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Network biomarkers for the diagnosis and treatment of movement disorders
Neurobiology of Disease 35 (2009) 141–147
Contents lists available at ScienceDirect
Neurobiology of Disease j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n b d i
Review
Network biomarkers for the diagnosis and treatment of movement disorders Kathleen L. Poston a,b, David Eidelberg a,c,⁎ a b c
Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, NY, USA Department of Neurology, Columbia University Medical Center, New York, NY, USA Departments of Neurology and Medicine, North Shore University Hospital and New York University School of Medicine, New York, NY, USA
a r t i c l e
i n f o
Article history: Received 5 August 2008 Revised 25 September 2008 Accepted 30 September 2008 Available online 1 November 2008 Keywords: Positron emission topography (PET) Parkinsonism Movement disorders Biomarkers Brain metabolism Differential diagnosis Treatment response
a b s t r a c t Functional brain networks provide a set of useful biomarkers for the assessment of movement disorders such as Parkinson's disease (PD). Spatial covariance analysis of imaging data from PD patients has led to the identification of abnormal metabolic patterns associated with the motor and cognitive features of this disease. Measurements of pattern expression have been used for diagnosis, assessment of rates of disease progression, and objective evaluation of the efficacy of therapeutic interventions. For instance, the recent identification of new disease-specific patterns for Multiple System Atrophy (MSA) and Progressive Supranuclear Palsy (PSP) has improved diagnostic accuracy in patients with parkinsonian syndromes. Further, disease-related networks have been found to be modulated by novel treatment strategies such as gene therapy. Finally, the application of network analysis to the study of inherited movement disorders such as Huntington's disease can aid in the assessment of disease-modifying therapies in pre-symptomatic gene mutation carriers. © 2008 Elsevier Inc. All rights reserved.
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metabolic networks in Parkinson's disease . . . . . . . . . . . . . . . The PD-related motor pattern (PDRP) . . . . . . . . . . . . . . . The PD-related cognitive pattern (PDCP) . . . . . . . . . . . . . . Using network analysis in the differential diagnosis of Parkinsonism . Network evolution during disease progression in PD . . . . . . . . . . Network modulation with PD treatment . . . . . . . . . . . . . . . . Dopaminergic treatment . . . . . . . . . . . . . . . . . . . . . Stereotaxic interventions . . . . . . . . . . . . . . . . . . . . . Network analysis in other movement disorders. . . . . . . . . . . . . Huntington's disease . . . . . . . . . . . . . . . . . . . . . . . Idiopathic torsion dystonia . . . . . . . . . . . . . . . . . . . . Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Introduction
⁎ Corresponding author. Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, 350 Community Drive, Manhasset, NY 11030, USA. Fax: +1 516 562 1008. E-mail address: [email protected] (D. Eidelberg). Available online on ScienceDirect (www.sciencedirect.com). 0969-9961/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.nbd.2008.09.026
Neurodegenerative diseases characterized by involuntary movements constitute a group of frequently diagnosed conditions in neurological clinics. Current practice relies on clinical assessment and follow-up for the diagnosis and treatment of these disorders. However, postmortem studies show only 76% diagnostic accuracy for patients thought to have Parkinson's disease (PD) by clinical evaluation (Hughes et al., 1992). Indeed, differentiating between parkinsonian disorders on clinical grounds alone has proven unsatisfactory, particularly early after
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symptom onset (Litvan et al., 1998). In approximately one-third of patients the initial diagnosis changes by the fifth year of symptoms; therefore the best diagnostic accuracy is achieved after follow-up assessment by a trained movement disorders specialist (Hughes et al., 2002). Early differential diagnosis in patients with movement disorders is important for both prognosis and decisions related to treatment options. Disease-specific biomarkers would be beneficial to aid in clinical diagnosis for patients with new symptoms and for those with more complicated symptoms. In addition, with the development of neuroprotective treatments in PD there is a critical need to identify biomarkers for use in clinical trials. One current challenge in the field of movement disorders is to validate sensitive and reliable biomarkers to diagnose various parkinsonian disorders, assess clinical progression, and evaluate therapeutic interventions. Metabolic brain imaging with 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) has become an important biomarker for the study of movement disorders. The application of network image analysis to FDG PET provides valuable information concerning abnormal functional connectivity in neurodegenerative disorders. By mapping glucose metabolism at a voxel level, this imaging approach provides a measure of regional synaptic activity (Eidelberg et al., 1997b). Localized pathology can therefore alter functional connectivity across the entire brain in a disease-specific manner. Spatial covariance methods have been used extensively to identify these abnormalities at the network level in PD and other neurodegenerative disorders (Eckert and Eidelberg, 2005; Eckert et al., 2007b; cf. Habeck et al., 2008).
dementia in PD can range from 17% to 43% (Riedel et al., 2008), mild cognitive deficits can be present much earlier (Caviness et al., 2007). A voxel-based spatial covariance approach with FDG PET has been used to identify a distinct PD-related metabolic pattern associated with cognitive dysfunction in non-demented patients (Huang et al., 2007a). The PD-related cognitive pattern (PDCP) is characterized by metabolic reductions in medial frontal and parietal association regions with relative increases in the cerebellar vermis and dentate nuclei (Fig. 1, right). It is statistically unrelated (i.e., orthogonal) to the PDRP motor network (Huang et al., 2007a). Correlations between PDCP expression and neuropsychological test performance are significant on tests of executive functioning (Huang et al., 2007a). Indeed, the expression of this pattern proved to be elevated in PD patients with mild cognitive impairment (MCI) relative to their cognitively normal counterparts (Huang et al., 2008). Like the PDRP, the PDCP pattern has been found to be highly reproducible in individual patients. However, unlike the PDRP, PDCP activity is not modulated by symptomatic treatment with levodopa or STN stimulation (Huang et al., 2007a; cf. Hirano et al., 2008). Moreover, the time course of PDCP expression in early stage disease is different from that of the PDRP (Huang et al., 2007b), with a slower, non-linear trajectory that is unrelated to concurrent changes in motor function or striatal dopamine transporter (DAT) binding (Eckert et al., 2007b). These findings suggest the PDCP can be used as a complementary network biomarker with specificity for early cognitive dysfunction in patients with PD.
