A group of structures deep inside the brain are thought to be responsible for the progression of Parkinson’s disease. These structures, known as the basal ganglia, play an important role in coordinating movement via two opposing motor pathways: the ‘indirect pathway’ which suppresses movement, and the ‘direct pathway’ which promotes movement. It has been reported that overactivation of the indirect pathway and underactivation of the direct pathway lead to the motor impairments associated with Parkinson’s disease (Albin et al., 1989; Bergman et al., 1990; Gerfen et al., 1990).
Previous studies investigating the cellular mechanisms that cause these abnormalities have largely focused on spiny projection neurons (SPNs for short), a group of cells found in a basal ganglia structure known as the striatum. These cells express one of two types of dopamine receptors called D1 and D2. SPNs expressing D1 are frequently referred to as the origin of the direct pathway, whereas SPNs expressing D2 are referred to as the origin of the indirect pathway. These pathways then pass this signal between multiple structures of the basal ganglia until they reach a group of cells known as the output nuclei.
The striatum receives most of its dopamine from an area in the brain that degenerates in Parkinson’s disease. This loss in dopamine is thought to reduce the activity of D1-SPNs and increase the activity of D2-SPNs, causing neurons in the striatum to fire at different rates: this is predicted to drive the excessive activity of the indirect pathway and reduced activity of the direct pathway, which leads to pathological activity throughout the basal ganglia (Gerfen and Surmeier, 2011; Figure 1A). This hypothesis is known as the ‘rate model’ and has had a huge influence on the field of Parkinson’s disease. Yet, there has been limited in vivo evidence showing the firing rates of SPNs changing, particularly from human patients.
Now, in eLife, Marc Deffains (University of Bordeaux) and colleagues – including Dan Valsky (Hebrew University of Jerusalem) as first author – report experiments investigating the firing rates of SPNs in patients with Parkinson’s disease (Valsky et al., 2020). The team were able to gather data from human patients who were undergoing a surgical procedure that implants electrodes into deep regions of the brain. Valsky et al. found that the firing rates of SPNs in patients with Parkinson’s disease were no different than expected values found in healthy non-human primates. Further computational analysis, clustering the different firing rates detected, was unable to identify two distinct populations of neurons that could represent overactive D2-SPNs and underactive D1-SPNs.
These results were in contrast to the only other human study which supports the predictions made by the rate model (Singh et al., 2016). However, both studies used a different method to isolate and analyze the activity of neurons. Valsky et al. applied strict criteria to ensure that the firing rates recorded only came from well-isolated, stationary single units. This minimizes the chance of other factors, such as noise from movement or signals from damaged cells, interfering with the firing rates being measured. Valsky et al. showed that when these criteria were not in place, they were able to replicate the changes in activity reported in the previous study, but argued that this is a spurious conclusion.
The fact that Valsky et al. were not able to find evidence for the rate model within the striatum was not entirely unexpected, as the exceptions and limitations of this model have become increasingly documented (Obeso and Lanciego, 2011). Other models have proposed that the abnormalities observed in the basal ganglia arise from neurons changing their patterns of activity to fire more irregularly or with increased oscillations (Nelson and Kreitzer, 2014). But when Valsky et al. searched their data for these other patterns of activity, they could not detect any of these features in the striatal neurons of patients with Parkinson’s disease (Figure 1B).
These findings raise a lot of questions about the role the striatum plays in the motor impairments associated with Parkinson’s disease. However, two critical variables that were not addressed in this study are synchrony (non-oscillatory), and total neuron recruitment within the striatum. When multiple SPNs fire simultaneously, this relays a more powerful signal to downstream areas of the brain than if SPNs are activated independently. Therefore, if dopamine depletion enabled more D2-SPNs to activate simultaneously, or in greater numbers, this could lead to increase indirect pathway output from the striatum. However, the techniques used to record neuronal activity in this study means it is not possible to determine whether this change occurred.
These findings highlight the need for a critical reassessment of long-standing assumptions about the cellular mechanisms involved in the onset of Parkinson’s disease. Even if this study does not completely rule out the striatum as a source of basal ganglia abnormalities in this condition, it narrows down the types of changes that might be responsible. It also emphasizes the roles of brain structures that are traditionally considered to be ‘downstream’ of the striatum in generating the neuronal defects associated with Parkinson’s disease (Figure 1B). Furthermore, this work provides a valuable, rigorously curated data set which will be beneficial to the field.
Modulation of striatal projection systems by dopamineAnnual Review of Neuroscience 34:441–466.https://doi.org/10.1146/annurev-neuro-061010-113641
Reassessing models of basal ganglia function and dysfunctionAnnual Review of Neuroscience 37:117–135.https://doi.org/10.1146/annurev-neuro-071013-013916
Past, present, and future of the pathophysiological model of the basal gangliaFrontiers in Neuroanatomy 5:39.https://doi.org/10.3389/fnana.2011.00039
Background: Neuronal and circuit level abnormalities of excitation and inhibition are shown to be associated with tau and amyloid-beta (Aβ) in preclinical models of Alzheimer's disease (AD). These relationships remain poorly understood in patients with AD.
Methods: Using empirical spectra from magnetoencephalography (MEG) and computational modeling (neural mass model; NMM) we examined excitatory and inhibitory parameters of neuronal subpopulations and investigated their specific associations to regional tau and Aβ, measured by positron emission tomography (PET), in patients with AD.
Results: Patients with AD showed abnormal excitatory and inhibitory time-constants and neural gains compared to age-matched controls. Increased excitatory time-constants distinctly correlated with higher tau depositions while increased inhibitory time-constants distinctly correlated with higher Aβ depositions.
Conclusions: Our results provide critical insights about potential mechanistic links between abnormal neural oscillations and cellular correlates of impaired excitatory and inhibitory synaptic functions associated with tau and Aβ in patients with AD.
Funding: This study was supported by the National Institutes of Health grants: K08AG058749 (KGR), F32AG050434-01A1 (KGR), K23 AG038357 (KAV), P50 AG023501, P01 AG19724 (BLM), P50-AG023501 (BLM & GDR), R01 AG045611 (GDR); AG034570, AG062542 (WJ); NS100440 (SSN), DC176960 (SSN), DC017091 (SSN), AG062196 (SSN); a grant from John Douglas French Alzheimer's Foundation (KAV); grants from Larry L. Hillblom Foundation: 2015-A-034-FEL and (KGR); 2019-A-013-SUP (KGR); a grant from the Alzheimer's Association: (PCTRB-13-288476) (KAV), and made possible by Part the CloudTM, (ETAC-09-133596); a grant from Tau Consortium (GDR & WJJ), and a gift from the S. D. Bechtel Jr. Foundation.
The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships.
Using cross-sectional data from 306 previously concussed children aged 9–10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures.
Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results.
Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity.
Financial support for this work came from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (G.I.G.), an Ontario Graduate Scholarship (S.S.), a Restracomp Research Fellowship provided by the Hospital for Sick Children (S.S.), an Institutional Research Chair in Neuroinformatics (M.D.), as well as a Natural Sciences and Engineering Research Council CREATE grant (M.D.).