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. Author manuscript; available in PMC: 2026 Mar 20.
Published in final edited form as: Neurobiol Dis. 2023 Feb 21;179:106048. doi: 10.1016/j.nbd.2023.106048

Levodopa responsive freezing of gait is associated with reduced norepinephrine transporter binding in Parkinson’s disease

J Lucas McKay a,b,c, Jonathan Nye d,1, Felicia C Goldstein e, Barbara Sommerfeld a, Yoland Smith a,f, David Weinshenker g, Stewart A Factor a,*,1
PMCID: PMC13001024  NIHMSID: NIHMS2142694  PMID: 36813207

Abstract

Background:

Freezing of gait (FOG) is a major cause of falling in Parkinson’s disease (PD) and can be responsive or unresponsive to levodopa. Pathophysiology is poorly understood.

Objective:

To examine the link between noradrenergic systems, the development of FOG in PD and its responsiveness to levodopa.

Methods:

We examined norepinephrine transporter (NET) binding via brain positron emission tomography (PET) to evaluate changes in NET density associated with FOG using the high affinity selective NET antagonist radioligand [11C]MeNER (2S,3S)(2-[α-(2-methoxyphenoxy)benzyl]morpholine) in 52 parkinsonian patients. We used a rigorous levodopa challenge paradigm to characterize PD patients as non-freezing (NO-FOG, N = 16) levodopa responsive freezing (OFF-FOG, N = 10), and levodopa-unresponsive freezing (ONOFF-FOG, N = 21) and also included a non-PD FOG group, primary progressive freezing of gait (PP-FOG, N = 5).

Results:

Linear mixed models identified significant reductions in whole brain NET binding in the OFF-FOG group compared to the NO-FOG group (−16.8%, P = 0.021) and regionally in the frontal lobe, left and right thalamus, temporal lobe, and locus coeruleus, with the strongest effect in right thalamus (P = 0.038). Additional regions examined in a post hoc secondary analysis including the left and right amygdalae confirmed the contrast between OFF-FOG and NO-FOG (P = 0.003). A linear regression analysis identified an association between reduced NET binding in the right thalamus and more severe New FOG Questionnaire (N-FOG-Q) score only in the OFF-FOG group (P = 0.022).

Conclusion:

This is the first study to examine brain noradrenergic innervation using NET-PET in PD patients with and without FOG. Based on the normal regional distribution of noradrenergic innervation and pathological studies in the thalamus of PD patients, the implications of our findings suggest that noradrenergic limbic pathways may play a key role in OFF-FOG in PD. This finding could have implications for clinical subtyping of FOG as well as development of therapies.

Keywords: Parkinson’s disease, Freezing of gait, Norepinephrine transporter, Positron emission tomography, Norepinephrine, Primary progressive freezing of gait, Thalamus

1. Introduction

Freezing of gait (FOG), defined as paroxysmal episodic arrests in stepping when initiating gait, turning, and walking, is a common, disabling feature of Parkinson’s disease (PD) (Nutt et al., 2011). It is a major reason for falling with injury and leads to loss of independence and poor quality of life (Michalowska et al., 2005; Perez-Lloret et al., 2014). The pathophysiology is complex and likely includes extranigral nondopaminergic circuits (Bohnen et al., 2022). The potential contribution of norepinephrine (NE) to FOG has historically received a great deal of interest considering approval of a NE precursor drug in Japan for the treatment of this problem three decades ago. NE has widespread distribution in the brain and appears to play a key role in arousal, attention, stress responses, mood, and cognition (Benarroch, 2009; Borodovitsyna et al., 2020; Lewitt, 2012; Aston-Jones and Cohen, 2005).

The primary NE nucleus is the locus coeruleus (LC) which projects to subcortical and cortical regions, including frontal cortex, thalamus, hypothalamus, brainstem, amygdala, hippocampus, and cerebellum (Ordway et al., 1997; Tejani-Butt, 1992; Gross-Isseroff et al., 1988; Gaspar et al., 1991; Pifl et al., 2012). In PD, the LC shows α-synuclein pathology relatively early and ultimately undergoes catastrophic degeneration (Braak et al., 2003; Buddhala et al., 2015; Del Tredici et al., 2002; Zarow et al., 2003), resulting in decreased tissue NE levels in LC projection regions (Pifl et al., 2012; Weinshenker, 2018). Animal and patient studies indicate that NE loss may exacerbate motor symptoms of PD and contribute to dopamine neuron degeneration (Rommelfanger et al., 2004; Rommelfanger et al., 2007; Zhou et al., 2021; Bornert and Bouret, 2021; Song et al., 2019; Rommelfanger and Weinshenker, 2007). NE also impacts the therapeutic benefits of levodopa. This may be important in relation to FOG since the levodopa response is quite varied (McKay et al., 2019). We hypothesize that the impact of NE on FOG may relate to levodopa responsiveness.

