinical characteristics, including cognitive impairments. An important question is whether these disorders also have overlapping neuronal deficits.AimsTo determine whether large-scale brain networks associated with working memory, as measured with functional magnetic resonance imaging (fMRI), are the same in both schizophrenia and bipolar disorder, and how they differ from those in healthy individuals.MethodPatients with schizophrenia (n = 100) and bipolar disorder (n = 100) and a healthy control group (n = 100) performed a 2-back working memory task while fMRI data were acquired. The imaging data were analysed using independent component analysis to extract large-scale networks of task-related activations.ResultsSimilar working memory networks were activated in all groups. However, in three out of nine networks related to the experimental task there was a graded response difference in fMRI signal amplitudes, where patients with schizophrenia showed greater activation than those with bipolar disorder, who in turn showed more activation than healthy controls. Secondary analysis of the patient groups showed that these activation patterns were associated with history of psychosis and current elevated mood in bipolar disorder.ConclusionsThe same brain networks were related to working memory in schizophrenia, bipolar disorder and controls. However, some key networks showed a graded hyperactivation in the two patient groups, in line with a continuum of neuronal abnormalities across psychotic disorders.

scholarly journals Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency

2016 ◽  
Vol 47 (4) ◽  
pp. 585-596 ◽  
Author(s):  
K. Baek ◽  
L. S. Morris ◽  
P. Kundu ◽  
V. Voon
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Data Driven ◽  
Local Network ◽  
Healthy Controls ◽  
Network Efficiency ◽  
Whole Brain ◽  
Network Properties ◽  
Obese Subjects

BackgroundThe efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED.MethodMulti-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%–25%. We also verified our findings using a separate parcellation, the Harvard–Oxford atlas parcellated into 470 regions.ResultsObese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis.ConclusionsUsing this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.

scholarly journals Low serum uric acid levels are associated with the nonmotor symptoms and brain gray matter volume in Parkinson’s disease

2021 ◽  
Author(s):  
Xiaoxue Shi ◽  
Jinhua Zheng ◽  
Jianjun Ma ◽  
Zhidong Wang ◽  
Wenhua Sun ◽  
...  
Keyword(s):  
Uric Acid ◽  
Cognitive Dysfunction ◽  
Gray Matter ◽  
Early Stage ◽  
Gray Matter Volume ◽  
Healthy Controls ◽  
Nonmotor Symptoms ◽  
Whole Brain ◽  
Matter Volume ◽  
Anxiety Depression

Abstract Background Uric acid (UA) plays a protective role in Parkinson’s disease (PD). To date, studies on the relationship between serum UA levels and nonmotor symptoms and brain gray matter volume in PD patients have been rare. Methods Automated enzymatic analysis was used to determine serum UA levels in 68 healthy controls and 88 PD patients, including those at the early (n = 56) and middle-late (n = 32) stages of the disease. Evaluation of motor symptoms and nonmotor symptoms in PD patients was assessed by the associated scales. Image acquisition was performed using a Siemens MAGNETOM Prisma 3 T MRI scanner. Results Serum UA levels in early stage PD patients were lower than those in healthy controls, and serum UA levels in the middle-late stage PD patients were lower than those in the early stage PD patients. Serum UA levels were significantly negatively correlated with the disease course, dysphagia, anxiety, depression, apathy, and cognitive dysfunction. ROC assessment confirmed that serum UA levels had good predictive accuracy for PD with dysphagia, anxiety, depression, apathy, and cognitive dysfunction. Furthermore, UA levels were significantly positively correlated with gray matter volume in whole brain. Conclusions This study shows that serum UA levels were correlated with the nonmotor symptoms of dysphagia, anxiety, depression, apathy, and cognitive dysfunction and the whole-brain gray matter volume. That is the first report examining the relationships between serum UA and clinical manifestations and imaging features in PD patients.

