Apathy, depression, and motor symptoms have distinct and separable resting activity patterns in idiopathic Parkinson disease

A full-brain, bootstrapped analysis of diffusion tensor imaging robustly differentiates Parkinson disease from healthy controls

There is a compelling need for early, accurate diagnosis of Parkinson's disease (PD). Various magnetic resonance imaging modalities are being explored as an adjunct to diagnosis. A significant challenge in using MR imaging for diagnosis is developing appropriate algorithms for extracting diagnostically relevant information from brain images. In previous work, we have demonstrated that individual subject variability can have a substantial effect on identifying and determining the borders of regions of analysis, and that this variability may impact on prediction accuracy. In this paper we evaluate a new statistical algorithm to determine if we can improve accuracy of prediction using a subjects left-out validation of a DTI analysis. Twenty subjects with PD and 22 healthy controls were imaged to evaluate if a full brain diffusion tensor imaging-fractional anisotropy (DTI-FA) map might be capable of segregating PD from controls. In this paper, we present a new statistical algorithm based on bootstrapping. We compare the capacity of this algorithm to classify the identity of subjects left out of the analysis with the accuracy of other statistical techniques, including standard cluster-thresholding. The bootstrapped analysis approach was able to correctly discriminate the 20 subjects with PD from the 22 healthy controls (area under the receiver operator curve or AUROC 0.90); however the sensitivity and specificity of standard cluster-thresholding techniques at various voxel-specific thresholds were less effective (AUROC 0.72-0.75). Based on these results sufficient information to generate diagnostically relevant statistical maps may already be collected by current MRI scanners. We present one statistical technique that might be used to extract diagnostically relevant information from a full brain analysis.

Frequency-dependent neural activity in Parkinson's disease

The brainstem and basal ganglia are important in the pathophysiology of Parkinson's disease (PD). Reliable and sensitive detection of neural activity changes in these regions should be helpful in scientific and clinical research on PD. In this study, we used resting state functional MRI and amplitude of low frequency fluctuation (ALFF) methods to examine spontaneous neural activity in 109 patients with PD. We examined activity in two frequency bands, slow-4 (between 0.027 and 0.073 Hz) and slow-5 (0.010-0.027 Hz). Patients had decreased ALFF in the striatum and increased ALFF in the midbrain, and changes were more significant in slow-4. Additionally, changes in slow-4 in both basal ganglia and midbrain correlated with the severity of the parkinsonism. The ALFF in the caudate nucleus positively correlated with the dose of levodopa, while the ALFF in the putamen negatively correlated with the disease duration in both slow-4 and slow-5 bands. In addition, the ALFF in the rostral supplementary motor area negatively correlated with bradykinesia subscale scores. Our findings show that with a large cohort of patients and distinguishing frequency bands, neural modulations in the brainstem and striatum in PD can be detected and may have clinical relevance. The physiological interpretation of these changes needs to be determined.

What light have resting state fMRI studies shed on cognition and mood in Parkinson's disease?

Much remains unknown about non-motor symptoms of Parkinson’s disease (PD), which have variable occurrence, progression, and severity among patients. The existing suite of neuroimaging tools has yielded insight that cannot be garnered by traditional methods such as behavioral and post-mortem assessment. They provide information on brain activity and structure that is invaluable to understanding abnormalities associated with neurodegeneration in PD. Among these tools, functional magnetic resonance imaging (fMRI) is often favored for its safety and spatial resolution. Resting state fMRI research capitalizes on the wealth of information that the brain offers when a person is not performing a motor or cognitive task. It is also a good means to study impaired and heterogeneous populations, such as people with PD. The present article reviews research that applies resting state fMRI to the ongoing hunt for biomarkers of PD non-motor symptoms. Thus far, research in this subfield has focused on two of the most common and significant non-motor symptoms: cognitive impairment and depression. These studies support resting state fMRI as a valid and practical tool for the study of these symptoms, but discrepancies among findings highlight the importance of further research with standardized procedures.

