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Abnormal Baseline Brain Activity in Non-Depressed Parkinson’s Disease and Depressed Parkinson’s Disease: A Resting-State Functional Magnetic Resonance Imaging Study

  • Xuyun Wen,

    Affiliation State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

  • Xia Wu ,

    Affiliations State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, School of Information Science and Technology, Beijing Normal University, Beijing, China

  • Jiangtao Liu,

    Affiliation Beijing Xuan Wu Hospital, Beijing, China

  • Ke Li,

    Affiliation Beijing 306 Hospital, Beijing, China

  • Li Yao

    Affiliations State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, School of Information Science and Technology, Beijing Normal University, Beijing, China

Abnormal Baseline Brain Activity in Non-Depressed Parkinson’s Disease and Depressed Parkinson’s Disease: A Resting-State Functional Magnetic Resonance Imaging Study

  • Xuyun Wen, 
  • Xia Wu, 
  • Jiangtao Liu, 
  • Ke Li, 
  • Li Yao


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.


For people with Parkinson’s disease (PD), depression is the most common and disabling symptom, and up to 50 percent of people with PD experience mild or moderate depressive symptoms [1], [2]. In addition to the unpleasant mood characteristics, depression can worsen the symptoms of PD, such as motor symptom deterioration [3], [4], rapid disease progression [5] and cognitive attenuation [3], [6]. Therefore, understanding and characterizing the underlying brain mechanisms of depression in PD patients using a neuroimaging approach is clearly an international imperative.

During the last decades, the pathophysiology of depression in PD patients has been accumulated from structural and functional neuroimaging studies [2], [7], [8], [9], [10]. High-resolution structural magnetic resonance imaging (MRI) showed PD patients with depression displayed abnormality in size of some areas, including the orbitofrontal gyrus, the superior temporal pole, and the mediodorsal thalamus, when compared with the patients with PD alone [2], [9]. Functional neuroimaging techniques were also been used to study depression in PD patients [2], [7]. A previous PET study found decreased levels of regional cerebral blood flow (rCBF) in the medial prefrontal cortex and the cingulated cortex in depressed PD group contrast to non-depressed PD group [7]. Recently, Cardoso and his colleagues using functional magnetic resonance imaging (fMRI) observed decreased activities in the left mediodorsal thalamic nucleus and the left dorsomedial prefrontal cortex of depressed PD patients but not of non-depressed PD patients [2]. These abnormal brain regions, which were found in these previous studies, mainly focused on the prefrontal cortex and limbic system, implying depression in PD patients may be associated with abnormal alterations in the prefrontal-limbic network.

Recently, resting-state fMRI has been widely used for investigating the brain functions under normal and pathological conditions for several special advantages, including high-resolution, no radiation use, and easy application [11], [12], [13], [14]. During rest, low-frequency blood-oxygen level fluctuations within a specific frequency range (0.01–0.08 Hz) are considered to be related to spontaneous neuronal activity [11], [12], [15]. The amplitude of low-frequency fluctuations (ALFF), in a method developed by Zang et al., has been widely applied to explore abnormal brain activity associated with some neuropsychiatric disorders, including mild cognitive impairment (MCI) [16], depression [17], Alzheimer’s disease (AD) [18], schizophrenia [19] and medial temporal lobe epilepsy [20]. Compared with traditional, task-related fMRI, the resting-state fMRI can be performed in all manner of people and is especially fit for people who are unable to cooperate with functional tasks [21]. To date, few resting-state fMRI studies have examined whether depressed PD patients present an abnormal activities.

In our study, we utilized ALFF to investigate the alterations in resting state brain activities in depressed PD patients compared with non-depressed PD patients. These abnormalities may be a trait marker and could be helpful for the future diagnosis of depression in PD patients. Based on previous studies, we hypothesized that an abnormal ALFF would be discovered in certain areas of the prefrontal-limbic network in depressed PD patients contrast to those patients with PD alone. In addition, we also compared PD patients those with and without depression with normal controls (Ncs).


Ethics Statement

The human fMRI experiment conducted in this study was approved by the Institutional Review Board of Beijing Normal University (BNU) Imaging Center for Brain Research, National Key Laboratory of Cognitive Neuroscience. All of the subjects gave written informed consent according to the guidelines set by the MRI Center of Beijing Normal University.