The link between NE and FOG is primarily based on data showing that the synthetic NE precursor droxidopa significantly improved FOG in 25% of patients in a study from the 1980’s (Narabayashi et al., 1987; Fukada et al., 2013), with similar improvement reported in FOG related to atypical parkinsonism (Yamamoto et al., 1997). Other noradrenergic drugs also appear to be effective in some patients with FOG (Jankovic, 2009; Revuelta et al., 2015). Further, an association between FOG and NE deficit was shown in a study utilizing 6-[18F]fluoro-l-m-tyrosine (FMT) PET (a marker for catecholamine metabolism) which showed that decreased NE metabolism in LC was strongly correlated with self-reported FOG severity (Ono et al., 2016). Finally, the relationship between FOG and anxiety is also consistent with the link between FOG and NE (Giladi and Hausdorff, 2006; Ehgoetz Martens et al., 2016). FOG can be elicited in situations of elevated stress (Ehgoetz Martens et al., 2014), which normally increases LC activity, NE release and arousal in animal models (Borodovitsyna et al., 2020; Tillage et al., 2021; McCall et al., 2015; Lustberg et al., 2020). This suggests the hypothesis that a dysregulated LC-NE system may have aberrant responses to stress in PD that could create a state from which FOG episodes may emerge.

In this study we examined NE transporter (NET) binding using the high affinity, selective radioligand [11C]MeNER (2S,3S) (2-[α-(2-methoxyphenoxy) benzyl]morpholine) (Brumberg et al., 2019; Ding et al., 2005) via PET imaging to evaluate changes in NET density associated with FOG in PD. NET is a monoamine transporter located on the nerve terminals and the somatodendritic fields of NE neurons and serves as a proxy for LC terminal integrity. We characterized PD patients as FOG or no-FOG, and then FOG patients as responsive or non-responsive to levodopa using a rigorous levodopa challenge we previously developed (McKay et al., 2019) to determine the contribution of dopamine circuits as they related to their interaction with NE. We also included a non-PD FOG group (referred to as primary progressive freezing of gait (Factor et al., 2006)) considering the prior report of response to droxidopa. It is hypothesized that they will be similar to the unresponsive FOG group.

2. Materials and methods

2.1. Study population

The work described has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Study patients from our center provided written informed consent according to procedures approved by the Emory University Institutional Review Board. Inclusion criteria for PD were: Age ≥ 18 years; PD diagnosis defined by the United Kingdom Brain Bank criteria (Hughes et al., 1992); Hoehn & Yahr stage II-IV in the OFF state; levodopa responsive. Additional inclusion criteria for PD patients with FOG were: FOG noted in medical history and confirmed visually by examiner. Patients with primary progressive freezing gait (PP-FOG) were included based on previously reported criteria (Factor et al., 2006). Exclusion criteria included: previous treatment with dopamine receptor blocking agents; treatment with medications that interfere with NET-PET ligand binding including methylphenidate, atomoxetine, venlafaxine, β-adrenergic blockers; neurological or orthopedic disorders interfering with gait including vascular parkinsonism; dementia or other medical problems precluding completion of study protocol.

2.2. Levodopa challenge paradigm

The levodopa challenge paradigm details have been described previously (McKay et al., 2019). Briefly, we assessed patients in both the practically-defined “OFF” state (>12 h after antiparkinsonian medication intake) and in the full “ON” state after a levodopa equivalent dose (LED) of ~140% of their typical morning dose. The assessments included the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS-III) and a gait evaluation comprised of timed-up-and-go tests with and without a cognitive dual task and two rapid 360° turns in each direction to instigate the development of FOG, completed in our motion capture laboratory. As we were primarily interested in whether FOG was present in each medication state, we scored it based on MDS-UPDRS-III item 11. Serum levodopa levels were measured in both states. Additional scales were completed at the time of this visit including the MDS-UPDRS parts I, II and IV, Montreal Cognitive Assessment (MoCA), Beck Anxiety Inventory (BAI) and the New FOG Questionnaire (N-FOG-Q).

2.3. FOG group assignment

We classified each PD patient into one of three study groups based on history of the presence of FOG and on the MDS UPDRS-III FOG item (item 3.11) score in each of the “OFF” and “ON” states (McKay et al., 2019). Participants with no history of FOG who received a score of zero on item 11 in both states were classified as “no freezing” (“NO-FOG.”). Participants who received a nonzero score in the “OFF” state but a zero score in the “ON” state were classified as “OFF-FOG.” Participants who received a nonzero score in both states were classified as “ONOFF-FOG.” We classified patients with primary progressive freezing of gait as “PP-FOG.”