scholarly journals Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics

2019 ◽  
Vol 50 (11) ◽  
pp. 1852-1861 ◽  
Author(s):  
Du Lei ◽  
Walter H. L. Pinaya ◽  
Therese van Amelsvoort ◽  
Machteld Marcelis ◽  
Gary Donohoe ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Diagnostic Value ◽  
Subject Classification ◽  
System Level ◽  
Single Subject ◽  
Healthy Controls ◽  
Brain Images ◽  
Whole Brain ◽  
The Individual

AbstractBackgroundPrevious studies using resting-state functional neuroimaging have revealed alterations in whole-brain images, connectome-wide functional connectivity and graph-based metrics in groups of patients with schizophrenia relative to groups of healthy controls. However, it is unclear which of these measures best captures the neural correlates of this disorder at the level of the individual patient.MethodsHere we investigated the relative diagnostic value of these measures. A total of 295 patients with schizophrenia and 452 healthy controls were investigated using resting-state functional Magnetic Resonance Imaging at five research centres. Connectome-wide functional networks were constructed by thresholding correlation matrices of 90 brain regions, and their topological properties were analyzed using graph theory-based methods. Single-subject classification was performed using three machine learning (ML) approaches associated with varying degrees of complexity and abstraction, namely logistic regression, support vector machine and deep learning technology.ResultsConnectome-wide functional connectivity allowed single-subject classification of patients and controls with higher accuracy (average: 81%) than both whole-brain images (average: 53%) and graph-based metrics (average: 69%). Classification based on connectome-wide functional connectivity was driven by a distributed bilateral network including the thalamus and temporal regions.ConclusionThese results were replicated across the three employed ML approaches. Connectome-wide functional connectivity permits differentiation of patients with schizophrenia from healthy controls at single-subject level with greater accuracy; this pattern of results is consistent with the ‘dysconnectivity hypothesis’ of schizophrenia, which states that the neural basis of the disorder is best understood in terms of system-level functional connectivity alterations.

Brain structural differences in temporal lobe and frontal lobe epilepsy patients: A voxel-based morphometry and vertex-based surface analysis

2021 ◽  
pp. 197140092110348
Author(s):  
Chun-Qiang Lu ◽  
Grant P Gosden ◽  
Lela Okromelidze ◽  
Ayushi Jain ◽  
Vivek Gupta ◽  
...  
Keyword(s):  
Frontal Lobe ◽  
Surface Analysis ◽  
Temporal Lobe ◽  
Gray Matter ◽  
Control Group ◽  
Frontal Lobe Epilepsy ◽  
Healthy Controls ◽  
Voxel Based Morphometry ◽  
And Control ◽  
The Brain

Purpose Exploration of the effect of chronic recurrent seizures in focal epilepsy on brain volumes has produced many conflicting reports. To determine differences in brain structure in temporal lobe epilepsy (TLE) and extratemporal epilepsy (using frontal lobe epilepsy (FLE) a surrogate) further, we performed a retrospective analysis of a large cohort of patients with seizure-onset zone proven by intracranial monitoring. Methods A total of 120 TLE patients, 86 FLE patients, and 54 healthy controls were enrolled in this study. An analysis of variance of voxel-based morphometry (VBM) was used to seek morphometric brain differences among TLE patients, FLE patients, and healthy controls. Additionally, a vertex-based surface analysis was utilized to analyze the hippocampus and thalamus. Significant side-specific differences in hippocampal gray matter volume were present between the left TLE (LTLE), right TLE RTLE (RTLE), and control groups ( p<0.05, family-wise error (FWE) corrected). Results Vertex analyses revealed significant volume reduction in inferior parts of the left hippocampus in the LTLE group and lateral parts of the right hippocampus in the RTLE group compared to controls ( p<0.05, FWE corrected). Significant differences were also detected between the LTLE and control group in the bilateral medial and inferior thalamus ( p<0.05, FWE corrected). FLE patients did not exhibit focal atrophy of gray matter across the brain. Conclusion Our results highlight the variation in morphometric lateralized changes in the brain between different epilepsy onset zones, providing critical insight into the natural history of people with drug-resistant focal epilepsies.