Twenty-one right-handed NCs and thirty-three right-handed patients with idiopathic Parkinson’s disease, who were recruited from the Beijing Xuan Wu Hospital of China, participated in this study after giving written informed consent. The diagnosis of PD was based on medical history, physical and neurological examinations, response to levodopa or dopaminergic drugs, and laboratory tests and MRI scans to exclude other diseases. All subjects came in off medication for imaging and neuropsychological testing. Only PD patients with normal cognitive function as defined by a score on the Mini-Mental State Examination (MMSE) of 27 or more [22] were selected. Seventeen of PD patients were diagnosed with depression disorder according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) criteria (American Psychiatric Association, 1994) and the remaining sixteen patients had PD alone. The 24-item Hamilton Depression Rating Scale (HDRS) was used to evaluate the severity of depression and all depressed PD patients had a score of at least 8 points at HDRS [23]. Additionally, Unified Parkinson’s Disease Rating Scale (UPDRS) [24] and Hoehn and Yahr (HY) [25] were also recorded for describing the severity of the PD. The detailed clinical data were shown in Table I.

fMRI Data Acquisition

All fMRI data were acquired on a 3-Tesla Siemens whole-body MRI system scanner at Xuan Wu Hospital in Beijing, China. Foam padding and earplugs were used to limit head movement and reduce scanner noise for the subject. During the scan, the subjects were instructed to rest and keep their eyes closed without thinking about anything in particular. The functional images were collected using echo planar imaging (EPI) sequence. For each subject, 210 images were collected and the imaging parameters were as follows: repetition time = 2000 ms; echo time = 40 ms; Flip Angle = 90o; slice = 28; matrix size = 64×64; voxel size = 4×4×5 mm3. A high-resolution, three-dimensional T1-weighted structural image was acquired for each subject with the following parameters: repetition time = 2100 ms; echo time = 3.25 ms; Flip Angle = 10o; slice = 176; matrix size = 224×256; voxel size = 1×1×1 mm3.

Data Processing

Image preprocessing was performed using Statistical Parametric Mapping (SPM8, Allowing for the equilibration of the magnetic field, the first 10 volumes were discarded. The remaining 200 time points were slice-timing corrected to the middle axial slice, and all images were then realigned to the first image to account for head motion. A participant would be excluded if the translation and rotation parameters exceeded ±2 mm or 2o during the whole fMRI scans. In our study, no subjects were excluded. After slice acquisition and head motion correction were performed, all of the volumes were spatially normalized to the standard SPM8 Montreal Neurological Institute (MNI) template, re-sampled to 3 mm cubic voxels, and smoothed by a Gaussian kernel with the full width set at a half maximum of 5 mm.

Table 2. Brain regions exhibiting an altered ALFF between depressed PD patients and non-depressed PD patients.

ALFF Calculation

After preprocessing in SPM8, Further data preprocessing and ALFF analysis was performed with REST software ( [26]. Firstly, the linear trend was removed, and every voxel was band-pass filtered (0.01 Hz<f<0.08 Hz) to remove the effects of low-frequency drift and high-frequency noise. Then we removed the influence of head motion using linear regression but white matter and cerebral cerebrospinal fluid (CSF) were not regressed out. The ALFF calculation procedure: 1) Fast Fourier Transform (FFT) was used to convert all voxels from the time domain to the frequency domain; 2) the ALFF of every voxel was calculated by averaging the square root of the power spectrum across 0.01 Hz to 0.08 Hz; 3) the resulting ALFF was converted into z-scores by subtracting the mean and dividing by the global standard deviation for standardization purposes.

Table 3. Brain regions exhibiting an altered ALFF between non-depressed PD patients and NCs.

Statistical Analysis

A two-sample t-test was performed to explore the ALFF differences among depressed PD patients, non-depressed PD patients and NCs. The between-group statistical threshold was set at p = 0.005 and cluster size> = 432 mm3 (16 voxels), which corresponded to a corrected p<0.05. This correction was determined by the Monte Carlo simulations, which were performed with REST software ( (whole brain mask: 70831 voxels; simulation number = 5000) [26].

Table 4. Brain regions exhibiting an altered ALFF between depressed PD patients and NCs.

Correlation between Clinical Data and ALFF

To examine the association of the ALFF abnormality with the severity of the depression of PD patients, we performed a partial correlation analysis (controlling age and gender) between HDRS data and ALFF values extracted from clusters of voxels, which showed the most significant differences between depressed and non-depressed PD patients. Each of the clusters was the intersection of the corresponding region defined by Anatomical Automatic Labeling atlas toolbox [27] and the within group two sample t-test map with a cut-off threshold at p = 0.005.