Apathy and impaired emotional facial recognition networks overlap in Parkinson's disease: a PET study with conjunction analyses

Apathy is a disabling non-motor symptom that is frequently observed in Parkinson's disease (PD). Its description and physiopathology suggest that it is partially mediated by emotional impairment, but this research issue has never been addressed at a clinical and metabolic level. We therefore conducted a metabolic study using (18)fluorodeoxyglucose positron emission tomography ((18)FDG PET) in 36 PD patients without depression and dementia. Apathy was assessed on the Apathy Evaluation Scale (AES), and emotional facial recognition (EFR) performances (ie, percentage of correct responses) were calculated for each patient. Confounding factors such as age, antiparkinsonian and antidepressant medication, global cognitive functions and depressive symptoms were controlled for. We found a significant negative correlation between AES scores and performances on the EFR task. The apathy network was characterised by increased metabolism within the left posterior cingulate (PC) cortex (Brodmann area (BA) 31). The impaired EFR network was characterised by decreased metabolism within the bilateral PC gyrus (BA 31), right superior frontal gyrus (BAs 10, 9 and 6) and left superior frontal gyrus (BA 10 and 11). By applying conjunction analyses to both networks, we identified the right premotor cortex (BA 6), right orbitofrontal cortex (BA 10), left middle frontal gyrus (BA 8) and left posterior cingulate gyrus (BA 31) as the structures supporting the association between apathy and impaired EFR. These results confirm that apathy in PD is partially mediated by impaired EFR, opening up new prospects for alleviating apathy in PD, such as emotional rehabilitation.

A review of the use of magnetic resonance imaging in Parkinson's disease

To date, the most frequently used Parkinson's disease (PD) biomarkers are the brain imaging measures of dopaminergic dysfunction using positron emission tomography and single photon emission computed tomography. However, major advances have occurred in the development of magnetic resonance imaging (MRI) biomarkers for PD in the past decade. Although conventional structural imaging remains normal in PD, advanced techniques have shown changes in the substantia nigra and the cortex. The most well-developed MRI markers in PD include diffusion imaging and iron load using T2/T2* relaxometry techniques. Other quantitative biomarkers such as susceptibility-weighted imaging for iron load, magnetization transfer and ultra-high-field MRI have shown great potential. More sophisticated techniques such as tractography and resting state functional connectivity give access to anatomical and functional connectivity changes in the brain, respectively. Brain perfusion can be assessed using non-contrast-agent techniques such as arterial spin labelling and spectroscopy gives access to metabolites concentrations. However, to date these techniques are not yet fully validated and standardized quantitative metrics for PD are still lacking. This review presents an overview of new structural, perfusion, metabolic and anatomo-functional connectivity biomarkers, their use in PD and their potential applications to improve the clinical diagnosis of Parkinsonian syndromes and the quality of clinical trials.

A multi-methodological MR resting state network analysis to assess the changes in brain physiology of children with ADHD

The purpose of this work was to highlight the neurological differences between the MR resting state networks of a group of children with ADHD (pre-treatment) and an age-matched healthy group. Results were obtained using different image analysis techniques. A sample of n = 46 children with ages between 6 and 12 years were included in this study (23 per cohort). Resting state image analysis was performed using ReHo, ALFF and ICA techniques. ReHo and ICA represent connectivity analyses calculated with different mathematical approaches. ALFF represents an indirect measurement of brain activity. The ReHo and ICA analyses suggested differences between the two groups, while the ALFF analysis did not. The ReHo and ALFF analyses presented differences with respect to the results previously reported in the literature. ICA analysis showed that the same resting state networks that appear in healthy volunteers of adult age were obtained for both groups. In contrast, these networks were not identical when comparing the healthy and ADHD groups. These differences affected areas for all the networks except the Right Memory Function network. All techniques employed in this study were used to monitor different cerebral regions which participate in the phenomenological characterization of ADHD patients when compared to healthy controls. Results from our three analyses indicated that the cerebellum and mid-frontal lobe bilaterally for ReHo, the executive function regions in ICA, and the precuneus, cuneus and the clacarine fissure for ALFF, were the “hubs” in which the main inter-group differences were found. These results do not just help to explain the physiology underlying the disorder but open the door to future uses of these methodologies to monitor and evaluate patients with ADHD.

