Structural Covariance Network Disruption and Functional Compensation in Parkinson's Disease

Altered structural covariance of the cortex and hippocampal formation in patients with lung cancer after chemotherapy

Objective

In this retrospective study, we aimed to investigate changes in brain morphology and structural topological networks in patients with lung cancer (LC) with or without chemotherapy.

Methods

We retrospectively recruited 191 participants for this cross-sectional study, including 113 patients with LC without chemotherapy (Ch-), 38 patients with LC with chemotherapy (Ch+), and 40 healthy controls (HC) matched for age, sex, and education. The gray matter volume (GMV) and cortical properties were compared among the three groups. We constructed the structural covariant network (SCN) based on cortical thickness, volumes of subcortical structures, and volumes of hippocampal subfields and the amygdala in all participants. The global and nodal topological properties of SCN were compared among groups. In addition, 23 patients with LC (8 Ch+ and 15 Ch-) who received two identical brain magnetic resonance scans were enrolled in the follow-up study. The paired t-test was used to compare group differences in brain morphology and topological properties in the structural network.

Results

The GMV of the bilateral caudate and thalamus were smaller in the Ch- and Ch + groups compared to the HC group using threshold-free cluster enhancement and permutation (P < 0.05, 5000 times permutations) for multiple comparison correction. The cortical SCN analysis suggested multiple enhanced nodal properties in several brain areas in Ch+ and Ch-compared to HC, mainly in the temporal gyrus, using permutations test and false discovery rate (FDR) (P < 0.05) corrections. Moreover, an increased sigma was found in the Ch + compared with HC (P = 0.0238). The reduced nodal degree (P = 0.0002) and betweenness (P = 0.0008) in the right amygdala of Ch + compared to HC were detected by subcortical SCN analysis. Furthermore, reduced gamma (P = 0.0342) and sigma (P < 0.0001) were found in Ch-compared with HC in the SCN analysis of subfields of the amygdala-hippocampal complex. In the follow-up study, reduced nodal degree (P < 0.0001) was found in the right anterior amygdala, and reduced clustering coefficient and local efficiency were found in patients with LC after the permutation test.

Conclusions

Our study showed GMV defects and structural topological property abnormalities related to LC and chemotherapy. Such morphological changes associated with LC and chemotherapy could be used as imaging markers for clinical assessments and pathological indicators.

Specific structural changes in Parkinson's disease-related olfactory dysfunction compared to others forms of olfactory dysfunction

Context

Olfactory dysfunction (OD) is a common early symptom of Parkinson’s disease (PD). However, OD is not specific to PD, as approximatively 20% of the general population exhibit different forms of OD. To use olfactory measures for early Parkinson screening, it is crucial to distinguish PD-related OD from Non-Parkinsonian OD (NPOD).

Objectives and hypothesis

This study aimed to compare the structural changes associated with PD-related OD (n = 15) with NPOD (n = 15), focusing on gray matter volumes and white matter fiber integrity in chemosensory regions. We hypothesized that PD-related OD presents specific structural alterations in these regions.

Methods

Participants underwent a 3 T MRI scan, which included anatomical T1 and diffusion-weighted imaging. Gray and white matter integrity were assessed using both whole-brain analyses (voxel-based morphometry—VBM and tract-based spatial statistics—TBSS, respectively) and localized approaches, including regions of interest and tractography.

Results

PD patients exhibited significantly higher gray matter volume in the left insula using restricted regions-of-interest analyses, while no other significant gray or white matter differences were found between groups.

Conclusion

Structural imaging of the gray matter, particularly the insula, but not of white matter, differentiates PD-related OD from NPOD.

Deformation-based morphometry: a sensitive imaging approach to detect radiation-induced brain injury?

BACKGROUND

Radiotherapy is a major therapeutic approach in patients with brain tumors. However, it leads to cognitive impairments. To improve the management of radiation-induced brain sequalae, deformation-based morphometry (DBM) could be relevant. Here, we analyzed the significance of DBM using Jacobian determinants (JD) obtained by non-linear registration of MRI images to detect local vulnerability of healthy cerebral tissue in an animal model of brain irradiation.