Clinical and Demographic Testing of Samples

In regard to our clinical and demographics of sample participants (Table I), there were no significant differences in gender (t = 0.495, p = 0.624), age (t = 1.668, p = 0.105), MMSE (t = 0.692, p = 0.495), HY (t = 0.394, p = 0.730) and UPDRS scores (t = 1.656, p = 0.110) between depressed PD patients and non-depressed PD patients. As for HDRS (t = 8.965, p<0.001), PD patients with depression were significantly higher than those with PD alone. Our study tested non-depressed PD patients and NCs and found the differences in gender (t = 0.709, p = 0.483) and age (t = 1.414, p = 0.166) were also not significant.

Depressed PD Patients versus Non-depressed PD Patients

Compared to non-depressed PD patients, depressed PD patients exhibited a decreased ALFF in the right dorsolateral prefrontal cortex (DLPFC), ventromedial prefrontal cortex (vMPFC), the rostral anterior cingulate cortex (rACC), the superior frontal cortex and the right middle temporal gyrus. The opposite (depressed>non-depressed) was observed in the right cerebellum posterior lobe and the right cerebellum anterior lobe. Detailed information about the Montreal neurological institute (MNI) coordinates and clusters was provided in Fig. 1A and Table II.

Figure 1. Statistical parametric map showing the significant differences in the ALFF between three groups: depressed PD patients, non-depressed PD patients and NCs.

A) The differences between depressed PD patients and non-depressed PD patients. B) The differences between non-depressed PD patients and NCs. C) The differences between depressed PD patients and NCs. The threshold for display was set to p<0.005, cluster size> = 432 mm3.

Non-depressed PD Patients versus NCs

The group differences between non-depressed PD patients and NCs were shown in Fig. 1B and Table III. The ALFF in NCs was higher than in patients with PD alone in the bilateral caudate, the left putamen, the supplementary motor area (SMA), the bilateral superior frontal gyrus and the posterior cingulated gyrus. The ALFF, which was significantly lower in NCs than non-depressed PD, was found in the left middle temporal gyrus, the right middle occipital gyrus, the bilateral superior occipital gyrus, the left inferior temporal gyrus, the left precuneus and the right angular gyrus.

Depressed PD Patients versus NCs

As shown in Fig. 1C and Table IV, the depressed PD group demonstrated a decreased ALFF in the bilateral caudate, the left putamen, the bilateral precuneus, the right superior frontal gyrus, the right middle frontal gyrus, the left putamen, the right medial frontal gyrus, the right superior temporal gyrus and the right thalamus contrast to NCs. Conversely, the right angular gyrus, the bilateral middle temporal gyrus, the left inferior frontal gyrus, the left precuneus, the left inferior parietal gyrus and the right fusiform gyrus displayed an increased ALFF in the depressed PD patients.

Correlations between ALFF Values and HDRS

We examined the relationships between the HDRS and ALFF in regions with significant group differences (depressed PD patients vs. non-depressed PD patients), including DLPFC, rACC, vMPFC. The only significant correlation we found between ALFF values and HDRS was in the DLPFC (r = 0.698, p = 0.003). The other correlation were all less than ±0.2 (p>0.05).


The present fMRI study aimed to investigate the alterations in resting-state brain activities in depressed PD patients, and we found a decreased ALFF in the DLPFC, the vMPFC and the rACC in depressed PD patients when compared with non-depressed PD patients. Inversely, An increased ALFF (depressed PD patients> non-depressed PD patients) was observed in the cerebellum posterior cortex. In addition, when compared with NCs, the depressed PD patents and non-depressed PD patients both showed altered activities mainly in the basal ganglia and the prefrontal cortex. Furthermore, a significant positive correlation was found between the HDRS score and ALFF within the DLPFC.