Metabolic networks in Parkinson's disease
Accurate diagnosis based solely on clinical symptoms can be challenging in patients with an atypical parkinsonian syndrome, particularly early in the disease course. Multiple System Atrophy (MSA) and Progressive Supranuclear Palsy (PSP) are two of the most common forms of atypical parkinsonism and are found on neuropathological examination in 5% to 10% of patients with a clinical diagnosis of PD (Schrag et al., 1999; Hughes et al., 2002). These atypical forms are characterized by more rapid progression of
The PD-related motor pattern (PDRP) PET can be used to study the pathophysiology of PD and to assess the treatment of this disease. In patients with the hallmark motor features of PD, network analysis of FDG PET data consistently reveal the presence of an abnormal spatial covariance pattern involving metabolic changes at key nodes of the cortico-striato-pallidothalamocortical (CSPTC) loops and related pathways (Eidelberg et al., 1994; 1997b; Moeller et al., 1999; cf. Ma et al., 2007). This Parkinson's disease-related pattern (PDRP) is characterized by increased pallido-thalamic and pontine metabolic activity associated with relative reductions in premotor cortex, supplementary motor area (SMA), and in parietal association regions (Fig. 1, left). To date, the PDRP metabolic network has been detected in seven independent patient populations scanned using vastly different techniques (e.g., Moeller et al., 1999; Feigin et al., 2002; Lozza et al., 2004; cf. Eckert et al., 2007b). Its expression in individual subjects is highly reproducible, with stable network activity recorded over hours to weeks (Ma et al., 2007). PDRP scores have also been used to discriminate between patients with PD and atypical parkinsonism (Ma et al., 2007; Eckert et al., 2007a, 2008) and patients with essential tremor (S. Hirano and I.U. Isaias, personal communication). In PD patients, pattern expression has been found consistently to correlate with standardized motor ratings (e.g., Asanuma et al., 2006; cf. Eidelberg et al., 1994, 1995), symptom duration (Moeller and Eidelberg, 1997; Huang et al., 2007b), and intraoperative measurements of subthalamic nucleus (STN) firing rate (Lin et al., 2008). These studies support the use of PDRP as a biomarker to differentiate PD patients from patients with atypical parkinsonism, and to assess longitudinal changes in disease-related motor disability (see below). The PD-related cognitive pattern (PDCP) In addition to motor symptoms, PET has been used to study the cognitive changes associated with PD. While the prevalence of
Using network analysis in the differential diagnosis of Parkinsonism
Fig. 1. Parkinson's disease-related spatial covariance patterns. Left: PD-related motor pattern (PDRP) characterized by pallidothalamic, pontine, and motor cortical hypermetabolism, associated with relative metabolic reductions in the lateral premotor and posterior parietal areas (Ma et al., 2007). Right: PD-related cognitive pattern (PDCP) characterized by hypometabolism of prefrontal cortex, rostral supplementary motor area, and superior parietal regions (Huang et al., 2007a). [Both covariance patterns were overlaid on T1-weighted MR-template images. The displays represent voxels that contributed significantly to each covariance pattern and that were found to be reliable on bootstrap resampling. Relative metabolic increases are displayed in red; relative metabolic decreases are displayed in blue. The left hemisphere was cut in the transverse plane at z = − 5 mm. The right hemisphere was displayed as a surface projection on the same brain template.] [J. Neurosci. 28, 4201–4209 Copyright 2008 By the Society for Neuroscience].
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symptoms and very poor response to medications (Litvan et al., 2003; Geser et al., 2006). Using FDG PET and voxel-based spatial covariance analysis, we have identified characteristic patterns of regional glucose metabolism in patients with MSA and PSP (Eckert et al., 2005; Eckert and Edwards, 2007). By applying strictly defined statistical criteria to the imaging data to identify disease-related patterns (Eckert et al., 2008; cf. Habeck et al., 2008), we found that MSA and PSP were each associated with specific and highly stable metabolic brain networks. The MSArelated pattern (MSARP) is characterized by bilateral metabolic decreases in the putamen and the cerebellum (Fig. 2, top). By contrast, the PSP-related pattern (PSPRP) is characterized by metabolic decreases predominately in the upper brainstem and medial prefrontal cortex as well as in the medial thalamus, the caudate nuclei, the anterior cingulate area, the ventrolateral prefrontal cortex and in the frontal eye fields (Fig. 2, bottom). In both diseases, pattern expression was significantly elevated (p b 0.001) in patients relative to age-matched healthy control subjects (Eckert et al., 2008). In addition, pattern expression was also elevated (p N 0.001) in two independent patient groups when compared to a prospectively scanned healthy control group. Achieving a high sensitivity in separating patients from normal subjects suggests potential utility of these patterns as functional imaging biomarkers for MSA and PSP. Due to the deficient treatment response and the overall poor outcome in atypical forms of parkinsonism, the differentiation of parkinsonian disorders is crucial for appropriate therapeutic decisions and prognostic counseling. As part of an ongoing study, we developed a fully automated algorithm that is blind to clinical assessment to diagnose individual cases (Spetsieris et al., 2006). We quantified the expression of the PDRP, MSARP and PSPRP in patients with clinical parkinsonism that was otherwise undiagnosed at the time of the scan.
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Indeed, the use of disease-related networks yielded highly accurate (approximately 90%) differentiation between PD and atypical parkinsonism (Eckert and Edwards, 2007). Further studies on larger patient cohorts with clinical and pathological diagnostic follow-up are currently in progress. Accurate diagnosis is essential for patient recruitment in PD clinical trials, particularly with the development of potential neuroprotective therapies. In prior studies, dopaminergic imaging has been found to be normal in approximately 15% of parkinsonian patients enrolled in clinical trials (The Parkinson Study Group, 2002; Whone et al., 2003; Fahn et al., 2004). Such patients have been termed “scans without evidence of dopaminergic deficit” (SWEDD) (Marek et al., 2005). FDG PET can be used as a biomarker to aid in discrimination of PD and atypical parkinsonian disorders, particularly when dopamine imaging is normal. Using network analysis in FDG PET, we studied eight parkinsonian patients with normal 18F-fluorodopa (F-DOPA) PET who fit the definition for SWEDD. These subjects had further imaging with FDG PET to aid in differential diagnosis (Eckert et al., 2007a). In these subjects, the patterns of metabolic abnormality were inconsistent with classical PD, MSA and PSP. Network quantification revealed that as opposed to patients with classical PD, PDRP expression was not elevated in these patients. Follow-up 3 years after imaging confirmed that none of these patients had developed clinical signs of classical PD. Therefore, the combination of clinical assessment, dopaminergic imaging and network analysis in FDG PET may yield better diagnostic accuracy in clinical trials recruiting patients with early PD. Network evolution during disease progression in PD In addition to aiding in differential diagnosis, an ideal biomarker measures the pathologic processes associated with clinical disease
Fig. 2. Spatial covariance patterns associated with atypical parkinsonian syndromes. Top: Metabolic pattern associated with multiple system atrophy (MSARP) characterized by covarying metabolic decreases in the putamen and the cerebellum (Eckert et al., 2008). Bottom: Metabolic pattern associated with progressive supranuclear palsy (PSPRP) characterized by covarying metabolic decreases in the medial prefrontal cortex (PFC), the frontal eye fields, the ventrolateral prefrontal cortex (VLPFC), the caudate nuclei, the medial thalamus, and the upper brainstem (Eckert et al., 2008). [The covariance patterns were overlaid on T1-weighted MR-template images. The displays represent regions that contributed significantly to the network and that were demonstrated to be reliable by bootstrap resampling. Voxels with negative region weights (metabolic decreases) are color-coded blue.]
