Reliability analysis of the resting state can sensitively and specifically identify the presence of Parkinson disease

Functional Connectivity Differences of the Subthalamic Nucleus Related to Parkinson's Disease

A typical feature of Parkinson's disease (PD) is pathological activity in the subthalamic nucleus (STN). Here, we tested whether in patients with PD under dopaminergic treatment functional connectivity of the STN differs from healthy controls (HC) and whether some brain regions show (anti-) correlations between functional connectivity with STN and motor symptoms. We used functional magnetic resonance imaging to investigate whole-brain resting-state functional connectivity with STN in 54 patients with PD and 55 HC matched for age, gender, and within-scanner motion. Compared to HC, we found attenuated negative STN-coupling with Crus I of the right cerebellum and with right ventromedial prefrontal regions in patients with PD. Furthermore, we observed enhanced negative STN-coupling with bilateral intraparietal sulcus/superior parietal cortex, right sensorimotor, right premotor, and left visual cortex compared to HC. Finally, we found a decline in positive STN-coupling with the left insula related to severity of motor symptoms and a decline of inter-hemispheric functional connectivity between left and right STN with progression of PD-related motor symptoms. Motor symptom related uncoupling of the insula, a key region in the saliency network and for executive function, from the STN might be associated with well-known executive dysfunction in PD. Moreover, uncoupling between insula and STN might also induce an insufficient setting of thresholds for the discrimination between relevant and irrelevant salient environmental stimuli, explaining observations of disturbed response control in PD. In sum, motor symptoms in PD are associated with a reduced coupling between STN and a key region for executive function.

Neuroimaging of Parkinson's disease: Expanding views

Advances in molecular and structural and functional neuroimaging are rapidly expanding the complexity of neurobiological understanding of Parkinson's disease (PD). This review article begins with an introduction to PD neurobiology as a foundation for interpreting neuroimaging findings that may further lead to more integrated and comprehensive understanding of PD. Diverse areas of PD neuroimaging are then reviewed and summarized, including positron emission tomography, single photon emission computed tomography, magnetic resonance spectroscopy and imaging, transcranial sonography, magnetoencephalography, and multimodal imaging, with focus on human studies published over the last five years. These included studies on differential diagnosis, co-morbidity, genetic and prodromal PD, and treatments from L-DOPA to brain stimulation approaches, transplantation and gene therapies. Overall, neuroimaging has shown that PD is a neurodegenerative disorder involving many neurotransmitters, brain regions, structural and functional connections, and neurocognitive systems. A broad neurobiological understanding of PD will be essential for translational efforts to develop better treatments and preventive strategies. Many questions remain and we conclude with some suggestions for future directions of neuroimaging of PD.

Parkinson's disease-related spatial covariance pattern identified with resting-state functional MRI

In this study, we sought to identify a disease-related spatial covariance pattern of spontaneous neural activity in Parkinson's disease using resting-state functional magnetic resonance imaging (MRI). Time-series data were acquired in 58 patients with early to moderate stage Parkinson's disease and 54 healthy controls, and analyzed by Scaled Subprofile Model Principal Component Analysis toolbox. A split-sample analysis was also performed in a derivation sample of 28 patients and 28 control subjects and validated in a prospective testing sample of 30 patients and 26 control subjects. The topographic pattern of neural activity in Parkinson's disease was characterized by decreased activity in the striatum, supplementary motor area, middle frontal gyrus, and occipital cortex, and increased activity in the thalamus, cerebellum, precuneus, superior parietal lobule, and temporal cortex. Pattern expression was elevated in the patients compared with the controls, with a high accuracy (90%) to discriminate the patients from the controls. The split-sample analysis produced a similar pattern but with a lower accuracy for group discrimination in both the derivation (80%) and the validation (73%) samples. Our results showed that resting-state functional MRI can be potentially useful for identification of Parkinson's disease-related spatial covariance patterns, and for differentiation of Parkinson's disease patients from healthy controls at an individual level.