Resting-state fMRI study on drug-naive patients with Parkinson's disease and with depression

OBJECTIVE

This study used resting-state functional MRI (fMRI) to evaluate regional and network alterations in patients with Parkinson's disease (PD) with and without depression.

METHOD

We recruited 29 patients with PD with depression (PD-Dep), 30 patients with PD without depression (PD-NDep), and 30 normal controls. All participants underwent resting-state fMRI scans on a 3-T MR system. The amplitude of low-frequency fluctuation (ALFF) of blood oxygen level-dependent signals was used to characterise regional cerebral function. Functional integration of the brain network was evaluated by seed-based correlation approach.

RESULTS

The PD-Dep group showed significantly higher ALFF value in the left orbitofrontal area compared with both the PD-NDep and control groups (p<0.05 corrected by FWE). In patients with PD, the Hamilton Depression Rating Scale score was positively correlated with the ALFF value in the left orbitofrontal cortex (p<0.005 uncorrected). Brain network connectivity analysis revealed reduced functional connectivity of putamen in both PD subgroups. However, the PD-Dep group showed more distributed reduced connectivity in the prefrontal-limbic network than the PD-NDep group did (p<0.05 corrected by FWE).

CONCLUSIONS

Our study demonstrates that PD-Dep patients are characterised by increased regional spontaneous neural activity in the orbitofrontal area and decreased functional integration within the prefrontal-limbic network. These findings may be helpful for facilitating further understanding of the potential mechanisms underlying depression in PD.

Distinct neuropsychological correlates of cognitive, behavioral, and affective apathy sub-domains in acquired brain injury

Apathy has a high prevalence and a significant contribution to treatment and rehabilitation outcomes in acquired brain damage. Research on the disorder's neuropsychological correlates has produced mixed results. While the mixed picture may be due to the use of varied assessment tools on different patient populations, it is also the case that most studies treat apathy as a unitary syndrome. This is despite the evidence that apathy is a multifaceted and multidimensional syndrome. This study investigates the neuropsychological correlates of apathy in 49 patients with acquired brain damage. It further fractionates apathy symptoms into affective, cognitive, and behavioral sub-domains and investigates their individual relations with standard measures of affective, cognitive, and behavioral functioning. Global apathy scores were not related to any of these measures. Affective apathy was associated with emotion perception deficits, and cognitive apathy was associated with executive deficits on the Brixton test. These results demonstrate that treating apathy as a single entity may hide important correlates to apathy symptoms that become visible when the disorder is fractionated into its sub-domains. The study highlights the research and clinical importance of treating apathy as a multidimensional syndrome.

Affective disorders in Parkinson's disease

PURPOSE OF REVIEW

This review explores recent literature pertaining to affective disorders associated with Parkinson's disease.

RECENT FINDINGS

Nonmotor symptoms including affective disorders are becoming more widely recognized as complications of Parkinson's disease. As awareness of these symptoms increases, and new neuroimaging tools are developed and become more accessible, more studies are being conducted pertaining to behavioral complications in Parkinson's disease. The functional connectivity of the basal ganglia can predispose people with Parkinson's to develop affective disorders. Furthermore, dopaminergic treatments may exacerbate or trigger behavioral symptoms. It is now understood that changes associated with Parkinson's disease are widespread, affecting striatal and extrastriatal regions and resulting in alterations in gray matter, white matter, blood flow, metabolism, and dopaminergic and serotonergic function.