2.4. [11C]MeNER-PET Imaging

Patients were assessed with NET-PET imaging on a separate testing day. The typical interval between levodopa challenge testing and NET-PET imaging was 2.2 ± 2.8 months. Negative urine pregnancy tests were obtained ≤24 h prior to radiotracer injection for female subjects, unless there were no menses during the prior year. Patients maintained their typical medication schedule for NET-PET imaging. Adverse events were assessed with telephone follow-up.

2.4.1. Scanning procedure

[11C]MeNER was synthesized by methylation of (S,S)-2-(α-(2-Methoxyphenoxy)benzyl)morpholine (MeNER), an analogue of the selective norepinephrine reuptake inhibitor (S,S)-reboxetine, as previously described (Schou et al., 2003). The administered activity ranged from 570 to 832 MBq delivered intravenously with an average specific activity of 32 ± 12 GBq/μmol, resulting in an injected mass ranging between 3.4 and 17 μg.

2.4.2. Imaging procedures

PET data was collected on a High-Resolution Research Tomography scanner (CTI, Inc. Knoxville, TN) at the Emory Center for Systems Imaging. The PET system has a 2.5 mm spatial resolution and 10-fold higher sensitivity than current clinical PET cameras (de Jong et al., 2007). Subjects were administered [11C]MENeR intravenously as a bolus and remained in a quiet room for 60 min prior to 30 min of PET data collection. This simplified uptake protocol was chosen to increase patient compliance during PET and shown to correlate highly (r = 0.97) with full dynamic PET (Brumberg et al., 2019). PET data were divided into six frames, each five minutes duration and reconstructed with an ordinary Poisson ordered-subset expectation maximization algorithm (6 iterations, 16 subsets) including scatter and attenuation correction. Structural MRI images were acquired on a 3 T Siemens Prisma scanner with a 32-channel receiver array head coil. A standard whole-brain 3D T1-weighted MPRAGE sequence (Mugler 3rd., 1999; Lusebrink et al., 2013) (FOV = 256 mm; TR/TI/TE/FA = 2530 ms/1100 ms/3 ms/8°; 1 mm × 1 mm × 1 mm resolution; time of acquisition ~9 min) was acquired to provide anatomic detail and to normalize PET data to the standard Montreal Neurological Institute analysis space.

2.4.3. Image analysis

PET data were corrected for inter-subject motion and co-registered to structural MRI scans by mutual information metric optimization. MRI structural data were then spatially normalized to the Montreal Neurological Institute (MNI T2 152) template with SPM8’s segment option and default parameters. The inverse non-linear transform was used to map regions of interest from the Anatomical Automatic Labeling template (Tzourio-Mazoyer et al., 2002) including the frontal lobe, left and right thalamus, temporal lobe, LC and cerebellum. Regional standardized uptake value ratios (SUVr) were extracted from the reconstructed PET data normalized to the cerebellum. Numerical values ≥1.0 correspond to average expression greater than that observed in the cerebellum. We have shown that this approach is highly correlated with NET binding density with a structurally similar PET tracer analogue based on reboxitine (Adhikarla et al., 2016). Imaging technologists were blinded to study group codes.

2.5. Statistical analysis

All summary statistics are presented as mean (standard deviation) or frequency (percentage). Clinical and demographic variables were compared across study groups using univariate tests (Chi-squared, ANOVA). Variation in NET expression across study groups was assessed with a linear mixed model with fixed effects for study group (NO-FOG, OFF-FOG, ONOFF-FOG, and PP-FOG, with NO-FOG as the reference group) and brain region (frontal cortices, left thalamus, right thalamus, locus ceruleus, temporal lobes, with frontal cortices as the reference group) and a random effect for patient. Subsequent models evaluated sensitivity of main results to inclusion of covariates identified as marginally significant in univariate tests (sex, duration). PD duration was included as a covariate to control for overall disease progression and dopaminergic degeneration. Region-specific group effects were assessed with an additional linear mixed model with region by group interaction terms. Linear mixed models were implemented in LmerTest::lme4 in R software and fitted using restricted maximum likelihood. Linear regression models were applied to evaluate associations between NET expression and self-reported FOG severity (NFOG-Q) score in regions identified as varying with FOG group (stats::lm in R software). Additional sensitivity analyses and analyses of additional brain regions and analyses considering laterality of all regions were completed based on initial results and all are reported in detail in Supplemental Information. All statistical analyses were performed in R v4.1.1 at alpha = 0.05.