Motor loop dysfunction causes impaired cognitive sequencing in patients suffering from Parkinson's disease

Cognitive impairment in Parkinson's disease (PD) is often attributed to dopamine deficiency in the prefrontal-basal ganglia-thalamo-cortical loops. Although recent studies point to a close interplay between motor and cognitive abilities in PD, the so-called "motor loop" connecting supplementary motor area (SMA) and putamen has been considered solely with regard to the patients' motor impairment. Our study challenges this view by testing patients with the serial prediction task (SPT), a cognitive task that requires participants to predict stimulus sequences and particularly engages premotor sites of the motor loop. We hypothesised that affection of the motor loop causes impaired SPT performance, especially when the internal sequence representation is challenged by suspension of external stimuli. As shown for motor tasks, we further expected this impairment to be compensated by hyperactivity of the lateral premotor cortex (PM). We tested 16 male PD patients ON and OFF dopaminergic medication and 16 male age-matched healthy controls in an functional Magnetic Resonance Imaging study. All subjects performed two versions of the SPT: one with on-going sequences (SPT0), and one with sequences containing non-informative wildcards (SPT+) increasing the demands on mnemonic sequence representation. Patients ON (compared to controls) revealed an impaired performance coming along with hypoactivity of SMA and putamen. Patients OFF compared to ON medication, while showing poorer performance, exhibited a significantly increased PM activity for SPT+ vs. SPT0. Furthermore, patients' performance positively co-varied with PM activity, corroborating a compensatory account. Our data reveal a contribution of the motor loop to cognitive impairment in PD, and suggest a close interplay of SMA and PM beyond motor control.

Altered Resting-State Brain Activity and Connectivity in Depressed Parkinson's Disease

Depressive symptoms are common in Parkinson's disease (PD), but the neurophysiological mechanisms of depression in PD are poorly understood. The current study attempted to examine disrupted spontaneous local brain activities and functional connectivities that underlie the depression in PD. We recruited a total of 20 depressed PD patients (DPD), 40 non-depressed PD patients (NDPD) and 43 matched healthy controls (HC). All the subjects underwent neuropsychological tests and resting-state fMRI scanning. The between-group differences in the amplitude of low frequency fluctuations (ALFF) of BOLD signals were examined using post-hoc tests after the analysis of covariance. Compared with the NDPD and HC, the DPD group showed significantly increased ALFF in the left median cingulated cortex (MCC). The functional connectivity (FC) between left MCC and all the other voxels in the brain were then calculated. Compared with the HC and NDPD group, the DPD patients showed stronger FC between the left MCC and some of the major nodes of the default mode network (DMN), including the post cingulated cortex/precuneus, medial prefrontal cortex, inferior frontal gyrus, and cerebellum. Correlation analysis revealed that both the ALFF values in the left MCC and the FC between the left MCC and the nodes of DMN were significantly correlated with the Hamilton Depression Rating Scale score. Moreover, higher local activities in the left MCC were associated with increased functional connections between the MCC and the nodes of DMN in PD. These abnormal activities and connectivities of the limbic-cortical circuit may indicate impaired high-order cortical control or uncontrol of negative mood in DPD, which suggested a possible neural mechanism of the depression in PD.

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.

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.

Reduced topological efficiency in cortical-basal Ganglia motor network of Parkinson's disease: a resting state fMRI study

Parkinson's disease (PD) is mainly characterized by dopamine depletion of the cortico-basal ganglia (CBG) motor circuit. Given that dopamine dysfunction could affect functional brain network efficiency, the present study utilized resting-state fMRI (rs-fMRI) and graph theoretical approach to investigate the topological efficiency changes of the CBG motor network in patients with PD during a relatively hypodopaminergic state (12 hours after a last dose of dopamimetic treatment). We found that PD compared with controls had remarkable decreased efficiency in the CBG motor network, with the most pronounced changes observed in rostral supplementary motor area (pre-SMA), caudal SMA (SMA-proper), primary motor cortex (M1), primary somatosensory cortex (S1), thalamus (THA), globus pallidus (GP), and putamen (PUT). Furthermore, reduced efficiency in pre-SMA, M1, THA and GP was significantly correlated with Unified Parkinson's Disease Rating Scale (UPDRS) motor scores in PD patients. Together, our results demonstrate that individuals with PD appear to be less effective at information transfer within the CBG motor pathway, which provides a novel perspective on neurobiological explanation for the motor symptoms in patients. These findings are in line with the pathophysiology of PD, suggesting that network efficiency metrics may be used to identify and track the pathology of PD.