The DLPFC provides a key hub in the prefrontal-limbic network which connects to the orbitofrontal cortex, the thalamus, parts of the basal ganglia, the hippocampus, and primary and secondary association areas of the neocortex [28]. It has an important role in cognitive, executive and emotional processes, especially the down-regulation of negative emotional conditions [29], [30], [31]. Abnormal activity in the DLPFC may lead to a cognitive and mental disorder and partly contribute to interest or pleasure deficiency and cognition declines exhibited by patients with depression [32], [13]. Our current study using resting-state fMRI found a decreased ALFF in the DLPFC in depressed PD patients contrast to those patients with PD alone and a positive correlation was also been found between HDRS score and ALFF values in the DLPFC. Consistent with our result, the hypoactivity in the DLPFC in depression has been identified by many previous studies, which was regarded as a critical hallmark for depression [7], [32], [33], [34], [35], [36], [37]. For example, Bench et al. found a decreased rate of metabolism and decreased rCBF levels in the DLPFC in depression [32], and an increase in activity in the DLPFC will remit depression symptoms [37]. Similar results were also been found in depressed PD groups. A previous PET study reported a decreased rCBF level in the DLPFC of depressed PD patients compared with non-depressed PD patients [7], and stimulating the DLPFC with repetitive transcranial magnetic stimulation (rTMS) can be effective in remitting depression symptoms in PD [33], [35]. Together with these findings, we speculated that hypoactivation in the DLPFC may be an important factor for the genesis and development of depression in PD patients.

The vMPFC seems to be a critical area in PD-associated depression. The abnormalities within the vMPFC in patients with major depressive disorder (MDD) have been documented in previous structure and functional studies [38], [39], [40]. In our study, we using ALFF investigated the abnormalities of depressed PD patients and found a decreased activity in the vMPFC in depressed PD contrast to those without depression PD patients. Similar to our finding, a previous PET study compared the regional blood flow of depressed and non-depressed PD patients and found a decreased rCBF level in the vMPFC [7]. The vMPFC is connected with the ACC, the hippocampus and the amygdala [41] and plays a vital role in emotion generation and regulation [42], [43]. The activity of the vMPFC was associated with the suppression of affective responses to a negative emotional signal and might dampen amygdala activity [44]. Jonestone et al. found, during an effortful affective reappraisal task, normal subjects showed an inverse relationship between amygdala but depressed individuals were not [45]. Therefore, the decreased level of activity in the vMPFC in depressed PD patients may lead to an imbalance in the inhibitory influence of the amygdala on activity leading to the genesis of depression. Our data do not allow us to state that an altered relationship between the vMPFC and the amygdala is responsible for the observed decrease in activity levels in the vMPFC, but, based on previous reports, this hypothesis should be evaluated in the future.

In addition, a decreased ALFF in the rACC was also reported in our current study. The rACC is a part of the brain’s limbic system which is strongly connected with the amygdala, the orbitofrontal cortex and the hippocampus, and it has been reported to be associated with the processing and integration of affect-related information [46], [47]. Lesions in the rACC can lead to a series of symptoms, including apathy, inattention, the dysregulation of autonomic functions, akineticmutism and emotional instability, which overlap considerably with the quintessential symptoms of patients with MDD, implying depression has a relationship with abnormal activity in rACC [48]. A recent resting-state fMRI study also demonstrated that the severity of depression in PD patients was correlated with the ALFF values in the rACC, which was consistent with the result of our current study [49]. However, in the absence of normal control group, Skidmore and his colleagues’ study could not decide whether the activity in rACC was decreased or increased in depressed PD patients. Our study compensated for this limitation and identified the rACC showed a decreased ALFF in depressed PD patients compared with non-depressed PD group, which gave a more complete fMRI status of depression in PD patients.

The regions we found to have a decreased ALFF in the depressed PD group, including the DLPFC, the vMPFC and the rACC, are parts of the prefrontal-limbic network, which is important for affective processing [50]. Previous non-invasive brain imaging study has identified abnormal changes in the prefrontal-limbic network existed in patients with MDD [41]. In our study, we found abnormal activity levels in prefrontal-limbic network were also existed in the depressed PD patients giving a new clue to the pathophysiology of depression in PD group.

In contrast to the decreased activities in the prefrontal-limbic network, we observed an increased ALFF in the right cerebellum posterior lobe in depressed PD patients compared with non-depressed PD patients. The traditional view of the cerebellum is that it is only responsible for the regulation of motor functions, but recent studies identified this area also being associated with emotional and cognitive processing [51], [52]. Previous studies demonstrated the patients with depression showed abnormal changes in cerebellum [53], [54], [55], [56]. Pillay et al. reported that patients with depression showed a volume reduction in the cerebellum [54]. Using fMRI, Liu et al. and Guo et al. found a decrease in regional homogeneity (ReHo) in depression patient group compared with NCs [55], [56]. Additionally, the reciprocal connections linking the cerebellum with brainstem areas contain neurotransmitters involved in mood regulation, including serotonin, norepinephrine and dopamine [57]. The degeneration of the dopaminergic pathway, a hallmark of PD patients [8], may lead to the genesis of increased activity in the cerebellum. Our current study provides evidence for the involvement of cerebellar abnormality in depressed PD patients.