3. Results

3.1. Clinical and demographic characteristics

A total of N = 60 patients were enrolled and assessed with a levodopa challenge. Of these, PET data was obtained for 52: 16 were classified as NO-FOG, 10 as OFF-FOG, and 21 as ONOFF-FOG, 5 as PP-FOG. Clinical and demographic characteristics are presented in Table 1. N-FOGQ score and MDS-UPDRS-III FOG item scores were higher in the FOG participants compared to the NO-FOG participants, as expected from the design. Otherwise, no statistically significant differences in study variables were observed across study groups, with the exception of LED, which was highest in ONOFF-FOG. No statistically significant differences in frequency of missing data were observed across study groups (Supplemental Information).

Table 1.

Demographic and clinical characteristics of the study sample.

Variable NO-FOG (N = 16) OFF-FOG (N = 10) ONOFF-FOG (N = 21) PP-FOG (N = 5) Total (N = 52) P Value
Age 0.88
 Mean (SD) 67.3 (11.8) 68.5 (5.2) 68.6 (6.6) 65.6 (5.9) 67.9 (8.2)
Sex 0.05
 Female, N (%) 5 (31%) 1 (10%) 2 (10%) 3 (60%) 11 (21%)
 Male, N (%) 11 (69%) 9 (90%) 19 (90%) 2 (40%) 41 (79%)
Duration 0.08
 Mean (SD) 6.0 (3.7) 10.2 (5.4) 10.2 (6.9) 6.0 (3.3) 8.5 (5.8)
MDS-UPDRS-III (OFF) 0.45
 Mean (SD) 31.1 (13.6) 30.6 (10.7) 34.3 (10.8) 39.4 (7.8) 33.1 (11.5)
MDS-UPDRS-III (ON) 0.08
 Mean (SD) 18.4 (14.9) 15.9 (8.1) 20.5 (9.1) 31.6 (9.0) 20.0 (11.6)
LED < 0.01
 Mean (SD) 833 (313) 1149 (528) 1531 (524) 1258 (640) 1217 (552)
MoCA 0.30
 Mean (SD) 26.1 (3.9) 24.3 (4.0) 23.3 (5.0) 25.2 (4.3) 24.6 (4.5)
BAI 0.69
 Mean (SD) 8.4 (6.1) 10.5 (7.6) 10.4 (8.2)a 12.8 (10.6) 10.0 (7.6)b
NFOG-Q < 0.01
 Mean (SD) 0.0 (0.0) 16.7 (6.2) 21.2 (3.7) 17.8 (7.5) 13.5 (10.1)
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MDS-UPDRS-III, Unified Parkinson’s Disease Rating Scale, Movement Disorders Society revision; LED, levodopa equivalent dose; MoCA, Montreal Cognitive Assessment; BAI, Beck Anxiety Inventory.

a

N = 20;

b

N = 51.

3.2. NET binding

A similar pattern of variation in NET binding across groups was observed across all brain regions of interest (frontal lobe, left and right thalamus, temporal lobe, LC). A representative image of [11C]MENeR in a patient with ONOFF-FOG is shown in Fig. 1. The lowest NET SUVr levels were observed in the OFF-FOG group, followed by PP-FOG, ONOFF-FOG, and NO-FOG, respectively. Linear mixed models identified significant reductions in whole brain NET binding in the OFF-FOG group compared to the NO-FOG group (change in SUVr −16.8%, P = 0.021; Fig. 2). Additional contrasts tested post-hoc identified marginally increased NET binding in ONOFF-FOG vs. OFF-FOG (≈10%; P = 0.123). Models controlling for sex, disease duration, and LED produced similar results (−16.1%, −14.4%, and −16.4%, respectively). Linear mixed models with interaction terms showed that the contrast between the OFF-FOG group and the NO-FOG group was strongest in the right thalamus (P = 0.038). Similar but smaller trends (P = 0.157) were observed in left thalamus. Region-specific SUVr values are summarized in Table 2.

Fig. 1.

Fig. 1.

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Representative image of [11C]MENeR in an ONOFF-FOG subject. (Top panel) PET images are presented as spatially normalized to the MNI template (bottom panel) space in standardized update value ratio (SUVr) units using the cerebellum as the reference region.

Fig. 2.

Fig. 2.

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Variation in NET binding with FOG classification. A linear mixed model identified statistically significant reduction in NET binding across all brain regions in the OFF-FOG group vs. the NO-FOG group. Lower edges, middles, and upper edges of boxes show the 25th, 50th (median) and 75th percentiles of the underlying data; whiskers extend to the most extreme data values no further than 1.5 times the interquartile ranges. Data beyond the ends of the whiskers represent “outlying” points. Two outliers with substantially increased NET expression were observed in each of the NO-FOG and ONOFF-FOG groups, with the highest among NO-FOG.

Table 2.

Region-specific NET SUVr values.