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.

Ventral tegmental area/midbrain functional connectivity and response to antipsychotic medication in schizophrenia

Medication management in schizophrenia is a lengthy process, as the lack of clinical response can only be confirmed after at least 4 weeks of antipsychotic treatment at a therapeutic dose. Thus, there is a clear need for the discovery of biomarkers that have the potential to accelerate the management of treatment. Using resting-state functional MRI, we examined the functional connectivity of the ventral tegmental area (VTA), the origin of the mesocorticolimbic dopamine projections, in 21 healthy controls and 21 unmedicated patients with schizophrenia at baseline (pre-treatment) and after 1 week of treatment with the antipsychotic drug risperidone (1-week post-treatment). Group-level functional connectivity maps were obtained and group differences in connectivity were assessed on the groups' participant-level functional connectivity maps. We also examined the relationship between pre-treatment/1-week post-treatment functional connectivity and treatment response. Compared with controls, patients exhibited significantly reduced pre-treatment VTA/midbrain connectivity to multiple cortical and subcortical regions, including the dorsal anterior cingulate cortex (dACC) and thalamus. After 1 week of treatment, VTA/midbrain connectivity to bilateral regions of the thalamus was re-established. Pre-treatment VTA/midbrain connectivity strength to dACC was positively correlated with good response to a 6-week course of risperidone, whereas pre-treatment VTA/midbrain connectivity strength to the default mode network was negatively correlated. Our findings suggest that VTA/midbrain resting-state connectivity may be a useful biomarker for the prediction of treatment response.

Rivastigmine is associated with restoration of left frontal brain activity in Parkinson's disease

The objective of this study was to investigate how acetylcholinesterase inhibitor (ChEI) treatment affects brain function in Parkinson's disease (PD). Twelve patients with PD and either dementia or mild cognitive impairment underwent task-free functional magnetic resonance imaging before and after 3 months of ChEI treatment and were compared with 15 age- and sex-matched neurologically healthy controls. Regional spontaneous brain activity was measured using the fractional amplitude of low-frequency fluctuations. At baseline, patients showed reduced spontaneous brain activity in regions important for motor control (eg, caudate, supplementary motor area, precentral gyrus, thalamus), attention and executive functions (eg, lateral prefrontal cortex), and episodic memory (eg, precuneus, angular gyrus, hippocampus). After treatment, the patients showed a similar but less extensive pattern of reduced spontaneous brain activity relative to controls. Spontaneous brain activity deficits in the left premotor cortex, inferior frontal gyrus, and supplementary motor area were restored such that the activity was increased posttreatment compared with baseline and was no longer different from controls. Treatment-related increases in left premotor and inferior frontal cortex spontaneous brain activity correlated with parallel reaction time improvement on a test of controlled attention. PD patients with cognitive impairment show numerous regions of decreased spontaneous brain function compared with controls, and rivastigmine is associated with performance-related normalization in the left frontal cortex function.

Dopamine replacement modulates oscillatory coupling between premotor and motor cortical areas in Parkinson's disease

Efficient neural communication between premotor and motor cortical areas is critical for manual motor control. Here, we used high-density electroencephalography to study cortical connectivity in patients with Parkinson's disease (PD) and age-matched healthy controls while they performed repetitive movements of the right index finger at maximal repetition rate. Multiple source beamformer analysis and dynamic causal modeling were used to assess oscillatory coupling between the lateral premotor cortex (lPM), supplementary motor area (SMA), and primary motor cortex (M1) in the contralateral hemisphere. Elderly healthy controls showed task-related modulation in connections from lPM to SMA and M1, mainly within the γ-band (>30 Hz). Nonmedicated PD patients also showed task-related γ-γ coupling from lPM to M1, but γ coupling from lPM to SMA was absent. Levodopa reinstated physiological γ-γ coupling from lPM to SMA and significantly strengthened coupling in the feedback connection from M1 to lPM expressed as β-β as well as θ-β coupling. Enhancement in cross-frequency θ-β coupling from M1 to lPM was correlated with levodopa-induced improvement in motor function. The results show that PD is associated with an altered neural communication between premotor and motor cortical areas, which can be modulated by dopamine replacement.