Additionally, comparing ALFF maps between non-depressed PD patients and NCs, our study also investigated the PD related pathophysiology and found the altered activities in PD mainly focused on basal ganglia (including putamen, caudate) and prefrontal cortex. Findings from previous studies suggested that basal ganglia plays an important role in cortico-subcortical circuits, including motor, oculomotor, dorsolateral, prefrontal, lateral orbitofrontal and anterior cingulate [58], [59]. PD is a movement disorder characterized by the triad of bradykinesia, tremor at rest and muscular rigidity [60], [61], which mainly result from varying forms of abnormally patterned activity throughout the motor circuit [62]. Similar to our study, some recent researches using the index of ALFF also found abnormal activities in PD patients mainly in prefrontal cortex and motor cortex including SMA, the mesial prefrontal cortex and middle frontal cortex [63], [64]. Combined these previous findings with our current study, the speculation, that PD was associated with abnormal changes in motor circuit, was further been demonstrated.

It has recently been reported that in-scanner head motion can have an influence on analysis results even though traditional realignment was performed [65], [66]. In our study, to control the impact of head motion, we not only made every effort to reduce its occurrence in the scanner and precluded those subjects with the translation and rotation parameters exceeded ±2 mm or 2o during the whole fMRI scan, but also removed the influence of head motion using linear regression based on REST software ( [26] before ALFF calculating. In addition, following previous studies, the mean relative displacement was used to measure subjects’ head motion in scanner [65], . Then two sample t-test was used to test differences of head motion between groups and no significant differences were found (depressed PD patients vs non-depressed PD patients: t = 0.435, p = 0.666; non-depressed PD patients vs NCs: t = 0.361, p = 0.720; depressed PD patients vs NCs: t = 0.795, p = 0.432). The significant correlations between ALFF and mean relative displacement were also not found (ACC: r = −0.114, p = 0.662; MPFC: r = −0.017, p = 0.947; DLPFC: r = 0.223, p = 0.39) according to calculating the correlations coefficients between these two indexes. These findings suggested the significant differences among groups in our current study may have no relationship with head motion. However, further works are needed to explore this issue.

In summary, our study used ALFF to examine the alterations in the resting state between depressed PD patients and non-depressed PD patients and found abnormal neural activity levels in several brain areas associated with the prefrontal-limbic network. Our study not only advances the knowledge of depression in PD but also provides a new insight into the underlying neural mechanism behind the high rate of depression in PD patients.

Author Contributions

Conceived and designed the experiments: X. Wu LY JL. Performed the experiments: JL KL. Analyzed the data: X. Wen. Contributed reagents/materials/analysis tools: X. Wu LY KL. Wrote the paper: X. Wen X. Wu.