Region NO-FOG (N = 16) OFF-FOG (N = 10) ONOFF-FOG (N = 21) PP-FOG (N = 5) Total (N = 52)
Locus Coeruleus 1.28 (0.27) 1.13 (0.25) 1.23 (0.18) 1.23 (0.09) 1.23 (0.22)
Frontal Cortices 1.20 (0.22) 1.09 (0.12) 1.15 (0.15) 1.12 (0.03) 1.15 (0.17)
Temporal Lobe 1.31 (0.22) 1.15 (0.09) 1.24 (0.12) 1.25 (0.03) 1.25 (0.16)
Thalamus (Left) 1.56 (0.27) 1.37 (0.14) 1.49 (0.24) 1.49 (0.12) 1.49 (0.23)
Thalamus (Right) 1.58 (0.23) 1.35 (0.15)* 1.51 (0.23) 1.51 (0.07) 1.50 (0.22)
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*

Significant reduction in NET binding in the OFF-FOG group compared to the NO-FOG group (P = 0.038).

3.3. Right thalamus NET expression and NFOG-Q score

Because of the significantly-reduced NET binding in the right thalamus in the OFF-FOG group, we tested whether NET abundance in this area was associated with self-reported FOG severity. Linear models showed a statistically significant negative relationship between NFOG-Q score and right thalamus SUVr values, with reduced NET associated with more severe NFOG-Q score in the OFF-FOG group (regression slope, P = 0.022; overall model, P < <0.001), but not in the other groups (Fig. 3). The overall variation in NFOG-Q score explained by NET binding with interaction terms for group was Radj2=0.88.

Fig. 3.

Fig. 3.

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Association between NET binding in right thalamus and NFOG-Q score. Linear regression identified a statistically significant association between NET binding and NFOG-Q among OFF-FOG (solid line) but not among the other groups (dashed lines).

3.4. Additional analyses

Similar results were obtained with additional linear mixed models performed post-hoc that: 1) considered only those cases with imaging within 3 months of levodopa challenge (NET binding in OFF-FOG vs. NO-FOG, −16.0%, P = 0.043), 2) that excluded the PP-FOG group (−16.8%, P = 0.027), and 3) that excluded the LC (−17.2%, P = 0.020). Linear regression results using square-root transformed NFOG-Q scores as the outcome variable produced very similar results to main models (regression slope, P = 0.012; overall model, P < <0.001, Radj2=0.96) (Supplemental Information).

Similar patterns of reduced NET expression in OFF-FOG vs. NO-FOG were also obtained in secondary analyses of other regions of interest performed post hoc: left and right amygdalae, left and right frontal cortices, left and right locus coeruleus, and left and right temporal lobes. Based on the contrast observed between OFF-FOG and NO-FOG in primary analyses, we compared NET expression between these groups in other regions of interest and confirmed a statistically significant contrast across all investigated regions (P = 0.003, post hoc Welch two sample t-test, Supplemental Information). No qualitative evidence of laterality was observed. The overall contrast corresponded to an effect size of Cohen’s d ≈ 2.0, considered a “large” effect (Cohen, 1992).

4. Discussion

There is mounting evidence supporting a role for noradrenergic (NA) systems in the development of FOG in PD. This is the first study to use [11C]MeNER NET-PET imaging to examine NA denervation in PD-FOG, and particularly, in relation to FOG responsiveness to levodopa. We found significantly decreased whole-brain NET binding in OFF-FOG compared to NO-FOG (P = 0.021). We also found grand mean NET expression between these groups in additional regions of interest with a statistically significant contrast across all investigated regions (P = 0.003). Importantly, this deficit was specific to OFF-FOG; patients with ONOFF-FOG or PP-FOG showed NET binding comparable to NO-FOG. Region-specific comparisons revealed decreased binding in the right thalamus (P = 0.038) with a trend for the left thalamus, in OFF-FOG. These findings support the hypothesis that a loss of NA innervation, perhaps more profoundly to the thalamus, may contribute to levodopa responsive FOG (OFF-FOG) in PD. In our subsequent analysis of additional, lateralized regions including the amygdalae, we found similar patterns. The results also add to previous findings suggesting a connection between NA dysfunction and FOG. Further, this decreased NA innervation may relate to the recently reported increase in CSF Aβ42 amyloid in PD-FOG patients (Hatcher-Martin et al., 2021). The CSF pattern seen in the PD-FOG report was an increase in Aβ42 and decrease in ptau181, which was a distinct pattern from that seen in PD-NoFOG and Alzheimer’s disease where Aβ42 levels are reduced (Hatcher-Martin et al., 2021). This PD-FOG pattern has been reported previously in animal studies where the reduction of NE contributed to increased Aβ pathology (Duffy et al., 2019; Heneka et al., 2006; Jardanhazi-Kurutz et al., 2010). Interestingly, the increased amyloid aggregation in these models was reversed by using droxidopa (Heneka et al., 2010).