Specific frequency band of amplitude low-frequency fluctuation predicts Parkinson's disease

Resting-state functional magnetic resonance imaging (RS-fMRI) has been considered for development as a biomarker and analytical tool for evaluation of Parkinson's disease (PD). Here we utilized analysis of the amplitude low-frequency fluctuations (ALFF) to determine changes in intrinsic neural oscillations in 72 patients with PD. Two different frequency bands (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz) were analyzed. In the slow-5 band, PD patients compared with controls had increased ALFF values mainly in the caudate and several temporal regions, as well as decreased ALFF values in the cerebellum and the parieto-temporo-occipital cortex. Additionally, in the slow-4 band, PD patients relative to controls exhibited reduced ALFF value in the thalamus, cerebellum, and several occipital regions. Together, our data demonstrate that PD patients have widespread abnormal intrinsic neural oscillations in the corticostriatal network in line with the pathophysiology of PD, and further suggest that the abnormalities are dependent on specific frequency bands. Thus, frequency domain analyses of resting state BOLD signals may provide a useful means to study the pathophysiology of PD and the physiology of the brain's dopaminergic pathways.

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.

Rhythm-specific modulation of the sensorimotor network in drug-naive patients with Parkinson's disease by levodopa

Brain activity during rest is characterized by slow (0.01-0.1 Hz) fluctuations of blood oxygenation level-dependent functional magnetic resonance imaging signals. These fluctuations are organized as functional connectivity networks called resting-state networks, anatomically corresponding to specific neuronal circuits. As Parkinson's disease is mainly characterized by a dysfunction of the sensorimotor pathways, which can be influenced by levodopa administration, the present study investigated the functional connectivity changes within the sensorimotor resting-state network in drug-naïve patients with Parkinson's disease after acute levodopa administration. Using a double-blind placebo-controlled design, resting-state functional magnetic resonance imaging was carried out in 20 drug-naïve patients with Parkinson's disease, immediately before and 60 min after, oral administration of either levodopa or placebo. Control resting-state functional magnetic resonance imaging data were recorded in 18 age- and sex-matched healthy volunteers. Independent component analysis was performed to extract resting-state network maps and associated time-course spectral features. At the anatomical level, levodopa enhanced the sensorimotor network functional connectivity in the supplementary motor area, a region where drug-naïve patients with Parkinson's disease exhibited reduced signal fluctuations compared with untreated patients. At the spectral frequency level, levodopa stimulated these fluctuations in a selective frequency band of the sensorimotor network. The reported effects induced by levodopa on sensorimotor network topological and spectral features confirm that the sensorimotor system is a target of acute levodopa administration in drug-naïve patients with Parkinson's disease. Moreover, while the regional changes in supplementary motor area reflect the functional improvement in motor function, the rhythm-specific modulation induced by the dopamine precursor discloses a novel aspect of pharmacological stimulation in Parkinson's disease, adding further insight to the comprehension of levodopa action.

Magnetic Resonance Imaging (MRI) in Parkinson's Disease

Recent developments in brain imaging methods are on the verge of changing the evaluation of people with Parkinson's disease (PD). This includes an assortment of techniques ranging from diffusion tensor imaging (DTI) to iron-sensitive methods such as T₂^*, as well as adiabatic methods R_1ρ and R_2ρ, resting-state functional MRI, and magnetic resonance spectroscopy (MRS). Using a multi-modality approach that ascertains different aspects of the pathophysiology or pathology of PD, it may be possible to better characterize disease phenotypes as well as provide a surrogate of disease and a potential means to track disease progression.