  1. 1. Edwards E, Kitt C, Oliver E, Finkelste J, Wagste M, et al. (2002) Depression and parkinson’s disease: a new look at an old problem. Depression and Anxiety 16(1): 39–48.
  2. 2. Cardoso EF, Maia FM, Fregni F, Myczkowski ML, Melo LM, et al. (2009) Depression in Parkinson’s disease: Convergence from voxel-based morphometry and functional magnetic resonance imaging in the limbic thalamus. NeuroImage 47(2): 467–472.
  3. 3. Stern Y, Marder K, Tang MX, Mayeux R (1993) Antecedent clinical features associated with dementia in Parkinson’s disease. Neurology 43: 1690–1962.
  4. 4. Marder K, Tang MX, Cote L, Stern Y, Mayeux R (1995) The frequency andassociated risk factors for dementia in patients with Parkinson’s disease. Arch Neurol 1995 52: 695–701.
  5. 5. Kuhn W, Heye N, Muller T, Kraus P, Klotz P (1996) The motor performance test series in Parkinson’s disease is influenced by depression. J Neural Transm 103: 349–354.
  6. 6. Starkstein SE, Preziosi TJ, Forrester AW, Robinson RG (1990) Specificity of affective and autonomic symptoms of depression in Parkinson’s disease. J Neurol Neurosurg Psychiatry 53:
  7. 7. Ring HA, Bench CJ, Trimble MR, Brooks DJ, Frackowiak RS, et al. (1994) Depression in Parkinson’s disease. A positron emission study. The British Journal of Psychiatry 165(3): 333–339.
  8. 8. Remy P, Doder M, Lees A, Turjanski N, Brooks D (2005) Depression in Parkinson’s disease: loss of dopamine and noradrenaline innervation in the limbic system. Brain 128(6): 1314–1322.
  9. 9. Feldmann A, Illes Z, Kosztolanyi P, Illes E, Mike A, et al. (2008) Morphometric changes of gray matter in Parkinson’s disease with depression: a voxel-based morphometry study. MovDisord 23: 42–46.
  10. 10. Politis M, Wu K, Loane C, Turkheimer FE, Molloy S, et al. (2010) Depressive symptoms in Parkinson’s disease correlate with higher 5HTT binding in raphe andlimbic structures. Neurology 75: 1920–1927.
  11. 11. Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. MagnReson Med 34: 537–541.
  12. 12. Kiviniemi V, Kantola JH, Jauhiainen J, Hyvarinen A, Tervonen O (2003) Independent component analysis of nondeterministic fMRI signal sources. Neuroimage19: 253–260.
  13. 13. Yan C, Liu D, He Y, Zou Q, Zhu C, et al.. (2009) Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PLoS ONE 4, e5743.
  14. 14. Wu X, Li R, Fleisher AS, Reiman EM, Guan XT, et al.. (2011) Altered default mode network connectivity in Alzheimer’s Disease- a resting functional MRI and Bayesian network study. Hum Brain Mapp. 10.1002/hbm.21269.
  15. 15. Zang YF, He Y, Zhu CZ, Cao QJ, Sui MQ, et al. (2007) Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development 29(2): 83–91.
  16. 16. Han Y, Wang J, Zhao ZL, Min BQ, Lu J, et al. (2011) Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study. NeuroImage 55(1): 287–295.
  17. 17. Liu CH, Li F, Li SF, Wang YJ, Tie CL, et al. (2012) Abnormal baseline brain activity in bipolar depression: A resting state functional magnetic resonance imaging study. Psychiatry Research: Neuroimaging 203(2–3): 175–179.
  18. 18. He Y, Wang L, Zang Y, Tian L, Zhang X, et al.. (2007) Regional coherence changes in the early stages of Alzheimer’s disease: a combined structural and resting-state functional MRI study. Neuroimage 35, 488–500.
  19. 19. Hoptman MJ, Zuo XN, Butler PD, Javitt DC, D’Angelo D, et al.. (2010) Amplitude of low-frequency oscillations in schizophrenia: a resting state fMRI study. Schizophr. Res. 117, 13–20.
  20. 20. Zhang ZQ, Lu GM, Zhong Y, Tan QF, Zhu JG, et al. (2008) Application of amplitude of low-frequency fluctuation to the temporal lobe epilepsy with bilateral hippocampal sclerosis: an fMRI study. Zhonghua Yi Xue Za Zhi. 88(23): 1594–1598.
  21. 21. Shimony JS, Zhang D, Johnston JM, Fox MD, Roy A, et al. (2009) Resting-state Spontaneous Fluctuations in Brain Activity. Academic Radiology 16(5): 578–583.
  22. 22. Folstein MF, Folstein SE, Mchugh PR (1975) Mini mental state: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.
  23. 23. Hamilton M (1967) Development of a rating scale for primary depressive illness. British Journal of Social and Clinical Psychology 6: 278–296.