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progression. In PD, such biomarkers can be used in conjunction with clinical assessment to evaluate the efficacy of a potential neuroprotective intervention. Despite similar underlying pathology, the clinical manifestations of PD and rates of clinical decline can vary between patient subtypes, such as tremor predominate and akinetic rigid forms (Jankovic and Kapadia, 2001). Even within clinical subtypes, individual rates of progression can vary depending on demographic factors such as age of onset (Alves et al., 2005). In clinical trials, these variations can make measures of disease progression difficult when based solely upon clinical assessment. For these reasons, imaging biomarkers are a potentially objective and accurate means of gauging disease progression. In a recent longitudinal study, we used FDG PET in conjunction with dopaminergic imaging and UPDRS assessments to follow the change in network PDRP and PDCP activity in patients with early PD (Huang et al., 2007b). Fifteen PD patients with symptoms for less than 2 years received FDG PET imaging at baseline, 24 months and 48 months. Each subject also underwent PET imaging with [18F]-fluoropropyl βCIT (FPCIT) to quantify longitudinal changes in caudate and putamen dopamine transporter (DAT) binding as a marker of presynaptic nigrostriatal dysfunction. We found that the development of PDRP expression over time occurred in parallel with progressive deterioration in UPDRS motor ratings and putamen DAT binding over all three time points (Fig. 3) (Huang et al., 2007b). Correlations between changes in PDRP activity, UPDRS motor score and DAT binding were significant, albeit of modest degree. No more than a third of the variability in any one of these progression biomarkers was explained by either of the two other biomarkers (Eckert et al., 2007b). Therefore, these progression measures cannot be viewed as interchangeable. Rather, each captures a unique feature of the neurodegenerative process. The complementary nature of the respective progression indices (motor ratings, dopaminergic dysfunction, and metabolic activity) suggests that together these approaches are a better way of assessing disease progression than each alone. In a longitudinal study with non-demented early onset PD patients, PDCP activity was assessed at baseline, 12 months and 24 months and, like the PDRP, was also found to progressively increase over the three time points (p b 0.0001) (Huang et al., 2007b). However, compared to PDRP activity, the changes in PDCP were
slower and only reached significant abnormality from baseline at 24 months. In addition, changes in PDCP activity did not correlate with concurrent declines in striatal DAT binding or increases in UPDRS motor ratings. Thus, the two network measures of disease progression, the PDRP and the PDCP, can be regarded as distinct. These findings suggest different underlying pathophysiological networks associated with motor and cognitive disease progression in PD, and support the use of the PDCP as a biomarker for assessment of cognitive changes in patients. Network modulation with PD treatment Biomarkers that quantify the clinical response to a therapeutic intervention can be used to assess the efficacy of therapies for PD and other movement disorders (Eckert and Eidelberg, 2005). The quantification of network activity during treatment may be used for objectively screening the effects of anti-parkinsonian therapy in small patient cohorts (e.g., Asanuma et al., 2006; Trošt et al., 2006; Feigin et al., 2007a). By comparing the changes in metabolic network activity during treatment, we can use these measures as biomarkers to identify the underlying pathological processes involved in the observed clinical changes. Application of this approach can be used in the development of novel PD treatments and in the assessment of intervention efficacy. Dopaminergic treatment Using FDG PET, changes in PDRP expression have been observed and quantified during dopaminergic therapy (Feigin et al., 2001a; Asanuma et al., 2006). The changes in both pallidal metabolism and PDRP network activity correlated significantly with clinical improvement in the UPDRS motor rating score during dopamine treatment. Interestingly, a recent study (Hirano et al., 2008) showed a highly significant dissociation between levodopa-mediated PDRP changes in cerebral blood flow and glucose metabolic scans . This phenomenon was accentuated in PD patients with levodopa-induced dyskinesias (LID), reflecting excessive dopaminergic vasodilation in these subjects. There was, however, no significant change in the cognitiverelated PDCP pattern (Huang et al., 2007a), which again suggests a different physiologic network associated with cognitive dysfunction in PD. Therefore, while PDRP measures progression and treatment of motor symptoms, the PDCP expression may be a useful marker of disease progression in clinical trials targeting the non-motor pathway. Stereotaxic interventions
Fig. 3. Longitudinal progression of PD-related pattern expression. Mean network activity at baseline, 24 and 48 months (Huang et al., 2007b). Values for the PD-related motor and cognitive spatial covariance patterns (PDRP and PDCP; see Fig. 1) were computed at each time point and displayed relative to the mean for 15 age-matched healthy subjects. Network activity increased significantly over time for both patterns (p b 0.0001; RMANOVA), with the PDRP progressing faster than the PDCP (p b 0.04). Relative to controls, PDRP activity in the patient group was elevated at all three time points, while PDCP activity reached abnormal levels only at the final time point. [Bars represent the standard error at each time point.] [Brain 130(Pt 7), 1834–1846 Copyright 2007 Oxford University Press].
In patients with PD, neurosurgical modulation of subthalamic nucleus (STN) activity has been used to mimic the clinical improvements seen with some pharmacologic interventions. Specifically, STN lesioning and deep brain stimulation (DBS) are thought to ameliorate the symptoms of PD through functional mechanisms similar to dopaminergic therapy (Vingerhoets et al., 2002; Krack et al., 2002). Moreover, the findings of a significant correlation between PDRP expression and intraoperative recordings of STN firing rate (Lin et al., 2008) suggest that the activity of this metabolic network may be modulated by surgical interventions in this region. Indeed, FDG PET studies have revealed sustained reductions in PDRP expression following therapeutic STN lesioning (Su et al., 2001; Trošt et al., 2003) as well as in two independent PD cohorts treated with STN DBS (Asanuma et al., 2006; Trošt et al., 2006; cf. Hirano et al., 2008). In these populations, treatment-induced changes in PDRP expression correlated with clinical changes in motor function. The effects of STN DBS on regional metabolism were generally similar to those identified following subthalamotomy, with metabolic reductions in the GPi and dorsal midbrain/pons (Trošt et al., 2006; Asanuma et al., 2006). Overall, the metabolic changes reported with
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treatment response under such conditions is to use metabolic brain networks like PDRP as in vivo biomarkers. In a phase I clinical trial, we used this approach to measure the safety, tolerability and potential efficacy of an adeno-associated virus (AAV) borne glutamic acid decarboxylase (GAD) gene transferred into the STN of patients with PD (Kaplitt et al., 2007; Feigin et al., 2007a). FDG PET was preformed on 12 patients with advanced PD before, 6 months after and 12 months after unilateral gene therapy of the STN. There were significant reductions in thalamic metabolism on the operated side as well as concurrent metabolic increases in ipsilateral motor and premotor cortical regions. The activity of the PDRP on the side of STN AAV-GAD infusion was significantly reduced at 6 months, with sustained reduction at 12 months (Fig. 4). The PDRP reduction correlated with improvement in the clinical motor ratings (Feigin et al., 2007a). By contrast, the activity of the cognitive-related network did not change after gene transfer. These results indicate the use of network analysis in imaging as biomarkers in the development of novel treatment strategies in PD and other neurodegenerative diseases. Fig. 4. Effect of STN gene therapy on PDRP activity. Changes in PDRP activity following unilateral subthalamic nucleus (STN) infusion of adeno-associated virus vector expressing glutamic acid decarboxylase (AAV-GAD). At the preoperative baseline, PDRP activity was significantly (p b 0.004; Student's t-test) elevated in the gene therapy trial participants relative to age-matched healthy volunteers. Progression-corrected values were computed to reflect the net effect of STN AAV-GAD on network activity for each subject and time point (Feigin et al., 2007a). Relative to baseline, these values declined following gene therapy (p b 0.001, RMANOVA), with significant reductions relative to baseline at both 6 (gray) and 12 (black) months. These changes correlated (p b 0.03) with clinical outcome over the course of the study. [⁎⁎p b 0.005, Bonferroni tests; bars represent standard error.] [Proc. Natl. Acad. Sci. U. S. A. 104, 19559-19564 Copyright 2007 National Academy of Sciences, U.S.A.]