Automatic assistance to Parkinson's disease diagnosis in DaTSCAN SPECT imaging

PURPOSE

In this work, an approach to computer aided diagnosis (CAD) system is proposed as a decision-making aid in Parkinsonian syndrome (PS) detection. This tool, intended for physicians, entails fully automatic preprocessing, normalization, and classification procedures for brain single-photon emission computed tomography images.

METHODS

Ioflupane[(123)I]FP-CIT images are used to provide in vivo information of the dopamine transporter density. These images are preprocessed using an automated template-based registration followed by two proposed approaches for intensity normalization. A support vector machine (SVM) is used and compared to other statistical classifiers in order to achieve an effective diagnosis using whole brain images in combination with voxel selection masks.

RESULTS

The CAD system is evaluated using a database consisting of 208 DaTSCAN images (100 controls, 108 PS). SVM-based classification is the most efficient choice when masked brain images are used. The generalization performance is estimated to be 89.02 (90.41-87.62)% sensitivity and 93.21 (92.24-94.18)% specificity. The area under the curve can take values of 0.9681 (0.9641-0.9722) when the image intensity is normalized to a maximum value, as derived from the receiver operating characteristics curves.

CONCLUSIONS

The present analysis allows to evaluate the impact of the design elements for the development of a CAD-system when all the information encoded in the scans is considered. In this way, the proposed CAD-system shows interesting properties for clinical use, such as being fast, automatic, and robust.

Functional brain networks in movement disorders: recent advances

PURPOSE OF REVIEW

Different neuroimaging techniques have been used to identify disease-specific functional brain networks in Parkinson's disease, atypical parkinsonian syndromes, and other movement disorders. This review highlights recent advances in network imaging and its clinical applications in movement disorders.

RECENT FINDINGS

Positron emission tomography and functional MRI studies have revealed distinct, abnormal metabolic brain networks and altered interregional connectivity in Parkinson's disease and related movement disorders. Network-level functional changes have been found to correlate with disease severity and progression. Moreover, network-based categorization algorithms are proving useful in enhancing the accuracy of clinical diagnosis in patients with early symptoms and in providing objective evidence of treatment response.

SUMMARY

Although in most movement disorders the predominant histopathology involves the basal ganglia, including the substantia nigra, functional changes in relevant neural circuits are not limited to these structures. The current advances in functional brain imaging have contributed to a better pathophysiological understanding of movement disorders as complex alterations of widespread functional brain networks. The promising findings from recent studies may help to establish new and reliable biomarkers to monitor disease progression and treatment effects in future clinical trials.

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.

Dissociable dorsal and ventral frontostriatal working memory circuits: evidence from subthalamic stimulation in Parkinson's disease

In this study, we investigated the neural substrates involved in visual working memory (WM) and the resulting effects of subthalamic nucleus (STN) stimulation in Parkinson's disease (PD). Cerebral activation revealed by positron emission tomography was compared among Parkinson patients with (PD-ON) or without (PD-OFF) STN stimulation, and a group of control subjects (CT) in two visual WM tasks with spatial (SP) and nonspatial (NSP) components. PD-OFF patients displayed significant reaction time (RT) deficits for both memory tasks. Although there were no significant differences in RT between patients with PD-ON and -OFF stimulation, patients with PD-ON stimulation performed comparably to controls. The memory tasks were executed with normal error rates in PD-ON and -OFF stimulation. In contrast to these behavioral results, whether the corresponding prefrontal activation was differentially affected by deep brain stimulation status in patients depended on whether the WM modality was SP versus NSP. Thus, SP WM was associated with (1) abnormal reduction in dorsolateral prefrontal activity in PD-OFF and -ON stimulation and (2) abnormal overactivation in parieto-temporal cortex in PD-OFF and in limbic circuits in PD-ON stimulation. In NSP WM, normal activation of the ventral prefrontal cortex was restored in PD-ON stimulation. In both visual modalities the posterior cerebral regions including fusiform cortex and cerebellum, displayed abnormally reduced activity in PD. These results indicate that PD induces a prefrontal hypoactivation that STN stimulation can partially restore in a modality selective manner by additional recruitment of limbic structures in SP WM or by recovery of the ventral prefrontal activation in NSP WM.

