Altered directional connectivity between emotion network and motor network in Parkinson's disease with depression

Disrupted topologic efficiency of brain functional connectome in de novo Parkinson's disease with depression

Growing evidence supports that depression in Parkinson's disease (PD) depends on disruptions in specific neural networks rather than regional dysfunction. According to the resting-state functional magnetic resonance imaging data, the study attempted to decipher the alterations in the topological properties of brain networks in de novo depression in PD (DPD). The study also explored the neural network basis for depressive symptoms in PD. We recruited 20 DPD, 37 non-depressed PD and 41 healthy controls (HC). The Graph theory and network-based statistical methods helped analyse the topological properties of brain functional networks and anomalous subnetworks across these groups. The relationship between altered properties and depression severity was also investigated. DPD revealed significantly reduced nodal efficiency in the left superior temporal gyrus. Additionally, DPD decreased five hubs, primarily located in the temporal-occipital cortex, and increased seven hubs, mainly distributed in the limbic cortico-basal ganglia circuit. The betweenness centrality of the left Medio Ventral Occipital Cortex was positively associated with depressive scores in DPD. In contrast to HC, DPD had a multi-connected subnetwork with significantly lower connectivity, primarily distributed in the visual, somatomotor, dorsal attention and default networks. Regional topological disruptions in the temporal-occipital region are critical in the DPD neurological mechanism. It might suggest a potential network biomarker among newly diagnosed DPD patients.

The thalamus is the causal hub of intervention in patients with major depressive disorder: Evidence from the Granger causality analysis

Major depressive disorder (MDD) is the leading mental disorder and afflicts more than 350 million people worldwide. The underlying neural mechanisms of MDD remain unclear, hindering the accurate treatment. Recent brain imaging studies have observed functional abnormalities in multiple brain regions in patients with MDD, identifying core brain regions is the key to locating potential therapeutic targets for MDD. The Granger causality analysis (GCA) measures directional effects between brain regions and, therefore, can track causal hubs as potential intervention targets for MDD. We reviewed literature employing GCA to investigate abnormal brain connections in patients with MDD. The total degree of effective connections in the thalamus (THA) is more than twice that in traditional targets such as the superior frontal gyrus and anterior cingulate cortex. Altered causal connections in patients with MDD mainly included enhanced bottom-up connections from the thalamus to various cortical and subcortical regions and reduced top-down connections from these regions to the THA, indicating excessive uplink sensory information and insufficient downlink suppression information for negative emotions. We suggest that the thalamus is the most crucial causal hub for MDD, which may serve as the downstream target for non-invasive brain stimulation and medication approaches in MDD treatment.

The pathobiological basis of depression in Parkinson disease: challenges and outlooks

Depression, with an estimated prevalence of about 40% is a most common neuropsychiatric disorder in Parkinson disease (PD), with a negative impact on quality of life, cognitive impairment and functional disability, yet the underlying neurobiology is poorly understood. Depression in PD (DPD), one of its most common non-motor symptoms, can precede the onset of motor symptoms but can occur at any stage of the disease. Although its diagnosis is based on standard criteria, due to overlap with other symptoms related to PD or to side effects of treatment, depression is frequently underdiagnosed and undertreated. DPD has been related to a variety of pathogenic mechanisms associated with the underlying neurodegenerative process, in particular dysfunction of neurotransmitter systems (dopaminergic, serotonergic and noradrenergic), as well as to disturbances of cortico-limbic, striato-thalamic-prefrontal, mediotemporal-limbic networks, with disruption in the topological organization of functional mood-related, motor and other essential brain network connections due to alterations in the blood-oxygen-level-dependent (BOLD) fluctuations in multiple brain areas. Other hypothetic mechanisms involve neuroinflammation, neuroimmune dysregulation, stress hormones, neurotrophic, toxic or metabolic factors. The pathophysiology and pathogenesis of DPD are multifactorial and complex, and its interactions with genetic factors, age-related changes, cognitive disposition and other co-morbidities awaits further elucidation.

Resting-state brain network in Parkinson’s disease with different degrees of depression

Objective

The aim of this study is to explore the neural network mechanism of Parkinson’s disease (PD) with different degrees of depression using independent component analysis (ICA) of the functional connectivity changes in the forehead, limbic system, and basal ganglia regions.