STN DBS reflect normalization of the regional changes observed in untreated PD patients. Specifically, STN stimulation has been found to increase metabolism in parietal association cortex and, to a lesser degree, in the prefrontal cortex (Trošt et al., 2006; Asanuma et al., 2006), regions with significant reduced metabolism in untreated PD (Moeller et al., 1999; cf. Eckert et al., 2007b). This suggests that the baseline metabolic reductions observed in these cortical regions are to some degree reversible. This may be attributable to a correction of overactive signaling in inhibitory pallidal projections to a variety of thalamic relay nuclei (Taktakishvili et al., 2002). Interestingly, the effects of levodopa and STN DBS on PDRP expression were not found to be additive (Asanuma et al., 2006). The data from two patients treated with both therapies revealed that it is unlikely for combination therapy to lower network activity beyond a naturally defined “floor” determined by the effects of therapeutic STN lesioning. Similarly, a recent study addressed STN “microlesion effects” on PDRP expression, i.e., changes in network activity subsequent to STN electrode insertion without stimulation (Pourfar et al., 2008). Significant regional metabolic effects of electrode implantation were seen in the putamen, GP, and ventral thalamus, areas receiving direct or indirect input from the STN. These localized changes were smaller than seen with therapeutic STN lesioning and were not associated with significant PDRP modulation or clinical benefit as observed with subthalamotomy procedures (Su et al., 2001; Trošt et al., 2006). These findings suggest that upper and lower thresholds exist for therapeutic PDRP modulation. These bounds may define the minimum and maximum functional changes that are achievable with current interventions. Whether interventions at other key PDRP nodes like the pedunculopontine nucleus (PPN) (Stefani et al., 2007) can further reduce network activity is a topic of ongoing investigation. Gene therapy is a novel form of treatment for neurodegenerative diseases, including PD (Feigin and Eidelberg, 2007). However, past concerns for safety have prompted careful study design and assessment of safety and tolerability in clinical trials. Phase I trials for safety use small unblinded patient cohorts, making clinical assessment for efficacy quite challenging in early trials. One method to assess
Network analysis in other movement disorders Among movement disorders, PD is unique in that the pathological and neurochemical changes associated with disease are fairly discrete and well defined. The hallmark of PD is the loss of dopaminergic neurons in the substantia nigra with much of the motor dysfunction due to the downstream effects of nigral degeneration. Therefore, neuroimaging assessments of nigrostriatal dopaminergic function have been a traditional focus of research in this area. By contrast, other movement disorders are not associated with specific, highly localized neurochemical abnormalities that can be measured in vivo with imaging techniques. However, disease-related metabolic patterns can act as biomarkers of diagnosis and treatment in situations in which other imaging approaches are insufficient. Indeed, FDG PET has been
Fig. 5. Huntington's disease-related spatial covariance pattern (HDRP). This metabolic pattern was characterized by relative metabolic decreases in the striatum and cingulate cortex, associated with relative increases in the ventral thalamus, motor cortex (BA 4), and occipital lobe (BA 17, 18) (Feigin et al., 2007b). [The covariance patterns were overlaid on T1-weighted MR-template images. The display represents voxels that contribute significantly to the network and that were demonstrated to be reliable by bootstrap resampling procedures. Voxels with positive region weights (metabolic increases) are color-coded from red to yellow; those with negative region weights (metabolic decreases) are color-coded from blue to purple.] [Brain 130 (Pt 11), 2858– 2867 Copyright of Oxford University Press].
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used to identify specific, reproducible metabolic networks associated with Huntington's disease (Feigin et al., 2001b, 2007b), idiopathic torsion dystonia (Eidelberg, 1998a; Trošt et al., 2002), and Alzheimer's disease (Habeck et al., 2008). Huntington's disease Huntington's disease (HD) is a hereditary neurodegenerative disorder characterized by progressive development of involuntary movements and cognitive abnormalities. Due to commercially available genetic testing and autosomal dominant inheritance pattern, future patients can be identified and potentially treated to delay or prevent the onset of symptoms. In order to assess the efficacy of therapies in the preclinical phase of the illness, biomarkers are needed that accurately reflect the severity and the progression of disease. Using network analysis in FDG PET, we identified a Huntington's disease relatedpattern (HDRP) that is characterized by relative hypometabolism of the caudate, lentiform nuclei and mesial temporal cortex with relative metabolic increases in the occipital cortex (Feigin et al., 2007b; cf. 2001b) (Fig. 5). In a 44 month longitudinal study of preclinical HD carriers, we found that network expression increased over time in the early preclinical phase of the disease, but declined as HD gene carriers approached symptom onset (Feigin et al., 2007b). This non-linear trajectory of HDRP progression in the preclinical period may represent a compensatory process that is attenuated as symptom onset nears. Further studies in pre-symptomatic HD gene carriers are needed to understand the earliest changes detectable with this imaging technique. Idiopathic torsion dystonia Dystonia is a movement disorder characterized by sustained muscle contractions with twisting and repetitive movements or abnormal postures (Fahn et al., 1998). Idiopathic torsion dystonia has been linked to several genetic mutations; however a highly variable clinical spectrum and lack of consistent histopathology have made the development and evaluation of new treatments difficult (Zeman, 1970; Walker et al., 2002). Using FDG PET, we have described a reproducible pattern of abnormal brain glucose utilization associated with primary dystonia (Trošt et al., 2002; cf. Eidelberg et al., 1998). The torsion dystonia-related pattern (TDRP) is characterized by hypermetabolism of the basal ganglia, cerebellum and SMA (Eidelberg, 1998b). This pattern has been described in two independent cohorts of nonmanifesting carriers as well as DYT1 patients with clinical symptoms (Trošt et al., 2002). Moreover, the TDRP is not specific for the DYT1 genotype. It has been identified in both manifesting and nonmanifesting carriers of the DYT6 dystonia haplotype (Trošt et al., 2002), and in patients with sporadic essential blepharospasm (Hutchinson et al., 2000). It is likely that this pattern represents a metabolic trait of dystonia and could therefore be used as a marker in linkage studies to identify potential gene carriers among family members of dystonia patients. In addition, this brain-related metabolic network could be used as a marker of disease in therapeutic intervention trials. How the TDRP is modulated with various medical and surgical treatments, such as DBS, is an area of current investigation. Future directions The identification of disease-related metabolic networks as biomarkers in movement disorders is a rather new approach. Network analysis is a powerful tool in the diagnosis of parkinsonian patients, assessment of clinical progression, and in the evaluation of new therapeutic interventions for PD and related disorders. Preliminary studies support the use of automated pattern recognition routines to distinguish PD from atypical forms of parkinsonism. However, further validation of this novel approach is needed prior to use in clinical trials of disease-modifying agents. Longitudinal network imaging studies
are also needed in atypical forms of parkinsonism, such as MSA and PSP. Such data would determine if clinical progression of atypical parkinsonism can be measured by specific metabolic patterns for these conditions, and whether the expression of these patterns can be modulated by treatment. Finally, the assessment of disease-related metabolic networks is applicable to other movement disorders, such as Tourette Syndrome (cf. Eidelberg et al., 1997a). These brain networks are likely to aid in understanding the underlying pathophysiology of these disorders and in developing novel targets for therapy. In aggregate, network imaging is likely to improve the accuracy of diagnosis and facilitate the development of new therapies for patients with movement disorders. References Alves, G., Wentzel-Larsen, T., Aarsland, D., Larsen, J.P., 2005. Progression of motor impairment and disability in Parkinson disease: a population-based study. Neurology 65, 1436–1441. 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Contents lists available at ScienceDirect
Neurobiology of Disease j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n b d i
Review
Network biomarkers for the diagnosis and treatment of movement disorders Kathleen L. Poston a,b, David Eidelberg a,c,⁎ a b c
Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, NY, USA Department of Neurology, Columbia University Medical Center, New York, NY, USA Departments of Neurology and Medicine, North Shore University Hospital and New York University School of Medicine, New York, NY, USA
a r t i c l e
i n f o
Article history: Received 5 August 2008 Revised 25 September 2008 Accepted 30 September 2008 Available online 1 November 2008 Keywords: Positron emission topography (PET) Parkinsonism Movement disorders Biomarkers Brain metabolism Differential diagnosis Treatment response
a b s t r a c t Functional brain networks provide a set of useful biomarkers for the assessment of movement disorders such as Parkinson's disease (PD). Spatial covariance analysis of imaging data from PD patients has led to the identification of abnormal metabolic patterns associated with the motor and cognitive features of this disease. Measurements of pattern expression have been used for diagnosis, assessment of rates of disease progression, and objective evaluation of the efficacy of therapeutic interventions. For instance, the recent identification of new disease-specific patterns for Multiple System Atrophy (MSA) and Progressive Supranuclear Palsy (PSP) has improved diagnostic accuracy in patients with parkinsonian syndromes. Further, disease-related networks have been found to be modulated by novel treatment strategies such as gene therapy. Finally, the application of network analysis to the study of inherited movement disorders such as Huntington's disease can aid in the assessment of disease-modifying therapies in pre-symptomatic gene mutation carriers. © 2008 Elsevier Inc. All rights reserved.