4.1. Structural and functional changes in the thalamus in FOG

This work adds to a growing body of knowledge demonstrating structural and functional changes in the thalamus – and in thalamic connections – in PD patients with FOG. In a prospective MRI study, D’Cruz et al (D’Cruz et al., 2021) showed bilateral thalamic local inflations in patients who presented with or would later develop FOG. These inflations involved medial thalamic sub-nuclei volumes which are highly innervated by NA pathways. Baseline resting-state analyses further showed that patients who would later develop FOG had thalamo-cortical coupling with limbic and cognitive regions initially stronger than that among patients who would not develop FOG. This initial increased coupling declined over a two-year period. The authors hypothesized that many of these changes may have been compensatory in nature, with increased reliance on non-motor thalamic networks, particularly limbic connections, in PD patients with FOG; after initial success, decompensation occurred, and FOG would emerge over the course of disease progression.

Other studies have shown several anatomical changes in connections between the thalamus and other brain regions in FOG, including some known to be crucial for balance and gait. These include diminished structural connectivity between the thalamus and mesencephalic locomotor region (MLR) and reduced thalamic fiber tracts in the right hemisphere in FOG (Fling et al., 2013) and more pronounced white matter abnormalities in multiple tracts connecting to the thalamus in FOG (Wang et al., 2016). It has also been shown that FOG episodes are associated with decreased anterior thalamic Blood-Oxygen-Level-Dependent (BOLD) signal, potentially associated with paroxysmal reductions in MLR activity (Shine et al., 2013; Lewis and Barker, 2009) One additional PET study using vesicular acetylcholine transporter VAChT ([18F]FEOBV) showed significantly reduced thalamic expression in fallers, particularly in the right lateral geniculate nucleus (Bohnen et al., 2019).

Our results imply that there is decreased NA innervation of the thalamus in patients with OFF-FOG, which strongly suggests that interaction between NA sensory signaling and dopaminergic movement regulation is particularly important for this phenotype. It is possible that in the presence of dopamine replacement, some patients can successfully leverage non-motor regions of basal-ganglia-thalamocortical loops to compensate for impaired NA motor – specifically gait and balance – pathways. This could potentially lead to compensatory “inflation” of these areas as described by D’Cruz and colleagues, and that additional damage to other tracts impinging on the thalamus would impact this compensation. However, because the majority of these studies did not classify patients by FOG levodopa responsive state, it is difficult to draw firm conclusions. What appears clear is that there is a class of patients with greater NA deficits in the thalamus who do not experience FOG in the presence of adequate dopamine.

We found that the decreased NET binding was more prominent in the right thalamus. This adds to previously presented data suggesting right hemisphere is more selectively affected in developing FOG. For example, it has been shown through MRI diffusion tensor imaging studies that within the right hemisphere pedunculopontine nucleus network had reduced fiber tracts in numerous regions including thalamus (Fling et al., 2013). Further, such lateralizing findings involving right-hemisphere circuitry is reflected in patients with freezing of gait in studies using functional MRI (Snijders et al., 2011) and 18[F]-6-fluoro-levodopa and 18[F]-fluordesoxyglucose (Bartels et al., 2006). This trend warrants further examination.

4.2. Noradrenergic innervation of the thalamus in normal and PD brain

Noradrenergic innervation of the thalamus has been examined in the monkey and human in some detail. In a study of two nonhuman primates, NA axon maps were generated with dopamine β-hydroxylase (DβH) and NET immunohistochemistry, and in vitro quantitative autoradiography for alpha-1 and alpha-2 adrenergic receptor densities (Perez-Santos et al., 2021). The distribution patterns of DβH, NET, and adrenergic receptors were very similar: the most densely innervated thalamic regions were the midline, caudal intralaminar (paracentral and parafascicular), and medial mediodorsal nuclei. NA axons were present at moderate levels within the anterior nuclei, while the ventral motor thalamic nuclei exhibited moderate to low NA innervation. The sensory relay nuclei were generally less innervated. The visual relay nucleus, dorsal geniculate nucleus displayed the lowest NA innervation. These findings were concordant with postmortem human results (Pifl et al., 2012; Oke et al., 1997; Oke et al., 1978).