Patterns of spontaneous brain activity in amyotrophic lateral sclerosis: a resting-state FMRI study

By detecting spontaneous low-frequency fluctuations (LFF) of blood oxygen level–dependent (BOLD) signals, resting-state functional magnetic resonance imaging (rfMRI) measurements are believed to reflect spontaneous cerebral neural activity. Previous fMRI studies were focused on the examination of motor-related areas and little is known about the functional changes in the extra-motor areas in amyotrophic lateral sclerosis (ALS) patients. The aim of this study is to investigate functional cerebral abnormalities in ALS patients on a whole brain scale. Twenty ALS patients and twenty age- and sex-matched healthy volunteers were enrolled. Voxel-based analysis was used to characterize the alteration of amplitude of low frequency fluctuation (ALFF). Compared with the controls, the ALS patients showed significantly decreased ALFF in the visual cortex, fusiform gyri and right postcentral gyrus; and significantly increased ALFF in the left medial frontal gyrus, and in right inferior frontal areas after grey matter (GM) correction. Taking GM volume as covariates, the ALFF results were approximately consistent with those without GM correction. In addition, ALFF value in left medial frontal gyrus was negatively correlated with the rate of disease progression and duration. Decreased functional activity observed in the present study indicates the underlying deficits of the sensory processing system in ALS. Increased functional activity points to a compensatory mechanism. Our findings suggest that ALS is a multisystem disease other than merely motor dysfunction and provide evidence that alterations of ALFF in the frontal areas may be a special marker of ALS.

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.

Poor dissociation of patient-evaluated apathy and depressive symptoms

Apathy has traditionally been conceptualised as part of depression. The appropriateness of this conceptualisation has now been questioned, with the realization that apathy constitutes a distinct neuropsychiatric condition, with separate rehabilitation and patient-care implications to depression. Research on the relationship between apathy and depression has, however, produced mixed results. One reason for this inconsistency may lie behind who does the apathy evaluation. In this study we investigated whether the relationship between apathy and depression would differ when apathy was evaluated by the patients or an informant. A total of 49 brain damaged patients were assessed on self- and informant-rated Apathy Evaluation Scales. The relationship between the apathy scores and depressive symptoms was then investigated. Patient-rated, and not informant-rated apathy significantly correlated with depression. We discuss the implication of these results on the relationship between the two neuropsychiatric conditions and also in relation to the utility of patient self-evaluations in apathy.

In vivo imaging of the integration and function of nigral grafts in clinical trials

In vivo functional imaging has provided objective evidence for the integration and function of nigral grafts in the brains of patients with Parkinson's disease. Clinical trials with the use of positron emission tomography have shown that transplants of human dopamine-rich fetal ventral mesencephalic tissue can survive, grow, and release dopamine providing motor symptom relief, and also that they can restore brain activation related to movement. Positron emission tomography has aided in the elucidation of the pathophysiology of serious adverse effects, so-called graft-induced dyskinesias. With the use of newly established radioligands, positron emission tomography and single-photon emission computed tomography could help to improve Parkinson's patient selection in future clinical trials by selecting those with better predicted outcomes. Moreover, positron emission tomography could help monitoring postoperational inflammatory processes around the grafted tissue and the effect of immunosuppression. Recent evidence from positron emission tomography has provided insight of how ongoing extrastriatal serotonergic denervation may have relevance to nonmotor symptoms in transplanted Parkinson's disease patients indicating new cell therapy targets for a more complete relief of symptoms. Functional and structural magnetic resonance imaging techniques could help to better assess the integration of nigral graft with the host brain by assessing the restoration of brain activation during movement and of functional and structural connectivity. This knowledge should lead to the development of new, optimized in vivo imaging protocols that could help to better schedule, monitor, and modify the clinical outcomes of future human trials assessing the efficacy of fetal or stem cell therapy in Parkinson's disease.