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metabolic networks in Parkinson's disease . . . . . . . . . . . . . . . The PD-related motor pattern (PDRP) . . . . . . . . . . . . . . . The PD-related cognitive pattern (PDCP) . . . . . . . . . . . . . . Using network analysis in the differential diagnosis of Parkinsonism . Network evolution during disease progression in PD . . . . . . . . . . Network modulation with PD treatment . . . . . . . . . . . . . . . . Dopaminergic treatment . . . . . . . . . . . . . . . . . . . . . Stereotaxic interventions . . . . . . . . . . . . . . . . . . . . . Network analysis in other movement disorders. . . . . . . . . . . . . Huntington's disease . . . . . . . . . . . . . . . . . . . . . . . Idiopathic torsion dystonia . . . . . . . . . . . . . . . . . . . . Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Introduction
⁎ Corresponding author. Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, 350 Community Drive, Manhasset, NY 11030, USA. Fax: +1 516 562 1008. E-mail address: [email protected] (D. Eidelberg). Available online on ScienceDirect (www.sciencedirect.com). 0969-9961/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.nbd.2008.09.026
Neurodegenerative diseases characterized by involuntary movements constitute a group of frequently diagnosed conditions in neurological clinics. Current practice relies on clinical assessment and follow-up for the diagnosis and treatment of these disorders. However, postmortem studies show only 76% diagnostic accuracy for patients thought to have Parkinson's disease (PD) by clinical evaluation (Hughes et al., 1992). Indeed, differentiating between parkinsonian disorders on clinical grounds alone has proven unsatisfactory, particularly early after
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symptom onset (Litvan et al., 1998). In approximately one-third of patients the initial diagnosis changes by the fifth year of symptoms; therefore the best diagnostic accuracy is achieved after follow-up assessment by a trained movement disorders specialist (Hughes et al., 2002). Early differential diagnosis in patients with movement disorders is important for both prognosis and decisions related to treatment options. Disease-specific biomarkers would be beneficial to aid in clinical diagnosis for patients with new symptoms and for those with more complicated symptoms. In addition, with the development of neuroprotective treatments in PD there is a critical need to identify biomarkers for use in clinical trials. One current challenge in the field of movement disorders is to validate sensitive and reliable biomarkers to diagnose various parkinsonian disorders, assess clinical progression, and evaluate therapeutic interventions. Metabolic brain imaging with 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) has become an important biomarker for the study of movement disorders. The application of network image analysis to FDG PET provides valuable information concerning abnormal functional connectivity in neurodegenerative disorders. By mapping glucose metabolism at a voxel level, this imaging approach provides a measure of regional synaptic activity (Eidelberg et al., 1997b). Localized pathology can therefore alter functional connectivity across the entire brain in a disease-specific manner. Spatial covariance methods have been used extensively to identify these abnormalities at the network level in PD and other neurodegenerative disorders (Eckert and Eidelberg, 2005; Eckert et al., 2007b; cf. Habeck et al., 2008).
dementia in PD can range from 17% to 43% (Riedel et al., 2008), mild cognitive deficits can be present much earlier (Caviness et al., 2007). A voxel-based spatial covariance approach with FDG PET has been used to identify a distinct PD-related metabolic pattern associated with cognitive dysfunction in non-demented patients (Huang et al., 2007a). The PD-related cognitive pattern (PDCP) is characterized by metabolic reductions in medial frontal and parietal association regions with relative increases in the cerebellar vermis and dentate nuclei (Fig. 1, right). It is statistically unrelated (i.e., orthogonal) to the PDRP motor network (Huang et al., 2007a). Correlations between PDCP expression and neuropsychological test performance are significant on tests of executive functioning (Huang et al., 2007a). Indeed, the expression of this pattern proved to be elevated in PD patients with mild cognitive impairment (MCI) relative to their cognitively normal counterparts (Huang et al., 2008). Like the PDRP, the PDCP pattern has been found to be highly reproducible in individual patients. However, unlike the PDRP, PDCP activity is not modulated by symptomatic treatment with levodopa or STN stimulation (Huang et al., 2007a; cf. Hirano et al., 2008). Moreover, the time course of PDCP expression in early stage disease is different from that of the PDRP (Huang et al., 2007b), with a slower, non-linear trajectory that is unrelated to concurrent changes in motor function or striatal dopamine transporter (DAT) binding (Eckert et al., 2007b). These findings suggest the PDCP can be used as a complementary network biomarker with specificity for early cognitive dysfunction in patients with PD.