Methods

A total of 106 patients with PD were divided into three groups: PD with moderate-severe depression (PDMSD, n = 42), PD with mild depression (PDMD, n = 29), and PD without depression (PDND, n = 35). Fifty gender- and age-matched healthy subjects were recruited as a control group (HC). Three-dimensional T1-weighted image and resting-state functional magnetic resonance imaging (RS-fMRI) data were collected.

Results

Different functional connectivity was observed in the left precentral gyrus, right precuneus, right inferior frontal gyrus, right medial and paracingulate gyrus, left supplementary motor area, right brain insula, and the inferior frontal gyrus of the left orbit among the four groups (ANOVA, P < 0.05, Voxel size > 5). Both PDMD and PDMSD exhibited increased functional connectivity in the superior-posterior default-mode network (spDMN) and left frontoparietal network (LFPN); they also exhibited a decreased functional connectivity in the interior Salience Network (inSN) when compared with the PDND group. The functional connectivity within the inSN network was decreased in the PDMSD group when compared with the PDMD group (Alphasim correction, P < 0.05, voxel size > 5).

Conclusion

PD with different degrees of depression has abnormal functional connectivity in multiple networks, which is an important neurobiological basis for the occurrence and development of depression in PD. The degree of decreased functional connectivity in the inSN network is related to the degree of depression in patients with PD-D, which can be an imaging marker for PD to judge the severity of depression.

Abnormal cortical atrophy and functional connectivity are associated with depression in Parkinson’s disease

Objective

This study aimed to investigate the association of altered cortical thickness and functional connectivity (FC) with depression in Parkinson’s disease (PD).

Materials and methods

A total of 26 non-depressed PD patients (PD-ND), 30 PD patients with minor depression (PD-MnD), 32 PD patients with major depression (PD-MDD), and 30 healthy controls (HC) were enrolled. Differences in cortical thickness among the four groups were assessed, and the results were used to analyze FC differences in regions of cortical atrophy. Binary logistic regression and receiver operating characteristic (ROC) curve analyses were also performed to identify clinical features and neuroimaging biomarkers that might help in the prediction of PD-MDD.

Results

Patients with PD-MDD showed decreased cortical thickness compared to patients with PD-ND in the left superior temporal and right rostral middle frontal gyri (RMFG), as well as weak FC between the left superior temporal gyrus and right cerebellum posterior lobe and between right RMFG and right inferior frontal gyrus and insula. The combination of cortical thickness, FC, and basic clinical features showed strong potential for predicting PD-MDD based on the area under the ROC curve (0.927, 95% CI 0.854–0.999, p < 0.001).

Conclusion

Patients with PD-MDD show extensive cortical atrophy and FC alterations, suggesting that cortical thickness and FC may be neuroimaging-based diagnostic biomarkers for PD-MDD.

Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis

INTRODUCTION

The current diagnosis of Parkinson's disease (PD) comorbidity with depression (DPD) largely depends on clinical evaluation. However, the modality may tend to lack precision in detecting PD with depression. A radiomic approach that combines functional connectivity and activity with clinical scores has the potential to achieve accurate and differential diagnosis between PD and DPD.

METHODS

In this study, we aimed to employ the radiomic approach to extract large-scale features of functional connectivity and activity for differentiating among DPD, PD with no depression (NDPD), and healthy controls (HC). We extracted 6,557 features of five types from all subjects including clinical characteristics, resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and voxel-mirrored homotopic connectivity (VMHC). Lasso, random forest, and support vector machine (SVM) were implemented for feature selection and dimension reduction based on the training sets, and the prediction performance for different methods in the testing sets was compared.

RESULTS

The results showed that nineteen features were selected for the group of DPD versus HC, 34 features were selected for the group of NDPD versus HC, and 17 features were retained for the group of DPD versus NDPD. In the testing sets, Lasso prediction achieved the accuracies of 0.95, 0.96, and 0.85 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. Random forest achieved the accuracies of 0.90, 0.82, and 0.90 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively, while SVM yielded the accuracies of 1, 0.86 and 0.65 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively.

CONCLUSIONS

By identifying aberrant functional connectivity and activity as potential biomarkers, the radiomic approach facilitates a deeper understanding and provides new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy.