Although NET-PET does not provide the spatial resolution required to distinguish the distribution of NET binding within the various thalamic nuclei, the impact of decreased thalamic NET binding can be speculated considering the systems thought to be involved in freezing, including motor, visual, cognitive, and limbic circuits (Ehgoetz Martens et al., 2018). It is noteworthy that the highly NA-innervated caudal intralaminar regions include predominantly thalamic neurons that project heavily to associative and limbic striatum (McFarland and Haber, 2001; McFarland and Haber, 2002). The dense NA axonal innervation of these thalamic nuclei may allow for an indirect NA modulation of basal ganglia activity across motor and non-motor functional domains (Perez-Santos et al., 2021). Furthermore, these densely innervated regions are known to receive extensive cholinergic innervation from the pedunculopontine tegmental area (Oke et al., 1997), which is also severely disrupted in PD (Muller et al., 2013). Thalamic pathology has been studied in PD using neurochemical and anatomic methods. Although none of these studies examined PD-FOG separately, they provide some important relevant information. Pifl et al (Pifl et al., 2012) reported widespread loss of thalamic NE including limbic (≥ 78%), motor (>85%), and intralaminar/midline nuclei in PD, likely the result of the well-established degeneration of the LC (Zarow et al., 2003).

4.3. Limbic circuits and FOG

While thalamus generally functions as a sensory relay for various nodes, we believe the FOG related changes are associated more to its limbic connections. These results are consistent with the hypothesis that abnormal limbic-striatum connectivity may interfere with processing specific anxiety provoking situations that lead to the emergence of FOG – in this case particularly OFF-FOG. As described above, the thalamic NA system is closely linked to limbic circuits; here, we show a link between the thalamic NA system and FOG. We also found a similar trend of lower NET binding in the amygdalae. Other studies have shown a connection between FOG and limbic circuit changes. The limbic circuit plays a critical role in generating adequate emotional and behavioral responses, regulation of motivational processes and social behavior, and emotional processing, particularly of stimuli that induce anxiety and depression, including in PD (Diederich et al., 2016; Vriend et al., 2016; He et al., 2016; Marchand, 2010; Remy et al., 2005). Several reports suggest that this circuit is connected to the development of FOG. FOG is associated with increased anxiety and depression (Giladi and Hausdorff, 2006; Ehgoetz Martens et al., 2016), and greater situational anxiety is associated with worse FOG severity (Ehgoetz Martens et al., 2016). Imaging studies (Ehgoetz Martens et al., 2018; Gilat et al., 2018) have found significantly increased connectivity between the amygdala and striatum in FOG. One MRI study showed that medial thalamic sub-nuclei have a stronger coupling with cortical associative and limbic areas (D’Cruz et al., 2021). Another PET study showed significant VAChT expression reductions in the limbic archicortex in patients with FOG (Bohnen et al., 2019).

It has been hypothesized that this increased connectivity could cause transient overloading of these normally segregated (and already dopamine-depleted) networks, resulting in increased basal ganglia inhibitory output, and eventually FOG (Lewis and Shine, 2016). We did not find a difference in measures of anxiety between groups here and this was true for other studies (Gilat et al., 2018). This might suggest there is an abnormality in processing specific anxiety provoking situations that lead to OFF-FOG (Gilat et al., 2018). Considering that anxiety and panic frequently occur as manifestations of OFF episodes (Vazquez et al., 1993) it makes sense that the limbic circuitry would relate to the OFF-FOG group specifically.

4.4. The potential of noradrenergic drugs for PD OFF-FOG

These results suggest that NA agents could be of use in treating OFF-FOG. While these patients are responsive to levodopa, it is often at the cost of greater dyskinesia (McKay et al., 2019). While NA antagonist drugs can alleviate levodopa-induced dyskinesia in animal models and patients (Bhide et al., 2015; Lindenbach et al., 2011; Shi et al., 2020; Rascol et al., 2001; Carpentier et al., 1996), the impact of NA drugs on causing dyskinesia is limited. This would make use of NA drugs reasonable alternatives. In fact, several NA agents have been examined for PD-FOG, with mixed results. Droxidopa was shown in a large double-blind, placebo control trial (n = 218) in Japan in the 1980’s to improve FOG in 25% of patients (Narabayashi et al., 1987), leading to approval in that country. We believe that patient-level variations in NA activity could contribute to the variability in clinical trials. In particular, there were two NO-FOG cases in our sample with extremely high NET binding, consistent with the hypothesis that NA mechanisms could potentially act in a compensatory protective manner against FOG in these patients. There have also been two small pilot studies showing modest benefits of atomoxetine on FOG (Jankovic, 2009; Revuelta et al., 2015). Finally, a multi-center, parallel-group, double-blind, placebo-controlled, randomized trial of methylphenidate in PD patients previously treated with subthalamic nucleus deep brain stimulation showed improved gait hypokinesia and FOG (Moreau et al., 2012). We speculate that trials of NA agents for FOG would likely benefit from including only those patients with OFF-FOG (those with greatest decrease in NET PET signal), as it stands to reason that only these patients will likely benefit.