Metabolic networks in Parkinson's disease
Accurate diagnosis based solely on clinical symptoms can be challenging in patients with an atypical parkinsonian syndrome, particularly early in the disease course. Multiple System Atrophy (MSA) and Progressive Supranuclear Palsy (PSP) are two of the most common forms of atypical parkinsonism and are found on neuropathological examination in 5% to 10% of patients with a clinical diagnosis of PD (Schrag et al., 1999; Hughes et al., 2002). These atypical forms are characterized by more rapid progression of
The PD-related motor pattern (PDRP) PET can be used to study the pathophysiology of PD and to assess the treatment of this disease. In patients with the hallmark motor features of PD, network analysis of FDG PET data consistently reveal the presence of an abnormal spatial covariance pattern involving metabolic changes at key nodes of the cortico-striato-pallidothalamocortical (CSPTC) loops and related pathways (Eidelberg et al., 1994; 1997b; Moeller et al., 1999; cf. Ma et al., 2007). This Parkinson's disease-related pattern (PDRP) is characterized by increased pallido-thalamic and pontine metabolic activity associated with relative reductions in premotor cortex, supplementary motor area (SMA), and in parietal association regions (Fig. 1, left). To date, the PDRP metabolic network has been detected in seven independent patient populations scanned using vastly different techniques (e.g., Moeller et al., 1999; Feigin et al., 2002; Lozza et al., 2004; cf. Eckert et al., 2007b). Its expression in individual subjects is highly reproducible, with stable network activity recorded over hours to weeks (Ma et al., 2007). PDRP scores have also been used to discriminate between patients with PD and atypical parkinsonism (Ma et al., 2007; Eckert et al., 2007a, 2008) and patients with essential tremor (S. Hirano and I.U. Isaias, personal communication). In PD patients, pattern expression has been found consistently to correlate with standardized motor ratings (e.g., Asanuma et al., 2006; cf. Eidelberg et al., 1994, 1995), symptom duration (Moeller and Eidelberg, 1997; Huang et al., 2007b), and intraoperative measurements of subthalamic nucleus (STN) firing rate (Lin et al., 2008). These studies support the use of PDRP as a biomarker to differentiate PD patients from patients with atypical parkinsonism, and to assess longitudinal changes in disease-related motor disability (see below). The PD-related cognitive pattern (PDCP) In addition to motor symptoms, PET has been used to study the cognitive changes associated with PD. While the prevalence of
Using network analysis in the differential diagnosis of Parkinsonism
Fig. 1. Parkinson's disease-related spatial covariance patterns. Left: PD-related motor pattern (PDRP) characterized by pallidothalamic, pontine, and motor cortical hypermetabolism, associated with relative metabolic reductions in the lateral premotor and posterior parietal areas (Ma et al., 2007). Right: PD-related cognitive pattern (PDCP) characterized by hypometabolism of prefrontal cortex, rostral supplementary motor area, and superior parietal regions (Huang et al., 2007a). [Both covariance patterns were overlaid on T1-weighted MR-template images. The displays represent voxels that contributed significantly to each covariance pattern and that were found to be reliable on bootstrap resampling. Relative metabolic increases are displayed in red; relative metabolic decreases are displayed in blue. The left hemisphere was cut in the transverse plane at z = − 5 mm. The right hemisphere was displayed as a surface projection on the same brain template.] [J. Neurosci. 28, 4201–4209 Copyright 2008 By the Society for Neuroscience].
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symptoms and very poor response to medications (Litvan et al., 2003; Geser et al., 2006). Using FDG PET and voxel-based spatial covariance analysis, we have identified characteristic patterns of regional glucose metabolism in patients with MSA and PSP (Eckert et al., 2005; Eckert and Edwards, 2007). By applying strictly defined statistical criteria to the imaging data to identify disease-related patterns (Eckert et al., 2008; cf. Habeck et al., 2008), we found that MSA and PSP were each associated with specific and highly stable metabolic brain networks. The MSArelated pattern (MSARP) is characterized by bilateral metabolic decreases in the putamen and the cerebellum (Fig. 2, top). By contrast, the PSP-related pattern (PSPRP) is characterized by metabolic decreases predominately in the upper brainstem and medial prefrontal cortex as well as in the medial thalamus, the caudate nuclei, the anterior cingulate area, the ventrolateral prefrontal cortex and in the frontal eye fields (Fig. 2, bottom). In both diseases, pattern expression was significantly elevated (p b 0.001) in patients relative to age-matched healthy control subjects (Eckert et al., 2008). In addition, pattern expression was also elevated (p N 0.001) in two independent patient groups when compared to a prospectively scanned healthy control group. Achieving a high sensitivity in separating patients from normal subjects suggests potential utility of these patterns as functional imaging biomarkers for MSA and PSP. Due to the deficient treatment response and the overall poor outcome in atypical forms of parkinsonism, the differentiation of parkinsonian disorders is crucial for appropriate therapeutic decisions and prognostic counseling. As part of an ongoing study, we developed a fully automated algorithm that is blind to clinical assessment to diagnose individual cases (Spetsieris et al., 2006). We quantified the expression of the PDRP, MSARP and PSPRP in patients with clinical parkinsonism that was otherwise undiagnosed at the time of the scan.
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Indeed, the use of disease-related networks yielded highly accurate (approximately 90%) differentiation between PD and atypical parkinsonism (Eckert and Edwards, 2007). Further studies on larger patient cohorts with clinical and pathological diagnostic follow-up are currently in progress. Accurate diagnosis is essential for patient recruitment in PD clinical trials, particularly with the development of potential neuroprotective therapies. In prior studies, dopaminergic imaging has been found to be normal in approximately 15% of parkinsonian patients enrolled in clinical trials (The Parkinson Study Group, 2002; Whone et al., 2003; Fahn et al., 2004). Such patients have been termed “scans without evidence of dopaminergic deficit” (SWEDD) (Marek et al., 2005). FDG PET can be used as a biomarker to aid in discrimination of PD and atypical parkinsonian disorders, particularly when dopamine imaging is normal. Using network analysis in FDG PET, we studied eight parkinsonian patients with normal 18F-fluorodopa (F-DOPA) PET who fit the definition for SWEDD. These subjects had further imaging with FDG PET to aid in differential diagnosis (Eckert et al., 2007a). In these subjects, the patterns of metabolic abnormality were inconsistent with classical PD, MSA and PSP. Network quantification revealed that as opposed to patients with classical PD, PDRP expression was not elevated in these patients. Follow-up 3 years after imaging confirmed that none of these patients had developed clinical signs of classical PD. Therefore, the combination of clinical assessment, dopaminergic imaging and network analysis in FDG PET may yield better diagnostic accuracy in clinical trials recruiting patients with early PD. Network evolution during disease progression in PD In addition to aiding in differential diagnosis, an ideal biomarker measures the pathologic processes associated with clinical disease
Fig. 2. Spatial covariance patterns associated with atypical parkinsonian syndromes. Top: Metabolic pattern associated with multiple system atrophy (MSARP) characterized by covarying metabolic decreases in the putamen and the cerebellum (Eckert et al., 2008). Bottom: Metabolic pattern associated with progressive supranuclear palsy (PSPRP) characterized by covarying metabolic decreases in the medial prefrontal cortex (PFC), the frontal eye fields, the ventrolateral prefrontal cortex (VLPFC), the caudate nuclei, the medial thalamus, and the upper brainstem (Eckert et al., 2008). [The covariance patterns were overlaid on T1-weighted MR-template images. The displays represent regions that contributed significantly to the network and that were demonstrated to be reliable by bootstrap resampling. Voxels with negative region weights (metabolic decreases) are color-coded blue.]