Insights into the Pathophysiology of Psychiatric Symptoms in Central Nervous System Disorders: Implications for Early and Differential Diagnosis

Different psychopathological manifestations, such as affective, psychotic, obsessive-compulsive symptoms, and impulse control disturbances, may occur in most central nervous system (CNS) disorders including neurodegenerative and neuroinflammatory diseases. Psychiatric symptoms often represent the clinical onset of such disorders, thus potentially leading to misdiagnosis, delay in treatment, and a worse outcome. In this review, psychiatric symptoms observed along the course of several neurological diseases, namely Alzheimer’s disease, fronto-temporal dementia, Parkinson’s disease, Huntington’s disease, and multiple sclerosis, are discussed, as well as the involved brain circuits and molecular/synaptic alterations. Special attention has been paid to the emerging role of fluid biomarkers in early detection of these neurodegenerative diseases. The frequent occurrence of psychiatric symptoms in neurological diseases, even as the first clinical manifestations, should prompt neurologists and psychiatrists to share a common clinico-biological background and a coordinated diagnostic approach.

Functional Connectome in Parkinson's Disease and Parkinsonism

PURPOSE OF REVIEW

There has been an exponential growth in functional connectomics research in neurodegenerative disorders. This review summarizes the recent findings and limitations of the field in Parkinson's disease (PD) and atypical parkinsonian syndromes.

RECENT FINDINGS

Increasingly more sophisticated methods ranging from seed-based to network and whole-brain dynamic functional connectivity have been used. Results regarding the disruption in the functional connectome vary considerably based on disease severity and phenotypes, and treatment status in PD. Non-motor symptoms of PD also link to the dysfunction in heterogeneous networks. Studies in atypical parkinsonian syndromes are relatively scarce. An important clinical goal of functional connectomics in neurodegenerative disorders is to establish the presence of pathology, track disease progression, predict outcomes, and monitor treatment response. The obstacles of reliability and reproducibility in the field need to be addressed to improve the potential of the functional connectome as a biomarker for these purposes in PD and atypical parkinsonian syndromes.

Changes in Degree Centrality of Network Nodes in Different Frequency Bands in Parkinson's Disease With Depression and Without Depression

Background

Depression induces an early onset of Parkinson’s disease (PD), aggravates dyskinesia and cognitive impairment, and accelerates disease progression. However, it is very difficult to identify and diagnose PD with depression (PDD) in the early clinical stage. Few studies have suggested that the changes in neural networks are associated with PDD, while degree centrality (DC) has been documented to be effective in detecting brain network changes.

Objectives

The objectives of this study are to explore DC changes between patients with PDD and without depression (PDND) and to find the key brain hubs involved with depression in PD patients.

Methods

One hundred and four PD patients and 54 healthy controls (HCs) underwent brain resting-state functional magnetic resonance imaging. The Data Processing and Analysis of Brain Imaging and Resting-State Functional Magnetic Resonance Data Analysis Toolkit were used for processing and statistical analysis. The DC value of each frequency band was calculated. One-way analysis of variance and a two-sample t-test for post hoc comparison were used to compare the differences of the DC values in different frequency bands among PDD, PDND, and healthy control group. Gaussian random field was used for multiple comparison correction. Pearson correlation analysis was performed between each individual’s DC map and clinical indicators.

Results

The DC value of different brain regions changed in PDD and PDND in different frequency bands. The prefrontal lobe, limbic system, and basal ganglia were the main brain regions involved. PDD patients showed a wider range and more abnormal brain areas in the slow-4 frequency band (0.027–0.073 Hz) compared to the HCs. PDD showed a decreased DC value in the medial frontal gyrus, bilateral cuneus gyrus, right lingual gyrus, bilateral supplementary motor area (SMA), bilateral superior frontal gyrus, and left paracentral lobule, but an increased DC value in the bilateral brainstem, midbrain, bilateral parahippocampal gyrus, cerebellum, left superior temporal gyrus, bilateral insula, left fusiform gyrus, and left caudate nucleus in the traditional frequency band (0.01–0.08 Hz) compared to PDND patients. PDND patients displayed more abnormal functions in the basal ganglia in the slow-4 frequency band.

Conclusion

The DC changes in PDD and PDND are frequency dependent and frequency specific. The medial frontal gyrus, SMA, and limbic system may be the key hubs for depression in PD.