METHODS

Rats were exposed to fractionated whole-brain irradiation (WBI, 30 Gy). A multiparametric MRI (anatomical, diffusion and vascular) study was conducted longitudinally from 1 month up to 6 months after WBI. From the registration of MRI images, macroscopic changes were analyzed by DBM and microscopic changes at the cellular and vascular levels were evaluated by quantification of cerebral blood volume (CBV) and diffusion metrics including mean diffusivity (MD). Voxel-wise comparisons were performed on the entire brain and in specific brain areas identified by DBM. Immunohistology analyses were undertaken to visualize the vessels and astrocytes.

RESULTS

DBM analysis evidenced time-course of local macrostructural changes; some of which were transient and some were long lasting after WBI. DBM revealed two vulnerable brain areas, namely the corpus callosum and the cortex. DBM changes were spatially associated to microstructural alterations as revealed by both diffusion metrics and CBV changes, and confirmed by immunohistology analyses. Finally, matrix correlations demonstrated correlations between JD/MD in the early phase after WBI and JD/CBV in the late phase both in the corpus callosum and the cortex.

CONCLUSIONS

Brain irradiation induces local macrostructural changes detected by DBM which could be relevant to identify brain structures prone to radiation-induced tissue changes. The translation of these data in patients could represent an added value in imaging studies on brain radiotoxicity.

Structural underpinnings and long-term effects of resilience in Parkinson's disease

Resilience in neuroscience generally refers to an individual's capacity to counteract the adverse effects of a neuropathological condition. While resilience mechanisms in Alzheimer's disease are well-investigated, knowledge regarding its quantification, neurobiological underpinnings, network adaptations, and long-term effects in Parkinson's disease is limited. Our study involved 151 Parkinson's patients from the Parkinson's Progression Marker Initiative Database with available Magnetic Resonance Imaging, Dopamine Transporter Single-Photon Emission Computed Tomography scans, and clinical information. We used an improved prediction model linking neuropathology to symptom severity to estimate individual resilience levels. Higher resilience levels were associated with a more active lifestyle, increased grey matter volume in motor-associated regions, a distinct structural connectivity network and maintenance of relative motor functioning for up to a decade. Overall, the results indicate that relative maintenance of motor function in Parkinson's patients may be associated with greater neuronal substrate, allowing higher tolerance against neurodegenerative processes through dynamic network restructuring.

Changes in microstructural similarity of hippocampal subfield circuits in pathological cognitive aging

The hippocampal networks support multiple cognitive functions and may have biological roles and functions in pathological cognitive aging (PCA) and its associated diseases, which have not been explored. In the current study, a total of 116 older adults with 39 normal controls (NC) (mean age: 52.3 ± 13.64 years; 16 females), 39 mild cognitive impairment (MCI) (mean age: 68.15 ± 9.28 years, 14 females), and 38 dementia (mean age: 73.82 ± 8.06 years, 8 females) were included. The within-hippocampal subfields and the cortico-hippocampal circuits were assessed via a micro-structural similarity network approach using T1w/T2w ratio and regional gray matter tissue probability maps, respectively. An analysis of covariance was conducted to identify between-group differences in structural similarities among hippocampal subfields. The partial correlation analyses were performed to associate changes in micro-structural similarities with cognitive performance in the three groups, controlling the effect of age, sex, education, and cerebral small-vessel disease. Compared with the NC, an altered T1w/T2w ratio similarity between left CA3 and left subiculum was observed in the mild cognitive impairment (MCI) and dementia. The left CA3 was the most impaired region correlated with deteriorated cognitive performance. Using these regions as seeds for GM similarity comparisons between hippocampal subfields and cortical regions, group differences were observed primarily between the left subiculum and several cortical regions. By utilizing T1w/T2w ratio as a proxy measure for myelin content, our data suggest that the imbalanced synaptic weights within hippocampal CA3 provide a substrate to explain the abnormal firing characteristics of hippocampal neurons in PCA. Furthermore, our work depicts specific brain structural characteristics of normal and pathological cognitive aging and suggests a potential mechanism for cognitive aging heterogeneity.