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progression. In PD, such biomarkers can be used in conjunction with clinical assessment to evaluate the efficacy of a potential neuroprotective intervention. Despite similar underlying pathology, the clinical manifestations of PD and rates of clinical decline can vary between patient subtypes, such as tremor predominate and akinetic rigid forms (Jankovic and Kapadia, 2001). Even within clinical subtypes, individual rates of progression can vary depending on demographic factors such as age of onset (Alves et al., 2005). In clinical trials, these variations can make measures of disease progression difficult when based solely upon clinical assessment. For these reasons, imaging biomarkers are a potentially objective and accurate means of gauging disease progression. In a recent longitudinal study, we used FDG PET in conjunction with dopaminergic imaging and UPDRS assessments to follow the change in network PDRP and PDCP activity in patients with early PD (Huang et al., 2007b). Fifteen PD patients with symptoms for less than 2 years received FDG PET imaging at baseline, 24 months and 48 months. Each subject also underwent PET imaging with [18F]-fluoropropyl βCIT (FPCIT) to quantify longitudinal changes in caudate and putamen dopamine transporter (DAT) binding as a marker of presynaptic nigrostriatal dysfunction. We found that the development of PDRP expression over time occurred in parallel with progressive deterioration in UPDRS motor ratings and putamen DAT binding over all three time points (Fig. 3) (Huang et al., 2007b). Correlations between changes in PDRP activity, UPDRS motor score and DAT binding were significant, albeit of modest degree. No more than a third of the variability in any one of these progression biomarkers was explained by either of the two other biomarkers (Eckert et al., 2007b). Therefore, these progression measures cannot be viewed as interchangeable. Rather, each captures a unique feature of the neurodegenerative process. The complementary nature of the respective progression indices (motor ratings, dopaminergic dysfunction, and metabolic activity) suggests that together these approaches are a better way of assessing disease progression than each alone. In a longitudinal study with non-demented early onset PD patients, PDCP activity was assessed at baseline, 12 months and 24 months and, like the PDRP, was also found to progressively increase over the three time points (p b 0.0001) (Huang et al., 2007b). However, compared to PDRP activity, the changes in PDCP were
slower and only reached significant abnormality from baseline at 24 months. In addition, changes in PDCP activity did not correlate with concurrent declines in striatal DAT binding or increases in UPDRS motor ratings. Thus, the two network measures of disease progression, the PDRP and the PDCP, can be regarded as distinct. These findings suggest different underlying pathophysiological networks associated with motor and cognitive disease progression in PD, and support the use of the PDCP as a biomarker for assessment of cognitive changes in patients. Network modulation with PD treatment Biomarkers that quantify the clinical response to a therapeutic intervention can be used to assess the efficacy of therapies for PD and other movement disorders (Eckert and Eidelberg, 2005). The quantification of network activity during treatment may be used for objectively screening the effects of anti-parkinsonian therapy in small patient cohorts (e.g., Asanuma et al., 2006; Trošt et al., 2006; Feigin et al., 2007a). By comparing the changes in metabolic network activity during treatment, we can use these measures as biomarkers to identify the underlying pathological processes involved in the observed clinical changes. Application of this approach can be used in the development of novel PD treatments and in the assessment of intervention efficacy. Dopaminergic treatment Using FDG PET, changes in PDRP expression have been observed and quantified during dopaminergic therapy (Feigin et al., 2001a; Asanuma et al., 2006). The changes in both pallidal metabolism and PDRP network activity correlated significantly with clinical improvement in the UPDRS motor rating score during dopamine treatment. Interestingly, a recent study (Hirano et al., 2008) showed a highly significant dissociation between levodopa-mediated PDRP changes in cerebral blood flow and glucose metabolic scans . This phenomenon was accentuated in PD patients with levodopa-induced dyskinesias (LID), reflecting excessive dopaminergic vasodilation in these subjects. There was, however, no significant change in the cognitiverelated PDCP pattern (Huang et al., 2007a), which again suggests a different physiologic network associated with cognitive dysfunction in PD. Therefore, while PDRP measures progression and treatment of motor symptoms, the PDCP expression may be a useful marker of disease progression in clinical trials targeting the non-motor pathway. Stereotaxic interventions
Fig. 3. Longitudinal progression of PD-related pattern expression. Mean network activity at baseline, 24 and 48 months (Huang et al., 2007b). Values for the PD-related motor and cognitive spatial covariance patterns (PDRP and PDCP; see Fig. 1) were computed at each time point and displayed relative to the mean for 15 age-matched healthy subjects. Network activity increased significantly over time for both patterns (p b 0.0001; RMANOVA), with the PDRP progressing faster than the PDCP (p b 0.04). Relative to controls, PDRP activity in the patient group was elevated at all three time points, while PDCP activity reached abnormal levels only at the final time point. [Bars represent the standard error at each time point.] [Brain 130(Pt 7), 1834–1846 Copyright 2007 Oxford University Press].
In patients with PD, neurosurgical modulation of subthalamic nucleus (STN) activity has been used to mimic the clinical improvements seen with some pharmacologic interventions. Specifically, STN lesioning and deep brain stimulation (DBS) are thought to ameliorate the symptoms of PD through functional mechanisms similar to dopaminergic therapy (Vingerhoets et al., 2002; Krack et al., 2002). Moreover, the findings of a significant correlation between PDRP expression and intraoperative recordings of STN firing rate (Lin et al., 2008) suggest that the activity of this metabolic network may be modulated by surgical interventions in this region. Indeed, FDG PET studies have revealed sustained reductions in PDRP expression following therapeutic STN lesioning (Su et al., 2001; Trošt et al., 2003) as well as in two independent PD cohorts treated with STN DBS (Asanuma et al., 2006; Trošt et al., 2006; cf. Hirano et al., 2008). In these populations, treatment-induced changes in PDRP expression correlated with clinical changes in motor function. The effects of STN DBS on regional metabolism were generally similar to those identified following subthalamotomy, with metabolic reductions in the GPi and dorsal midbrain/pons (Trošt et al., 2006; Asanuma et al., 2006). Overall, the metabolic changes reported with
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treatment response under such conditions is to use metabolic brain networks like PDRP as in vivo biomarkers. In a phase I clinical trial, we used this approach to measure the safety, tolerability and potential efficacy of an adeno-associated virus (AAV) borne glutamic acid decarboxylase (GAD) gene transferred into the STN of patients with PD (Kaplitt et al., 2007; Feigin et al., 2007a). FDG PET was preformed on 12 patients with advanced PD before, 6 months after and 12 months after unilateral gene therapy of the STN. There were significant reductions in thalamic metabolism on the operated side as well as concurrent metabolic increases in ipsilateral motor and premotor cortical regions. The activity of the PDRP on the side of STN AAV-GAD infusion was significantly reduced at 6 months, with sustained reduction at 12 months (Fig. 4). The PDRP reduction correlated with improvement in the clinical motor ratings (Feigin et al., 2007a). By contrast, the activity of the cognitive-related network did not change after gene transfer. These results indicate the use of network analysis in imaging as biomarkers in the development of novel treatment strategies in PD and other neurodegenerative diseases. Fig. 4. Effect of STN gene therapy on PDRP activity. Changes in PDRP activity following unilateral subthalamic nucleus (STN) infusion of adeno-associated virus vector expressing glutamic acid decarboxylase (AAV-GAD). At the preoperative baseline, PDRP activity was significantly (p b 0.004; Student's t-test) elevated in the gene therapy trial participants relative to age-matched healthy volunteers. Progression-corrected values were computed to reflect the net effect of STN AAV-GAD on network activity for each subject and time point (Feigin et al., 2007a). Relative to baseline, these values declined following gene therapy (p b 0.001, RMANOVA), with significant reductions relative to baseline at both 6 (gray) and 12 (black) months. These changes correlated (p b 0.03) with clinical outcome over the course of the study. [⁎⁎p b 0.005, Bonferroni tests; bars represent standard error.] [Proc. Natl. Acad. Sci. U. S. A. 104, 19559-19564 Copyright 2007 National Academy of Sciences, U.S.A.]