Networks Are Associated With Depression in Patients With Parkinson's Disease: A Resting-State Imaging Study

Background

Disturbance of networks was recently proposed to be associated with the occurrence of depression in Parkinson's disease (PD). However, the neurobiological mechanism of depression underlying PD remains unclear.

Objective

This study was conducted to investigate whether intra-network and inter-network brain connectivity is differently changed in PD patients with and without depression (PDD and PDND patients, respectively).

Methods

Forty-one PDD patients, 64 PDND patients, and 55 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI). The default mode network (DMN), executive control network (ECN), salience network (SN), precuneus network (PCUN), and sensorimotor network (SMN) were extracted using independent component analysis (ICA), and then the functional connectivity (FC) values within and between these networks were measured.

Results

PDD patients exhibited abnormal FC values within the DMN, ECN, SN, PCUN, and SMN. In addition, PDD patients demonstrated decreased connectivity between anterior SN (aSN) and bilateral ECN, between posterior SN (pSN) and dorsal DMN (dDMN), and between PCUN and dDMN/SMN/bilateral ECN. Connectivity within the left hippocampus of dDMN and the right medial superior frontal gyrus of aSN was a significant predictor of depression level in PD patients.

Conclusions

Aberrant intra- and inter-network FC is involved in several important hubs in the large-scale networks, which can be a biomarker for distinguishing PDD from PDND.

Altered cerebellum functional network on newly diagnosed drug-naïve Parkinson's disease patients with anxiety

Introduction

Damage to the cerebellar functional network may underlie anxiety symptoms in patients with Parkinson’s disease (PD). Herein we investigated the regional homogeneity (ReHo) and functional connectivity (FC) patterns of cerebellar and clinical correlates in PD patients with anxiety and explored their clinical significance.

Methods

We enrolled 50 newly diagnosed drug-naïve PD patients and 30 normal controls (NCs). Twenty-six PD patients with anxiety symptoms (PD-A) and 24 PD patients without anxiety symptoms (PD-NA) were sorted into groups based on the Hamilton Anxiety Scale (HAMA). All included participants underwent rest-state functional magnetic resonance imaging (rs-fMRI) scanning. Cerebellar FC based on the seed-based method was used to investigate regional and whole brain function in PD-A, PD-NA, and NCs, and the relationship between the abnormal brain function and anxiety symptoms in PD patients was also detected.

Results

Compared with the PD-NA group and the NCs, the ReHo value of the PD-A group was significantly decreased in the left medial frontal gyrus and increased in the left cerebellum. Further, left-cerebellum-based FC patterns were used to detect the decreased FC in the right cerebellum, while FC was increased in the right caudate nucleus, and the right anterior cingulate cortex (ACC) in the PD-A group was compared with that in the PD-NA group. Further, the altered FC between the left cerebellum and the right cerebellum was significantly associated with anxiety symptoms in the PD-A group.

Conclusion

The present study found abnormal regional cerebellum function as well as disruptions in the connectivity network within the cerebellum, caudate, and ACC in patients with PD-A. In addition, the FC between the left cerebellum and the right cerebellum was associated with anxiety symptoms in patients with PD. The present study indicated that cerebellar functional damage may be associated with anxiety symptoms in PD patients.

Longitudinal Macro/Microstructural Alterations of Different Callosal Subsections in Parkinson's Disease Using Connectivity-Based Parcellation

Background

The corpus callosum (CC) is an important feature of Parkinson’s disease (PD) not only in motor but also in non-motor functions. However, CC is not a homogeneous component, and the damage of specific subsection may contribute to corresponding clinical deficit.

Objective

The objective of the study is to investigate the structural alterations of different callosal subsections cross-sectionally and longitudinally in PD and evaluate their relationships to clinical performance.

Methods

Thirty-nine PD patients who had been longitudinally reexamined and 82 normal controls (NC) were employed. According to their specific callosal–cortical connectivity, 3D CC was divided into five subsections (including prefrontal, premotor, motor, somatosensory, and temporal–parietal–occipital subsection). The fractional anisotropy (FA), mean diffusivity (MD), and volume of whole CC and its subsections were computed and compared between groups. Regression model was constructed to explore the relationships between callosal structure and clinical performance.