  24. 24. Lang AE, Fahn S (1989) Assessment of Parkinson’s disease. In: Munsat TL, editor. Quantification of neurological deficit. Boston: Butterworths. P: 285–309.
  25. 25. Hoehn MM, Yahr MD (1967) Parkinsonism: onset, progression and mortality. Neurology, 17, 427–442.
  26. 26. Song XW, Dong ZY, Long XY, Li SF, Zuo XN (2011) REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing. PLoS ONE 6(9): e25031.
  27. 27. Mazoyer B, Zago L, Mellet E, Bricogne S, Etard O, et al. (2001) Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain Res Bull 54: 287–298.
  28. 28. Philip DZ, Ulrich M (2002) Executive function in typical and atypical development, in: UshaGoswam (ed): Blackwell Handbook of Child Cognitive Development.
  29. 29. Barbas H (2000) Connections underlying the synthesis of cognition, memory, and emotion in primate prefrontal cortices. Brain Res Bull 52(5): 319–330.
  30. 30. Davidson RJ, Pizzagalli D, Nitschke JB, Putnam K (2002) Depression: perspectives from affective neuroscience. Annu Rev Psychol 2002 53: 545–574.
  31. 31. Pena-Gomez C, Vidal-Piñeiro D, Clemente IC, Pascual-Leone A, Bartres-Faz D (2011) Down-Regulation of Negative Emotional Processing by Transcranial Direct Current Stimulation: Effects of Personality Characteristics. PLoS ONE 6(7): e22812 .
  32. 32. Bench CJ, Friston KJ, Brown RG, Scott LC, Fracknowiak RS, et al. (2009) The anatomy of melancholia – focal abnormalities of cerebral blood flow in major depression. Psychological Medicine 22(03): 607.
  33. 33. Dragasevic N, Potrebic A, Damjanovic A, Stefanova E, Kostic VS (2002) Therapeutic efficacy of bilateral prefrontal slow repetitive transcranial magnetic stimulation in depressed patients with Parkinson’s disease: an open study. MovDisord 17: 528–532.
  34. 34. Mottaghya FM, Kellera CE, Gangitanoa M, Lya J, Thalla M, et al. (2002) correlation of cerebral blood flow and treatment effectments of repetitive transcranial magnetic stimulation in depressed patients. Psychiatry Research Neuroimaging 115: 1–14.
  35. 35. Fregni F, Ono CR, Santos CM, Bermpohl F, Buchpiguel C, et al. (2006) Effects of antidepressant treatment with rTMS and fluoxetine on brain perfusion in PD. Neurology 66(11): 1629–1637.
  36. 36. Cardoso EF, Fregni F, Maia FM, Boggio PS, Myczkowski ML, et al.. (2008) rTMS treatment for depression in Parkinson’s disease increases BOLD responses in the left prefrontal cortex. The International Journal of Neuropsychopharmacology 11(02).
  37. 37. Koenigs M, Grafman J (2009) The functional neuroanatomy of depression: Distinct roles for ventromedial and dorsolateral prefrontal cortex. Behavioural Brain Research 201(2): 239–243.
  38. 38. Surguladze S, Keedwell PA, Phillips M (2003) Neural systems underlying affective disorders. Adv Psychiatr Treat 9: 446–455.
  39. 39. Keedwell PA, Andrew C, Williams SC, Brammer MJ, Phillips ML (2005) A Double Dissociation of Ventromedial Prefrontal Cortical Responses to Sad and Happy Stimuli in Depressed and Healthy Individuals. Biological Psychiatry 58(6): 495–503.
  40. 40. Liotti M, Mayberg HS, McGinnis S, Brannan SL, Jerabek P (2002) Unmasking disease-specific cerebral blood flow abnormalities: mood challenge in patients with remitted unipolar depression. Am J Psychiatry 159: 1830–1840.
  41. 41. Bennett MR (2011) The prefrontal–limbic network in depression: Modulation by hypothalamus, basal ganglia and midbrain. Progress in Neurobiology 93(4): 468–487.
  42. 42. Koenigs M, Young L, Adolphs R, Tranel D, Cushman F, et al. (2007) Damage to the prefrontal cortex increases utilitarian moral judgements. Nature 446: 908–911.
  43. 43. Koenigs M, Tranel D (2007) Irrational economic decision-making after ventromedial prefrontal damage: evidence from the Ultimatum Game. J Neurosci 27: 951–956.
  44. 44. Urry HL (2006) Amygdala and Ventromedial Prefrontal Cortex Are Inversely Coupled during Regulation of Negative Affect and Predict the Diurnal Pattern of Cortisol Secretion among Older Adults. Journal of Neuroscience 26(16): 4415–4425.