scholarly journals Application of voxel-based morphometric measurement to brain magnetic resonance imaging in children with non-cyanotic congenital heart disease

2019 ◽  
Author(s):  
Xuan Jia ◽  
XIAOHUI MA ◽  
JIAWEI LIANG ◽  
HAICHUN ZHOU
Keyword(s):  
Magnetic Resonance Imaging ◽  
White Matter ◽  
Gray Matter ◽  
Congenital Heart ◽  
Healthy Controls ◽  
White Matter Volume ◽  
Resonance Imaging ◽  
Whole Brain ◽  
Matter Volume ◽  
Central Gyrus

Abstract Background Congenital heart disease（CHD） is a cardiovascular malformation caused by abnormal heart and/or vascular development in the fetus. In children with CHD, abnormalities in the development and function of the nervous system are common. At present, there is a lack of research on the preoperative neurological development and injury of young children with non-cyanotic CHD. The objective of the current study is to determine the changes in white matter, gray matter, and cerebrospinal fluid (CSF) by magnetic resonance imaging (MRI) in children with noncyanotic CHD as compared with healthy controls. MethodsChildren diagnosed with non-cyanotic CHD on ultrasonography (n=54) and healthy control subjects (n=35) aged 1–3 years. Brain MRI was performed prior to surgery for CHD. The SPM v12 software was used to calculate the volumes of the gray matter, white matter, CSF, and the whole brain (sum of the gray-matter, white-matter, and CSF volumes). Volume differences between the two groups were analyzed. Voxelbased morphometry was used to compare specific brain regions with statistically significant atrophy.ResultsCompared with the control group, the study group had significantly reduced whole-brain white-matter volume (P<0.05), but similar whole-brain graymatter, CSF, and whole-brain volumes(P>0.05). As compared with the healthy controls, children with non-cyanotic CHD had mild atrophy in the white matter of the anterior central gyrus, the posterior central gyrus and the pulvinar. ConclusionsChildren with non-cyanotic CHD show decreased white-matter volume before surgery, and this volume reduction is mainly concentrated in the somatosensory and somatic motor nerve regions.

Heterogeneity of Cerebral Blood Flow: a Fractal Approach

2000 ◽  
Vol 39 (02) ◽  
pp. 37-42 ◽  
Author(s):  
P. Hartikainen ◽  
J. T. Kuikka
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Fractal Dimension ◽  
Frontal Lobe ◽  
Fractal Analysis ◽  
Relative Dispersion ◽  
Emission Computed Tomography ◽  
Brain Blood Flow ◽  
Healthy Controls ◽  
Fractal Approach

Summary Aim: We demonstrate the heterogeneity of regional cerebral blood flow using a fractal approach and singlephoton emission computed tomography (SPECT). Method: Tc-99m-labelled ethylcysteine dimer was injected intravenously in 10 healthy controls and in 10 patients with dementia of frontal lobe type. The head was imaged with a gamma camera and transaxial, sagittal and coronal slices were reconstructed. Two hundred fifty-six symmetrical regions of interest (ROIs) were drawn onto each hemisphere of functioning brain matter. Fractal analysis was used to examine the spatial heterogeneity of blood flow as a function of the number of ROIs. Results: Relative dispersion (= coefficient of variation of the regional flows) was fractal-like in healthy subjects and could be characterized by a fractal dimension of 1.17 ± 0.05 (mean ± SD) for the left hemisphere and 1.15 ± 0.04 for the right hemisphere, respectively. The fractal dimension of 1.0 reflects completely homogeneous blood flow and 1.5 indicates a random blood flow distribution. Patients with dementia of frontal lobe type had a significantly lower fractal dimension of 1.04 ± 0.03 than in healthy controls. Conclusion: Within the limits of spatial resolution of SPECT, the heterogeneity of brain blood flow is well characterized by a fractal dimension. Fractal analysis may help brain scientists to assess age-, sex- and laterality-related anatomic and physiological changes of brain blood flow and possibly to improve precision of diagnostic information available for patient care.

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