Results

At baseline, PD did not show any significant macro/microstructural difference compared with NC. During disease course, there was a decreased FA and increased MD of whole CC as well as its subsections (except temporal–parietal–occipital subsection), and the volume of motor subsection was decreased. Moreover, the FA of temporal–parietal–occipital subsection and volume of motor subsection were correlated with the mood domain at baseline, and the MD of somatosensory subsection was associated with the motor domain at follow-up.

Conclusion

The structure of CC and its connectivity-specific subsections remain preserved at a relatively early stage in PD and are progressively disrupted during disease course. Besides, different callosal subsections possess specific associations with clinical performance in PD.

Common and unique connectivity at the interface of motor, neuropsychiatric, and cognitive symptoms in Parkinson's disease: A commonality analysis

Parkinson's disease (PD) is characterized by overlapping motor, neuropsychiatric, and cognitive symptoms. Worse performance in one domain is associated with worse performance in the other domains. Commonality analysis (CA) is a method of variance partitioning in multiple regression, used to separate the specific and common influence of collinear predictors. We apply, for the first time, CA to the functional connectome to investigate the unique and common neural connectivity underlying the interface of the symptom domains in 74 non-demented PD subjects. Edges were modeled as a function of global motor, cognitive, and neuropsychiatric scores. CA was performed, yielding measures of the unique and common contribution of the symptom domains. Bootstrap confidence intervals were used to determine the precision of the estimates and to directly compare each commonality coefficient. The overall model identified a network with the caudate nucleus as a hub. Neuropsychiatric impairment accounted for connectivity in the caudate-dorsal anterior cingulate and caudate-right dorsolateral prefrontal-right inferior parietal circuits, while caudate-medial prefrontal connectivity reflected a unique effect of both neuropsychiatric and cognitive impairment. Caudate-precuneus connectivity was explained by both unique and shared influence of neuropsychiatric and cognitive symptoms. Lastly, posterior cortical connectivity reflected an interplay of the unique and common effects of each symptom domain. We show that CA can determine the amount of variance in the connectome that is unique and shared amongst motor, neuropsychiatric, and cognitive symptoms in PD, thereby improving our ability to interpret the data while gaining novel insight into networks at the interface of these symptom domains.

Deterioration from healthy to mild cognitive impairment and Alzheimer's disease mirrored in corresponding loss of centrality in directed brain networks

OBJECTIVE

It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer's disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks.

METHODS

Therefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (N = 132) to compare the following populations (p < 0.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network.

RESULTS

MP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC.

CONCLUSION

Our findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.

Drug Choices and Advancements for Managing Depression in Parkinson's Disease

Depression is a frequent non-motor symptom of Parkinson’s disease (PD), and may even precede the onset of motor symptoms of parkinsonism. Beyond its negative influence on mood, depression in PD is frequently associated with other neuropsychiatric symptoms and with late-stage complications such as dementia. Despite its profound impact on the quality of life and cognitive functioning in PD, depression in PD is often under-recognized and poorly treated.

Pathophysiological studies demonstrated that depression in PD is associated with global dysfunction of interactions between discrete brain areas rather than focal structural or functional abnormalities, and that it is sustained by pathological changes of several neurotransmitter/receptor complexes.

In general, all traditional antidepressants and some dopamine agonists have been found to be safe and well-tolerated to treat depressive symptoms in PD, despite initial warning on worsening of parkinsonism. Available data suggest that the time-course of response differs among antidepressants. Efficacy results from clinical trials with antidepressant in PD are, however, rather uncertain, although pooled analysis suggests a moderate benefit. Several issues may critically impact the results of clinical trials with antidepressants in PD, including the correct psychiatric diagnosis, the overlap of symptoms between depression and PD, and the selection of appropriate end-points and rating scales.

Altered Cortico-Limbic Network Connectivity in Parkinsonian Depression: The Effect of Antidepressants

BACKGROUND

Depression is a common comorbidity of Parkinson's disease (PD); however, the impact of antidepressant status on cortical function in parkinsonian depression is not fully understood. While studies of resting state functional MRI in major depression have shown that antidepressant treatment affects cortical connectivity, data on connectivity and antidepressant status in PD is sparse.

OBJECTIVE

We tested the hypothesis that cortico-limbic network (CLN) resting state connectivity is abnormal in antidepressant-treated parkinsonian depression.