SUMMARY

Neuroimaging is advancing our knowledge of the mechanisms involved in Parkinson's disease, and their role in the development of behavioral disorders. An increased understanding of these disorders may lead to the discovery of new therapeutic targets, or the identification of risk factors for the development of these disorders. If preventive therapies become available, identification of risk factors will be important for the identification and treatment of susceptible individuals.

Depression and impulse control disorders in Parkinson's disease: two sides of the same coin?

Depression and impulse control disorders (ICD) are two common neuropsychiatric features in Parkinson's disease (PD). Studies have revealed that both phenomena are associated with aberrations in ventral striatal dopamine signaling and concomitant dysfunction of the reward-related (limbic) cortico-striatal-thalamocortical (CSTC) circuit. Depression in PD seems associated with decreased activity in the limbic CSTC circuit, whereas ICD seem associated with increased limbic CSTC circuit activity, usually after commencing dopamine replacement therapy (DRT). Not all DRT using PD patients, however, develop symptoms of ICD, suggesting an additional underlying neurobiological susceptibility. Furthermore, the symptoms of depression and ICD frequently coincide even though they are related to seemingly contrasting limbic CSTC circuit activation states. The aim of this review is to provide an overview of the currently available literature on the neurobiology of PD-related depression and ICD and discusses possible susceptibility factors. Finally, we propose a neurobiological model that identifies ventral striatal dopaminergic denervation as a common underlying neurobiological substrate of depression and ICD and subsequent dysfunction of reward and motivation-related brain areas.

Abnormal baseline brain activity in non-depressed Parkinson's disease and depressed Parkinson's disease: a resting-state functional magnetic resonance imaging study

Depression is the most common psychiatric disorder observed in Parkinson's disease (PD) patients, however the neural contribution to the high rate of depression in the PD group is still unclear. In this study, we used resting-state functional magnetic resonance imaging (fMRI) to investigate the underlying neural mechanisms of depression in PD patients. Twenty-one healthy individuals and thirty-three patients with idiopathic PD, seventeen of whom were diagnosed with major depressive disorder, were recruited. An analysis of amplitude of low-frequency fluctuations (ALFF) was performed on the whole brain of all subjects. Our results showed that depressed PD patients had significantly decreased ALFF in the dorsolateral prefrontal cortex (DLPFC), the ventromedial prefrontal cortex (vMPFC) and the rostral anterior cingulated cortex (rACC) compared with non-depressed PD patients. A significant positive correlation was found between Hamilton Depression Rating Scale (HDRS) and ALFF in the DLPFC. The findings of changed ALFF in these brain regions implied depression in PD patients may be associated with abnormal activities of prefrontal-limbic network.

Altered fronto-striatal and fronto-cerebellar circuits in heroin-dependent individuals: a resting-state FMRI study

BACKGROUND

The formation of compulsive pattern of drug use is related to abnormal regional neural activity and functional reorganization in the heroin addicts' brain, but the relationship between heroin-use-induced disrupted local neural activity and its functional organization pattern in resting-state is unknown.

METHODOLOGY/PRINCIPAL FINDINGS

With fMRI data acquired during resting state from 17 male heroin dependent individuals (HD) and 15 matched normal controls (NC), we analyzed the changes of amplitude of low frequency fluctuation (ALFF) in brain areas, and its relationship with history of heroin use. Then we investigated the addiction related alteration in functional connectivity of the brain regions with changed ALFF using seed-based correlation analysis. Compared with NC, the ALFF of HD was obviously decreased in the right caudate, right dorsal anterior cingulate cortex (dACC), right superior medial frontal cortex and increased in the bilateral cerebellum, left superior temporal gyrus and left superior occipital gyrus. Of the six regions, only the ALFF value of right caudate had a negative correlation with heroin use. Setting the six regions as "seeds", we found the functional connectivity between the right caudate and dorsolateral prefrontal cortex (dlPFC) was reduced but that between the right caudate and cerebellum was enhanced. Besides, an abnormal lateral PFC-dACC connection was also observed in HD.