A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis

Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Abnormal dynamic functional network connectivity in male obstructive sleep apnea with mild cognitive impairment: A data-driven functional magnetic resonance imaging study

Objective

The purpose of this study was to investigate the dynamic functional network connectivity (FNC) and its relationship with cognitive function in obstructive sleep apnea (OSA) patients from normal cognition (OSA-NC) to mild cognitive impairment (OSA-MCI).

Materials and methods

Eighty-two male OSA patients and 48 male healthy controls (HC) were included in this study. OSA patients were classified to OSA-MCI (n = 41) and OSA-NC (n = 41) based on cognitive assessments. The independent component analysis was used to determine resting-state functional networks. Then, a sliding-window approach was used to construct the dynamic FNC, and differences in temporal properties of dynamic FNC and functional connectivity strength were compared between OSA patients and the HC. Furthermore, the relationship between temporal properties and clinical assessments were analyzed in OSA patients.

Results

Two different connectivity states were identified, namely, State I with stronger connectivity and lower frequency, and State II with lower connectivity and relatively higher frequency. Compared to HC, OSA patients had a longer mean dwell time and higher fractional window in stronger connectivity State I, and opposite result were found in State II, which was mainly reflected in OSA-MCI patients. The number of transitions was an increasing trend and positively correlated with cognitive assessment in OSA-MCI patients. Compared with HC, OSA patients showed extensive abnormal functional connectivity in stronger connected State I and less reduced functional connectivity in lower connected State II, which were mainly located in the salience network, default mode network, and executive control network.

Conclusion

Our study found that OSA patients showed abnormal dynamic FNC properties, which was a continuous trend from HC, and OSA-NC to OSA-MCI, and OSA patients showed abnormal dynamic functional connectivity strength. The number of transformations was associated with cognitive impairment in OSA-MCI patients, which may provide new insights into the neural mechanisms in OSA patients.

Comparing the reliability of different ICA algorithms for fMRI analysis

Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.

Gray matter microstructural alterations in manganese-exposed welders: a preliminary neuroimaging study

OBJECTIVES

Chronic occupational manganese (Mn) exposure is characterized by motor and cognitive dysfunction. This study aimed to investigate structural abnormalities in Mn-exposed welders compared to healthy controls (HCs).

METHODS

Thirty-five HCs and forty Mn-exposed welders underwent magnetic resonance imaging (MRI) scans in this study. Based on T1-weighted MRI, the voxel-based morphometry (VBM), structural covariance, and receiver operating characteristic (ROC) curve were applied to examine whole-brain structural changes in Mn-exposed welders.

RESULTS

Compared to HCs, Mn-exposed welders had altered gray matter volume (GMV) mainly in the medial prefrontal cortex, lentiform nucleus, hippocampus, and parahippocampus. ROC analysis indicated the potential highest classification power of the hippocampus/parahippocampus. Moreover, distinct structural covariance patterns in the two groups were associated with regions, mainly including the thalamus, insula, amygdala, sensorimotor area, and middle temporal gyrus. No significant relationships were found between the findings and clinical characteristics.

CONCLUSIONS

Our findings showed Mn-exposed welders had changed GMV and structural covariance patterns in some regions, which implicated in motivative response, cognitive control, and emotional regulation. These results might provide preliminary evidence for understanding the pathophysiology of Mn overexposure.

KEY POINTS

• Chronic Mn exposure might be related to abnormal brain structural neural mechanisms. • Mn-exposed welders had morphological changes in brain regions implicated in emotional modulation, cognitive control, and motor-related response. • Altered gray matter volume in the hippocampus/parahippocampus and putamen might serve as potential biomarkers for Mn overexposure.

Altered Neural Network Connectivity Predicts Depression in de novo Parkinson’s Disease

Background

Depression, one of the most frequent non-motor symptoms in Parkinson’s disease (PD), was proposed to be related to neural network dysfunction in advanced PD patients. However, the underlying mechanisms in the early stage remain unclear. The study was aimed to explore the alterations of large-scale neural networks in de novo PD patients with depression.