METHODS

Thirteen antidepressant-treated depressed PD and 47 non-depressed PD participants from the Parkinson's Progression Markers Initiative (PPMI) database were included. Data was collected using 3T Siemens TIM Trio MR scanners and analyzed using SPM and CONN functional connectivity toolbox. Volumetric analysis was also performed using BrainSuite.

RESULTS

We found decreased connectivity in the antidepressant-treated depressed PD group when compared to non-depressed PD between the left frontal operculum and bilateral insula, and also reduced connectivity between right orbitofrontal cortex and left temporal fusiform structures. Increased depression scores were associated with decreased insular-frontal opercular connectivity. No ROI volumetric differences were found between groups.

CONCLUSION

Given the relationship between depression scores and cortico-limbic connectivity in PD, the abnormal insular-frontal opercular hypoconnectivity in this cohort may be associated with persistent depressive symptoms or antidepressant effects.

Imaging behavioural complications of Parkinson's disease

In addition to motor symptoms, behavioural complications are commonly found in patients with Parkinson's disease (PD). Behavioural complications, including depression, anxiety, apathy, impulse control disorder and psychosis, together have a large impact on PD patient's quality of life. Many neuroimaging studies using PET, SPECT and MRI techniques have been conducted to study the underlying neural mechanisms of PD pathogenesis and pathophysiology in relation to its behavioural complications. This review will survey these PET, SPECT and MRI studies to describe the current understanding of the neuro-chemical, functional and structural changes associated with behavioural complications in PD patients.

Neuroimaging depression and anxiety in essential tremor: A diffusion tensor imaging study

OBJECTIVE

Patients with essential tremor (ET) may exhibit non-motor features, including those that are neuropsychiatric. Depression and anxiety are the most common among these. This study aims to investigate the possible relationship between microstructural brain changes and symptoms of depression and anxiety in ET.

METHODS

We assessed 62 ET patients (40 women and 22 men, mean age 46.0 ± 20.4) for symptoms of depression and anxiety using the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI). Thirty-two patients had severe or moderate symptoms of anxiety, and 15 patients had severe or moderate depressive symptoms. Microstructural brain changes were evaluated using diffusion tensor imaging (DTI), which was reported using fractional anisotropy (FA), mean diffusivity (MD), apparent diffusion coefficient (ADC), radial diffusivity (RD), and axial diffusivity (AD) values calculated for 17 regions of interest including the prefrontal cortex, paralimbic and limbic structures and cerebellar peduncles. We evaluated the relationship between observed changes in brain regions and symptoms of depression and anxiety.

RESULTS

Decreased left amygdala FA (p = 0.003) and increased left amygdala RD (p = 0.04) were detected in depressed vs. non-depressed ET patients. Left ventrolateral prefrontal cortex (VLPFC) FA (p = 0.02) and left precuneus FA (p = 0.02) values differed between anxious patients vs. non-anxious ET patients. BDI scores were correlated with left amygdala FA and left RD, while BAI scores were correlated with left VLPFC FA and left precuneus FA.

DISCUSSION

Our results provide evidence that symptoms of depression and anxiety could be based in structural brain changes observed in patients with ET.

Strength and Temporal Variance of the Default Mode Network to Investigate Chronic Mild Traumatic Brain Injury in Service Members with Psychological Trauma

BACKGROUND

There is a significant number of military personnel with a history of mild traumatic brain injury (mTBI) who suffer from comorbid posttraumatic stress symptoms (PTS). Although there is evidence of disruptions of the default mode network (DMN) associated with PTS and mTBI, previous studies have only studied static connectivity while ignoring temporal variability of connectivity.

OBJECTIVE

To assess DMN disrupted or dysregulated neurocircuitry, cognitive functioning, and psychological health of active-duty military with mTBI and PTS.

METHOD

U.S. Army soldiers with PTS (n = 14), mTBI + PTS (n = 25), and healthy controls (n = 21) voluntarily completed a cognitive and symptom battery. In addition, participants had magnetic resonance imaging (MRI) to assess both static functional connectivity (SFC) and variance of dynamic functional connectivity (vDFC) of the DMN.