CONCLUSIONS

The observations of dysfunction of fronto-striatal and fronto-cerebellar circuit in HD implicate an altered balance between local neuronal assemblies activity and their integrated network organization pattern which may be involved in the process from voluntary to habitual and compulsive drug use.

Automatic classification of early Parkinson's disease with multi-modal MR imaging

Background

In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals.

Materials and Methods

Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels: ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method.

Results

Using the proposed methods to classify the data set, good results (accuracy  = 86.96%, sensitivity  = 78.95%, specificity  = 92.59%) were obtained.

Conclusions

This method demonstrates a promising diagnosis performance by the integration of information from a variety of imaging modalities, and it shows potential for improving the clinical diagnosis and treatment of PD.

[Apathy neural bases in neurodegenerative disorders]

Apathy is widely recognized as a lack of motivation, which expresses through the cognitive, behavioral and emotional dimensions of living. It is described within several neuropsychiatric syndromes such as degenerative disorder and is associated with poorer outcomes. In order to better understand the underpinnings of apathy and to develop specific treatment strategies, much research has been conducted to define its neural bases. In the present review, perfusion, metabolic, pathologic and functional results of apathy neural bases in Alzheimer's and Parkinson's diseases are displayed. Methods and strategies to control for confounding factors such as depression, cognitive impairments and other behavioral disorders are described. Results are not strictly identical between disorders and even within disorders. Variation of methods employed on assessment tools and control for confounding factors such as cognitive disorders, depression, other behavioral disorders and medical treatment is thought to be the main reason for this discrepancy. However, it seems that the inferior prefrontal cortex, especially the orbitofrontal cortex, the lateral prefrontal cortex and the anterior cingulate are of particular interest. The second part of the review discusses the literature in these three areas in conditional learning essentially via the reward characteristic encoding, auto-initiated and perseverance behaviors and emotional experience and its regulation.

L-DOPA changes spontaneous low-frequency BOLD signal oscillations in Parkinson's disease: a resting state fMRI study

Analysis of the amplitude of low frequency BOLD signal fluctuations (ALFF) in the resting state has recently been used to study the dynamics of intrinsic neural activity. Several studies have also suggested its potential as a biomarker for neuropsychiatric disease. In the current study, we quantified ALFF to determine changes in intrinsic neural oscillations in patients with Parkinson's disease (PD) on and off L-DOPA. Twenty-four PD patients and 24 healthy age-matched controls participated in the study. PD patients underwent two resting state fMRI sessions, either ON a controlled dose of L-DOPA or following a placebo pill (OFF). Control participants underwent one test session. We found that there was increased amplitude of low frequency BOLD signal oscillations for PD patients OFF L-DOPA in the primary and secondary motor areas, and in the middle and medial prefrontal cortices. L-DOPA significantly reduced the amplitude of low frequency oscillations within these regions. The degree of ALFF in the premotor cortex predicted patients' motor performance as measured by the Grooved Pegboard task, such that greater ALFF was associated with poorer performance. These results are in line with the pathophysiology of PD, which shows changes in neural oscillations. Thus, frequency domain analyses of resting state BOLD fMRI signals may provide a useful means to study the pathophysiology of PD and the physiology of the brain's dopaminergic pathways.

Toys and gadgets: construct validity of apathy in Parkinson's disease

Apathy is one of the primary neuropsychiatric signatures in Parkinson's disease, yet little research has addressed the construct validity of two commonly used apathy measures, the Apathy Scale and the Lille Apathy Rating Scale. The authors tested the hypothesis that apathy is associated with reduced initiative/engaged behaviors on a laboratory-based measure of apathy. Support was found for the hypothesis that apathy, as indexed by the Apathy Scale and the Lille Apathy Rating Scale, is associated with reduced initiative/engagement on an experimental measure of apathy in Parkinson's disease patients. These findings provide independent evidence for the construct validity of self-report apathy scales, beyond clinician judgment.