STN DBS reflect normalization of the regional changes observed in untreated PD patients. Specifically, STN stimulation has been found to increase metabolism in parietal association cortex and, to a lesser degree, in the prefrontal cortex (Trošt et al., 2006; Asanuma et al., 2006), regions with significant reduced metabolism in untreated PD (Moeller et al., 1999; cf. Eckert et al., 2007b). This suggests that the baseline metabolic reductions observed in these cortical regions are to some degree reversible. This may be attributable to a correction of overactive signaling in inhibitory pallidal projections to a variety of thalamic relay nuclei (Taktakishvili et al., 2002). Interestingly, the effects of levodopa and STN DBS on PDRP expression were not found to be additive (Asanuma et al., 2006). The data from two patients treated with both therapies revealed that it is unlikely for combination therapy to lower network activity beyond a naturally defined “floor” determined by the effects of therapeutic STN lesioning. Similarly, a recent study addressed STN “microlesion effects” on PDRP expression, i.e., changes in network activity subsequent to STN electrode insertion without stimulation (Pourfar et al., 2008). Significant regional metabolic effects of electrode implantation were seen in the putamen, GP, and ventral thalamus, areas receiving direct or indirect input from the STN. These localized changes were smaller than seen with therapeutic STN lesioning and were not associated with significant PDRP modulation or clinical benefit as observed with subthalamotomy procedures (Su et al., 2001; Trošt et al., 2006). These findings suggest that upper and lower thresholds exist for therapeutic PDRP modulation. These bounds may define the minimum and maximum functional changes that are achievable with current interventions. Whether interventions at other key PDRP nodes like the pedunculopontine nucleus (PPN) (Stefani et al., 2007) can further reduce network activity is a topic of ongoing investigation. Gene therapy is a novel form of treatment for neurodegenerative diseases, including PD (Feigin and Eidelberg, 2007). However, past concerns for safety have prompted careful study design and assessment of safety and tolerability in clinical trials. Phase I trials for safety use small unblinded patient cohorts, making clinical assessment for efficacy quite challenging in early trials. One method to assess
Network analysis in other movement disorders Among movement disorders, PD is unique in that the pathological and neurochemical changes associated with disease are fairly discrete and well defined. The hallmark of PD is the loss of dopaminergic neurons in the substantia nigra with much of the motor dysfunction due to the downstream effects of nigral degeneration. Therefore, neuroimaging assessments of nigrostriatal dopaminergic function have been a traditional focus of research in this area. By contrast, other movement disorders are not associated with specific, highly localized neurochemical abnormalities that can be measured in vivo with imaging techniques. However, disease-related metabolic patterns can act as biomarkers of diagnosis and treatment in situations in which other imaging approaches are insufficient. Indeed, FDG PET has been
Fig. 5. Huntington's disease-related spatial covariance pattern (HDRP). This metabolic pattern was characterized by relative metabolic decreases in the striatum and cingulate cortex, associated with relative increases in the ventral thalamus, motor cortex (BA 4), and occipital lobe (BA 17, 18) (Feigin et al., 2007b). [The covariance patterns were overlaid on T1-weighted MR-template images. The display represents voxels that contribute significantly to the network and that were demonstrated to be reliable by bootstrap resampling procedures. Voxels with positive region weights (metabolic increases) are color-coded from red to yellow; those with negative region weights (metabolic decreases) are color-coded from blue to purple.] [Brain 130 (Pt 11), 2858– 2867 Copyright of Oxford University Press].
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used to identify specific, reproducible metabolic networks associated with Huntington's disease (Feigin et al., 2001b, 2007b), idiopathic torsion dystonia (Eidelberg, 1998a; Trošt et al., 2002), and Alzheimer's disease (Habeck et al., 2008). Huntington's disease Huntington's disease (HD) is a hereditary neurodegenerative disorder characterized by progressive development of involuntary movements and cognitive abnormalities. Due to commercially available genetic testing and autosomal dominant inheritance pattern, future patients can be identified and potentially treated to delay or prevent the onset of symptoms. In order to assess the efficacy of therapies in the preclinical phase of the illness, biomarkers are needed that accurately reflect the severity and the progression of disease. Using network analysis in FDG PET, we identified a Huntington's disease relatedpattern (HDRP) that is characterized by relative hypometabolism of the caudate, lentiform nuclei and mesial temporal cortex with relative metabolic increases in the occipital cortex (Feigin et al., 2007b; cf. 2001b) (Fig. 5). In a 44 month longitudinal study of preclinical HD carriers, we found that network expression increased over time in the early preclinical phase of the disease, but declined as HD gene carriers approached symptom onset (Feigin et al., 2007b). This non-linear trajectory of HDRP progression in the preclinical period may represent a compensatory process that is attenuated as symptom onset nears. Further studies in pre-symptomatic HD gene carriers are needed to understand the earliest changes detectable with this imaging technique. Idiopathic torsion dystonia Dystonia is a movement disorder characterized by sustained muscle contractions with twisting and repetitive movements or abnormal postures (Fahn et al., 1998). Idiopathic torsion dystonia has been linked to several genetic mutations; however a highly variable clinical spectrum and lack of consistent histopathology have made the development and evaluation of new treatments difficult (Zeman, 1970; Walker et al., 2002). Using FDG PET, we have described a reproducible pattern of abnormal brain glucose utilization associated with primary dystonia (Trošt et al., 2002; cf. Eidelberg et al., 1998). The torsion dystonia-related pattern (TDRP) is characterized by hypermetabolism of the basal ganglia, cerebellum and SMA (Eidelberg, 1998b). This pattern has been described in two independent cohorts of nonmanifesting carriers as well as DYT1 patients with clinical symptoms (Trošt et al., 2002). Moreover, the TDRP is not specific for the DYT1 genotype. It has been identified in both manifesting and nonmanifesting carriers of the DYT6 dystonia haplotype (Trošt et al., 2002), and in patients with sporadic essential blepharospasm (Hutchinson et al., 2000). It is likely that this pattern represents a metabolic trait of dystonia and could therefore be used as a marker in linkage studies to identify potential gene carriers among family members of dystonia patients. In addition, this brain-related metabolic network could be used as a marker of disease in therapeutic intervention trials. How the TDRP is modulated with various medical and surgical treatments, such as DBS, is an area of current investigation. Future directions The identification of disease-related metabolic networks as biomarkers in movement disorders is a rather new approach. Network analysis is a powerful tool in the diagnosis of parkinsonian patients, assessment of clinical progression, and in the evaluation of new therapeutic interventions for PD and related disorders. Preliminary studies support the use of automated pattern recognition routines to distinguish PD from atypical forms of parkinsonism. However, further validation of this novel approach is needed prior to use in clinical trials of disease-modifying agents. Longitudinal network imaging studies
are also needed in atypical forms of parkinsonism, such as MSA and PSP. Such data would determine if clinical progression of atypical parkinsonism can be measured by specific metabolic patterns for these conditions, and whether the expression of these patterns can be modulated by treatment. Finally, the assessment of disease-related metabolic networks is applicable to other movement disorders, such as Tourette Syndrome (cf. Eidelberg et al., 1997a). These brain networks are likely to aid in understanding the underlying pathophysiology of these disorders and in developing novel targets for therapy. In aggregate, network imaging is likely to improve the accuracy of diagnosis and facilitate the development of new therapies for patients with movement disorders. References Alves, G., Wentzel-Larsen, T., Aarsland, D., Larsen, J.P., 2005. Progression of motor impairment and disability in Parkinson disease: a population-based study. Neurology 65, 1436–1441. 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