RESULTS

Both the PTS and mTBI + PTS groups had significant symptoms, but only the comorbid group had significant decrements in cognitive functioning. Both groups showed less stable and disrupted neural signatures of the DMN, mainly constituting the cingulate-frontal-temporal-parietal attention network. Specifically, the PTS group showed a combination of both reduced contralateral strength and reduced unilateral variability of frontal-cingulate-temporal connectivities, as well as increased variability of frontal-parietal connectivities. The mTBI + PTS group had fewer abnormal connectives than the PTS group, all of which included reduced strength of frontal-temporal regions and reduced variability frontal-cingulate-temporal regions. Greater SFC and vDFC connectivity of the left dorsolateral prefrontal cortex (dlPFC) ↔ precuneus was associated with higher cognitive scores and lower symptom scores.

CONCLUSIONS

Findings suggest that individuals with PTS and mTBI + PTS have a propensity for accentuated generation of thoughts, feelings, sensations, and/or images while in a resting state. Compared with controls, only the PTS group was associated with accentuated variability of the frontal-parietal attention network. While there were no significant differences in DMN connectivity strength between the mTBI + PTS and PTS groups, variability of connectivity was able to distinguish them.

Effectiveness of imaging genetics analysis to explain degree of depression in Parkinson's disease

Depression is one of the most common and important neuropsychiatric symptoms in Parkinson's disease and often becomes worse as Parkinson's disease progresses. However, the underlying mechanisms of depression in Parkinson's disease are not clear. The aim of our study was to find genetic features related to depression in Parkinson's disease using an imaging genetics approach and to construct an analytical model for predicting the degree of depression in Parkinson's disease. The neuroimaging and genotyping data were obtained from an openly accessible database. We computed imaging features through connectivity analysis derived from tractography of diffusion tensor imaging. The imaging features were used as intermediate phenotypes to identify genetic variants according to the imaging genetics approach. We then constructed a linear regression model using the genetic features from imaging genetics approach to describe clinical scores indicating the degree of depression. As a comparison, we constructed other models using imaging features and genetic features based on references to demonstrate the effectiveness of our imaging genetics model. The models were trained and tested in a five-fold cross-validation. The imaging genetics approach identified several brain regions and genes known to be involved in depression, with the potential to be used as meaningful biomarkers. Our proposed model using imaging genetic features predicted and explained the degree of depression in Parkinson's disease appropriately (adjusted R2 larger than 0.6 over five training folds) and with a lower error and higher correlation than with other models over five test folds.

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning

Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.

Altered brain structure in women with premenstrual syndrome

BACKGROUND

Functional brain abnormalities have been noted in premenstrual syndrome (PMS). However, the brain structural alterations related to PMS remain unclear. This study aimed to identify possible abnormalities in gray matter (GM) volumes and structural covariance patterns among PMS patients.

METHODS

Structural magnetic resonance imaging data were obtained from 20 PMS patients and 20 healthy controls. Voxel-based morphometry (VBM) analysis was applied to examine GM volumes changes between the two groups. Receiver operating characteristic (ROC) curve was used to investigate the most reliable biomarker for distinguishing PMS patients from health controls based on the intergroup differences. Correlation analysis was then performed to assess relationships between the daily rating of severity of problems (DRSP) and abnormal brain regions. Finally, the regions identified from VBM analysis were served as seeds to characterize the whole-brain structural covariance patterns.

RESULTS

Compared with healthy controls, PMS patients showed increased GM volumes in the precuneus/posterior cingulate cortex (precuneus/PCC) and thalamus, and decreased GM volumes in the insula. The precuneus/PCC exhibited the highest classification power by ROC analysis and positively correlated with the DRSP. Moreover, different patterns of structural covariance in the two groups were mainly located in the dorsolateral prefrontal cortex, anterior cingulate cortex, angular gyrus and hippocampus.

LIMITATIONS

This study is limited by a small sample and narrow age range of participants.

CONCLUSIONS

Our findings may provide preliminary evidence for brain morphology alterations in PMS patients and contribute to a better understanding of the pathophysiology of PMS.

Altered resting-state voxel-level whole-brain functional connectivity in depressed Parkinson's disease

BACKGROUND

Depression is one of the most common non-motor symptoms in Parkinson's disease (PD), but its pathogenesis is still not very clear. Recently, degree centrality, a voxel-level whole-brain functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging, has provided the most promising way to explore the neural network mechanisms underlying depressed PD.

METHODS

Degree centrality, voxel-wise image and clinical symptoms correlation and secondary seed-based FC analyses were performed in twenty-seven drug-naïve, early stage depressed PD patients, 27 non-depressed PD patients and 27 healthy controls (HCs) to reveal voxel-level whole-brain FC changes in depressed PD.