Methods

We performed independent component analysis (ICA) on the data of resting-state functional magnetic resonance imaging from 21 de novo PD patients with depression (dPD), 34 de novo PD patients without depression (ndPD), and 43 healthy controls (HCs) to extract functional networks. Intranetwork and internetwork connectivity was calculated for comparison between groups, correlation analysis, and predicting the occurrence of depression in PD.

Results

We observed an ordered decrease of connectivity among groups within the ventral attention network (VAN) (dPD < ndPD < HCs), mainly located in the left middle temporal cortex. Besides, dPD patients exhibited hypoconnectivity between the auditory network (AUD) and default mode network (DMN) or VAN compared to ndPD patients or healthy controls. Correlation analysis revealed that depression severity was negatively correlated with connectivity value within VAN and positively correlated with the connectivity value of AUD-VAN in dPD patients, respectively. Further analysis showed that the area under the curve (AUC) for dPD prediction was 0.863 when combining the intranetwork connectivity in VAN and internetwork connectivity in AUD-DMN and AUD-VAN.

Conclusion

Our results demonstrated that early dPD may be associated with abnormality of attention bias and especially auditory attention processing. Altered neural network connectivity is expected to be a potential neuroimaging biomarker to predict depression in PD.

Locus coeruleus degeneration is associated with disorganized functional topology in Parkinson's disease

Degeneration of the locus coeruleus (LC) is recognized as a critical hallmark of Parkinson's disease (PD). Recent studies have reported that noradrenaline produced from the LC has critical effects on brain functional organization. However, it is unknown if LC degeneration in PD contributes to cognitive/motor manifestations through modulating brain functional organization. This study enrolled 94 PD patients and 68 healthy controls, and LC integrity was measured using the contrast-to-noise ratio of the LC (CNR_LC) calculated from T1-weighted magnetic resonance imaging. We used graph-theory-based network analysis to characterize brain functional organization. The relationships among LC degeneration, network disruption, and cognitive/motor manifestations in PD were assessed. Whether network disruption was a mediator between LC degeneration and cognitive/motor impairments was assessed further. In addition, an independent PD subgroup (n = 35) having functional magnetic resonance scanning before and after levodopa administration was enrolled to evaluate whether LC degeneration-related network deficiencies were independent of dopamine deficiency. We demonstrated that PD patients have significant LC degeneration compared to healthy controls. CNR_LC was positively correlated with Montreal Cognitive Assessment score and the nodal efficiency (NE) of several cognitive-related regions. Lower NE of the superior temporal gyrus was a mediator between LC degeneration and cognitive impairment in PD. However, levodopa treatment could not normalize the reduced NE of the superior temporal gyrus (mediator). In conclusion, we provided evidence for the relationship between LC degeneration and extensive network disruption in PD, and highlight the role of network disorganization in LC degeneration-related cognitive impairment.

Abnormal cortical thickness and structural covariance networks in systemic lupus erythematosus patients without major neuropsychiatric manifestations

BACKGROUND

Non-neuropsychiatric systemic lupus erythematosus (non-NPSLE) has been confirmed to have subtle changes in brain structure before the appearance of obvious neuropsychiatric symptoms. Previous literature mainly focuses on brain structure loss in non-NPSLE; however, the results are heterogeneous, and the impact of structural changes on the topological structure of patients' brain networks remains to be determined. In this study, we combined neuroimaging and network analysis methods to evaluate the changes in cortical thickness and its structural covariance networks (SCNs) in patients with non-NPSLE.

METHODS

We compare the cortical thickness of non-NPSLE patients (N=108) and healthy controls (HCs, N=88) using both surface-based morphometry (SBM) and regions of interest (ROI) methods, respectively. After that, we analyzed the correlation between the abnormal cortical thickness results found in the ROI method and a series of clinical features. Finally, we constructed the SCNs of two groups using the regional cortical thickness and analyzed the abnormal SCNs of non-NPSLE.