4.5. Non-PD FOG

We included a separate group of subjects with PP-FOG, a syndrome characterized by early freezing, bradykinesia, no response to levodopa and a stereotyped course of progression (Factor et al., 2006). PP-FOG can result from several disorders, most commonly progressive supranuclear palsy (Factor et al., 2006; Fasano et al., 2012). Not all PP-FOG patients have nigrostriatal dopaminergic degeneration (Fasano et al., 2012), and it has been suggested that PP-FOG may result from extranigral neuronal degeneration. Based on our results, it appears that NA system disruption does not contribute significantly to PP-FOG, as NET-PET results in these individuals were similar to NO-FOG and ONOFF-FOG. Additional subjects will be needed to confirm this result.

4.6. Limitations

There are some limitations to this study. Although the sample size was fair, it remains difficult to completely control for potential confounds related to increased disease progression and age usually observed in PD patients with FOG. Sensitivity analyses used PD duration as a partial control, but more direct measures such as dopaminergic imaging markers would provide additional confidence. Additionally, due to technical limitations with current NET tracers, we were unable to image other relevant regions like putamen and caudate due to very low binding levels (Backstrom and Marcusson, 1990; Donnan et al., 1991; Smith et al., 2006). Similarly, regions like the substantia nigra were unable to be imaged due to the contrast resolution of the PET system. There is also potential that other uninvestigated brain regions could show additional differential NET expression between groups. Future studies using voxel-based analyses with larger samples would be required to investigate this.

We are nonetheless encouraged by the fact that the main deficit seen in OFF-FOG was not seen in more severe ONOFF-FOG, which makes confounding by overall disease severity less likely. Nevertheless, the potential for confounds remain, and these results should be replicated in a larger sample and preferably in a prospective context. In addition, spatial resolution of PET has also reduced our ability to image certain regions such as specific nuclei of the thalamus and the LC.

5. Conclusion

This is the first study to examine brain NA innervation using NET-PET in PD patients with and without FOG. We found significantly decreased whole-brain NET binding in OFF-FOG, with the largest deficits in the (right) thalami and similar trends in all regions assessed. Based on the normal regional distribution of NA innervation and pathological studies in the thalamus of PD patients, our findings suggest that limbic pathways may play a key role in OFF-FOG in PD. This finding could have implications for clinical subtyping of FOG as well as development of therapies.

Supplementary Material

supplemental material

Financial disclosures

Dr. McKay has the following disclosures: Consulting fees: Biocircuit Technologies.

Dr. Nye has nothing to disclose.

Dr. Goldstein has nothing to disclose.

Ms. Sommerfeld has nothing to disclose.

Dr. Smith has nothing to disclose.

Dr. Weinshenker has nothing to disclose.

Dr. Factor has the following disclosures: Honoraria: Lundbeck, Sunovion, Biogen, Acorda.

Grants:

Medtronics, Boston Scientific, Sun Pharmaceuticals Advanced Research Company, Biohaven, Impax, US World Meds, Sunovion Therapeutics, Neurocrine, Vaccinex, Voyager, Addex Pharma S.A., Prilenia Therapeutics CHDI Foundation, Michael J. Fox Foundation, NIH(U10 NS077366), Parkinson Foundation.

Royalties: Demos, Blackwell Futura, Springer for textbooks, Uptodate.

Other Signant Health (Bracket Global LLC), CNS Ratings LLC.

Funding

This work was supported by: Curtis family Fund; Sartain Lanier Family Foundation; American Parkinson Disease Association; NIH K25 HD086276. The funders had no role in study design, data collection, analysis and interpretation of data, or in the writing of the report.

Abbreviations:

PD

Parkinson’s disease

FOG

Freezing of gait

NO-FOG

no freezing of gait

OFF-FOG

levodopa responsive freezing of gait

ONOFF-FOG

levodopa unresponsive freezing of gait

PP-FOG

primary progressive freezing of gait

NET

Norepinephrine Transporter

N-FOG-Q

New FOG Questionnaire

LC

locus ceruleus

NE

norepinephrine

NA

noradrenergic

FMT

6-[18F]fluoro-l-m-tyrosine

MDS-UPDRS

Movement Disorder Society Unified Parkinson’s Disease Rating Scale

MoCA

Montreal Cognitive Assessment

BAI

Beck Anxiety Inventory

SUVr

Regional standardized uptake value ratios

LED

levodopa equivalent dose

MLR

mesencephalic locomotor region

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nbd.2023.106048.

Footnotes

Declaration of Competing Interest

There are no conflicts of interest with any of the authors.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supplemental material
NIHMS2142694-supplement-supplemental_material.docx (226.1KB, docx)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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