  45. 45. Johnstone T, van Reekum CM, Urry HL, Kalin NH, Davidson RJ (2007) Failure to Regulate: Counterproductive Recruitment of Top-Down Prefrontal-Subcortical Circuitry in Major Depression. Journal of Neuroscience 27(33): 8877–8884.
  46. 46. Devinsky O, Morrell MJ, Brent AV (1995) Contributions of anterior cingulate cortex to behavior. Brain 118(1): 279–306 .
  47. 47. Wagner G, Koch K, Schachtzabel C, Reichenbach JR, Sauer H, et al. (2007) Enhanced rostral anterior cingulate cortex activation during cognitive control is related to orbitofrontal volume reduction in unipolar depression. J Psychiatry Neurosci 2008 33(3): 199–208.
  48. 48. Bush G, Luu P, Posner MI (2000) Cognitive and emotional influences in anterior cingulated cortex.Trends Cogn. Sci. 4: 215–222.
  49. 49. Skidmore FM, Yang M, Baxter L, Deneen KV, Collingwood J, et al.. (2011) Apathy, depression, and motor symptoms have distinct and separable resting activity patterns in idiopathic Parkinson disease. NeuroImage.
  50. 50. Cardinal RN, Parkinson JA, Hall J, Everitt BJ (2002) Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neuroscience and Biobehavioral Reviews 26, 321–352.
  51. 51. Schmahmann JD, Sherman JC (1998) The cerebellar cognitive affective syndrome. Brain 121, 561–579.
  52. 52. Parvizi J, Anderson SW, Martin CO, Damasio H, Damasio AR (2001) Pathological laughter and crying: a link to the cerebellum. Brain 124, 1708–1719.
  53. 53. Escalona PR, Early B, McDonald WM (1993) Reduction of cerebellar volume in major depression: a controlled magnetic resonance imaging study. Depression 1, 156–158.
  54. 54. Pillay SS, Yurgelun-Todd DA, Bonello CM, Lafer B, Fava M, et al.. (1997) A quantitative magnetic resonance imaging study of cerebral and cerebellar grey matter volume in primary unipolar major depression: relationship to treatment response and clinical severity. Biological Psychiatry 42, 79–84.
  55. 55. Liu Z, Xu C, Xu Y, Wang Y, Zhao B, et al. (2010) Decreased regional homogeneity in insula and cerebellum: A resting-state fMRI study in patients with major depression and subjects at high risk for major depression. Psychiatry Research: Neuroimaging 182(3): 211–215.
  56. 56. Guo WB, Liu F, Xue ZM, Yu Y, Ma CQ, et al. (2011) Abnormal neural activities in first-episode, treatment-naïve, short-illness-duration, and treatment-response patients with major depressive disorder: A resting-state fMRI study. Journal of Affective Disorders 135(1–3): 326–331.
  57. 57. Turner BM, Paradiso S, Marvel CL, Pierson R, Boles-Ponto LL, et al.. (2007) The cerebellum and emotional experience. Neuropsychologia 45, 1331–1341.
  58. 58. Alexander GE, DeLong MR, Strick PL (1986) Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 9: 357–381.
  59. 59. Robinson S, Basso G, Soldati N, Sailer U, Jovicich J, et al.. (2009) A resting state network in the motor control circuit of the basal Ganglia. BMC Neuroscience, doi: 10.1186/1471-2202-10-137.
  60. 60. Long D, Wang J, Xuan M, Gu Q, Xu X, et al. (2012) Automatic Classification of Early Parkinson’s Disease with Multi-Modal MR Imaging. PLoS ONE 7(11): e47714 .
  61. 61. Vu TC, Nutt JG, Holford NH (2012) Progression of Motor and Non-Motor Features of Parkinson’s Disease and Their Response to Treatment. Br J Clin Pharmacol.
  62. 62. DeLong RM, Wichmann T (2007) Circuits and Circuit Disorders of the Basal Ganglia, Neurological Research, vol. 64, 20–24.
  63. 63. Kwak Y, Peltier SJ, Bohnen NI, Muller M, Dayalu P, et al.. (2012) L-DOPA changes spontaneous low-frequency BOLD signal oscillations in Parkinson’s disease: a resting state fMRI study. Frontiers in Systems Neuroscience 6.
  64. 64. Skidmore FM, Yang M, Baxter L, Deneen KV, Collingwood J, et al.. (2011) Reliability analysis of the resting state can sensitively and specifically identify the presence of Parkinson disease. NeuroImage.
  65. 65. Van Dijk KRA, Sabuncu MR, Buckner RL (2012) The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59(1): 431–438.
  66. 66. Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliot MA, et al. (2012) Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. NeuroImage 60: 623–632.