RESULTS

By SBM method, we found that cortical thickness of 34 clusters in the non-NPSLE group was thinner than that in the HC group. ROI method based on Destrieux atlas showed that cortical thickness of 57 regions in the non-NPSLE group was thinner than that in the HC group and related to the course of disease, autoantibodies, the cumulative amount of immunosuppressive agents, and cognitive psychological scale. In the SCN analysis, the cortical thickness SCNs of the non-NPSLE group did not follow the small-world attribute at a few densities, and the global clustering coefficient appeared to increase. The area under the curve analysis showed that there were significant differences between the two groups in clustering coefficient, degree, betweenness, and local efficiency. There are a total of seven hubs for non-NPSLE, and five hubs in HCs, the two groups do not share a common hub distribution.

CONCLUSION

Extensive and obvious reduction in cortical thickness and abnormal topological organization of SCNs are observed in non-NPSLE patients. The observed abnormalities may not only be the realization of brain damage caused by the disease, but also the contribution of the compensatory changes within the nervous system.

Structural covariance networks in schizophrenia: A systematic review Part II

BACKGROUND

Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia.

METHODS

A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria.

RESULTS

A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness.

CONCLUSIONS

Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.

The impact of dopaminergic treatment over cognitive networks in Parkinson's disease: Stemming the tide?

Dopamine‐replacing therapies are an effective treatment for the motor aspects of Parkinson's disease. However, its precise effect over the cognitive resting‐state networks is not clear; whether dopaminergic treatment normalizes their functional connectivity‐as in other networks‐ and the links with cognitive decline are presently unknown. We recruited 35 nondemented PD patients and 16 age‐matched controls. Clinical and neuropsychological assessments were performed at baseline, and conversion to dementia was assessed in a 10 year follow‐up. Structural and functional brain imaging were acquired in both the ON and practical OFF conditions. We assessed functional connectivity in both medication states compared to healthy controls, connectivity differences within participants related to the ON/OFF condition, and baseline connectivity of PD participants that converted to dementia compared to those who did not convert. PD participants showed and increased frontoparietal connectivity compared to controls: a pattern of higher connectivity between salience (SN) and default‐mode (DMN) networks both in the ON and OFF states. Within PD patients, this higher SN‐DMN connectivity characterized the participants in the ON state, while within‐DMN connectivity prevailed in the OFF state. Interestingly, participants who converted to dementia also showed higher SN‐DMN connectivity in their baseline ON scans compared to nonconverters. To conclude, PD patients showed higher frontoparietal connectivity in cognitive networks compared to healthy controls, irrespective of medication status, but dopaminergic treatment specifically promoted SN‐DM hyperconnectivity.

Homogeneous grey matter patterns in patients with obsessive-compulsive disorder

BACKGROUND

Changes in grey matter volume have frequently been reported in patients with obsessive-compulsive disorder (OCD). Most studies performed whole brain or region-of-interest based analyses whereas grey matter volume based on structural covariance networks has barely been investigated up to now. Therefore, the present study investigated grey matter volume within structural covariance networks in a sample of 228 participants (n = 117 OCD patients, n = 111 healthy controls).

METHODS

First, an independent component analysis (ICA) was performed on all subjects' preprocessed T1 images to derive covariance-dependent morphometric networks. Then, grey matter volume from each of the ICA-derived morphometric networks was extracted and compared between the groups. In addition, we performed logistic regressions and receiver operating characteristic (ROC) analyses to investigate whether network-related grey matter volume could serve as a characteristic that allows to differentiate patients from healthy volunteers. Moreover, we assessed grey matter pattern organization by correlating grey matter volume in all networks across all participants. Finally, we explored a potential association between grey matter volume or whole-brain grey matter pattern organization and clinical characteristics in terms of symptom severity and duration of illness.

RESULTS

There were only subtle group differences in network-related grey matter volume. Network-related grey matter volume had moreover a very poor discrimination performance. We found, however, significant group differences with regard to grey matter pattern organization. When correlating grey matter volume in all networks across all participants, patients showed a significantly higher homogeneity across all networks and a significantly lower heterogeneity, as assessed by the coefficient of variation across all networks as well as in several single networks. There was no association with clinical characteristics.

CONCLUSION

The findings of the present study suggest that the pathological mechanisms of OCD reduce interindividual grey matter variability. We assume that common characteristics associated with the disorder may lead to a more uniform, disorder-specific morphometry.