RESULTS

Compared with the HCs, depressed PD and non-depressed PD patients shared similar brain degree centrality abnormalities mainly in the basal ganglia, insular cortex, motor cortices, default mode network, prefrontal gyrus and the cerebellum. However, compared with non-depressed PD, depressed PD showed degree centrality abnormalities in the right middle prefrontal gyrus, anterior cingulate cortices, supplementary motor cortices and cerebellum lobule VI. The right middle prefrontal gyrus degree centrality abnormalities were correlated with the clinical depression severity, and using it as a seed, a secondary seed-based FC analysis further revealed the FC changes in the anterior cingulate cortices and the cerebellum lobule VI.

CONCLUSIONS

Our findings revealed that dysfunction in extensive brain areas were involved in depressed PD, and among these regions, the right middle prefrontal gyrus, anterior cingulate cortices and the cerebellum may pose as pathogenesis hubs underlying depressed PD.

The functional and structural characteristics of the emotion network in alexithymia

BACKGROUND

Alexithymia is a multifaceted personality trait characterized by emotional dysfunction.

METHODS

In this study, the functional and structural features of the emotion network in alexithymia were investigated using resting-state functional MRI (rsfMRI), voxel-based morphometry (VBM), functional connectivity (FC) analysis, and diffusion tensor imaging (DTI). Alexithymic and non-alexithymic students were recruited from the local university. The intrinsic neural activity and gray matter density of the brain regions in the emotion network were measured using rsfMRI and VBM; the FC and structural connectivity of the brain regions in the emotion network were measured using FC analysis and DTI.

RESULTS

The altered intrinsic neural activity in V1, rostral dorsal anterior cingulate cortex, and left amygdala, and the weak FC between V1 and left superior temporal gyrus and V1 and left paracentral lobule in alexithymia subjects were identified. However, no alteration of the structure and structural connectivity of the emotion network was identified.

CONCLUSION

The results indicated that the development of alexithymia might have been caused only by slight alteration of the neural activity. Furthermore, the results suggest that noninvasive treatment technologies for improving the brain activity are suitable for alexithymic individuals.

Separate neural representations of depression, anxiety and apathy in Parkinson's disease

Depression, anxiety and apathy are distinct neuropsychiatric symptoms that highly overlap in Parkinson’s disease (PD). It remains unknown whether each symptom is uniquely associated with a functional network dysfunction. Here, we examined whether individual differences in each neuropsychiatric symptom predict functional connectivity patterns in PD patients while controlling for all other symptoms and motor function. Resting-state functional connectivity MRI were acquired from 27 PD patients and 29 healthy controls. Widespread reduced functional connectivity was identified in PD patients and explained by either the neuropsychiatric or motor symptoms. Depression in PD predicted increased functional connectivity between the orbitofrontal, hippocampal complex, cingulate, caudate and thalamus. Apathy in PD predicted decreased caudate-thalamus and orbitofrontal-parahippocampal connectivity. Anxiety in PD predicted three distinct types of functional connectivity not described before: (i) increased limbic-orbitofrontal cortex; (ii) decreased limbic-dorsolateral prefrontal cortex and orbitofrontal-dorsolateral prefrontal cortices and (iii) decreased sensorimotor-orbitofrontal cortices. The first two types of functional connectivity suggest less voluntary and more automatic emotion regulation. The last type is argued to be specific to PD and reflect an impaired ability of the orbitofrontal cortex to guide goal-directed motor actions in anxious PD patients.

Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson's Disease

In this study, a new combination scheme has been proposed for detecting Parkinson's disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages. In the first stage, the DWT was applied to acquire the split frequency information; here, we use three-level DWT to decompose EEG signal into approximation and detail coefficients; in this stage, we aim to remove the useless and noise information and acquire the effective information. In the second stage, as the SampEn has advantage in analyzing the EEG signal, we use the approximation coefficient to compute the SampEn values. Finally, we detect the PD patients using three-way decision based on optimal center constructive covering algorithm (O_CCA) with the accuracy about 92.86%. Without DWT as preprocessing step, the detection rate reduces to 88.10%. Overall, the combination scheme we proposed is suitable and efficient in analyzing the EEG signal with higher accuracy.