Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing.

BACKGROUND: Deeper phenotyping may improve our understanding of depression. Because depression is heterogeneous, extracting cognitive signatures associated with severity of depressive symptoms, anhedonia, and affective states is a promising approach. METHODS: Sequential sampling models decomposed behavior from an adaptive approach-avoidance conflict task into computational parameters quantifying latent cognitive signatures. Fifty unselected participants completed clinical scales and the approach-avoidance conflict task by either approaching or avoiding trials offering monetary rewards and electric shocks. RESULTS: Decision dynamics were best captured by a sequential sampling model with linear collapsing boundaries varying by net offer values, and with drift rates varying by trial-specific reward and aversion, reflecting net evidence accumulation toward approach or avoidance. Unlike conventional behavioral measures, these computational parameters revealed distinct associations with self-reported symptoms. Specifically, passive avoidance tendencies, indexed by starting point biases, were associated with greater severity of depressive symptoms (R = 0.34, p = .019) and anhedonia (R = 0.49, p = .001). Depressive symptoms were also associated with slower encoding and response execution, indexed by nondecision time (R = 0.37, p = .011). Higher reward sensitivity for offers with negative net values, indexed by drift rates, was linked to more sadness (R = 0.29, p = .042) and lower positive affect (R = -0.33, p = .022). Conversely, higher aversion sensitivity was associated with more tension (R = 0.33, p = .025). Finally, less cautious response patterns, indexed by boundary separation, were linked to more negative affect (R = -0.40, p = .005). CONCLUSIONS: We demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection.

Co-transplantation of autologous Treg cells in a cell therapy for Parkinson's disease.

The specific loss of midbrain dopamine neurons (mDANs) causes major motor dysfunction in Parkinson's disease, which makes cell replacement a promising therapeutic approach1-4. However, poor survival of grafted mDANs remains an obstacle to successful clinical outcomes5-8. Here we show that the surgical procedure itself (referred to here as 'needle trauma') triggers a profound host response that is characterized by acute neuroinflammation, robust infiltration of peripheral immune cells and brain cell death. When midbrain dopamine (mDA) cells derived from human induced pluripotent stem (iPS) cells were transplanted into the rodent striatum, less than 10% of implanted tyrosine hydroxylase (TH)+ mDANs survived at two weeks after transplantation. By contrast, TH- grafted cells mostly survived. Notably, transplantation of autologous regulatory T (Treg) cells greatly modified the response to needle trauma, suppressing acute neuroinflammation and immune cell infiltration. Furthermore, intra-striatal co-transplantation of Treg cells and human-iPS-cell-derived mDA cells significantly protected grafted mDANs from needle-trauma-associated death and improved therapeutic outcomes in rodent models of Parkinson's disease with 6-hydroxydopamine lesions. Co-transplantation with Treg cells also suppressed the undesirable proliferation of TH- grafted cells, resulting in more compact grafts with a higher proportion and higher absolute numbers of TH+ neurons. Together, these data emphasize the importance of the initial inflammatory response to surgical injury in the differential survival of cellular components of the graft, and suggest that co-transplanting autologous Treg cells effectively reduces the needle-trauma-induced death of mDANs, providing a potential strategy to achieve better clinical outcomes for cell therapy in Parkinson's disease.

Accurate Deep Brain Stimulation Lead Placement Concurrent With Research Electrocorticography.

BACKGROUND: Using electrocorticography for research (R-ECoG) during deep brain stimulation (DBS) surgery has advanced our understanding of human cortical-basal ganglia neurophysiology and mechanisms of therapeutic circuit modulation. The safety of R-ECoG has been established, but potential effects of temporary ECoG strip placement on targeting accuracy have not been reported. OBJECTIVE: To determine whether temporary subdural electrode strip placement during DBS implantation surgery affects lead implantation accuracy. METHODS: Twenty-four consecutive patients enrolled in a prospective database who underwent awake DBS surgery were identified. Ten of 24 subjects participated in R-ECoG. Lead locations were determined after fusing postoperative computed tomography scans into the surgical planning software. The effect of brain shift was quantified using Lead-DBS and analyzed in a mixed-effects model controlling for time interval to postoperative computed tomography. Targeting accuracy was reported as radial and Euclidean distance errors and compared with Mann-Whitney tests. RESULTS: Neither radial error nor Euclidean distance error differed significantly between R-ECoG participants and nonparticipants. Pneumocephalus volume did not differ between the 2 groups, but brain shift was slightly greater with R-ECoG. Pneumocephalus volume correlated with brain shift, but neither of these measures significantly correlated with Euclidean distance error. There were no complications in either group. CONCLUSION: In addition to an excellent general safety profile as has been reported previously, these results suggest that performing R-ECoG during DBS implantation surgery does not affect the accuracy of lead placement.

Health phenome of Parkinson's patients reveals prominent mood-sleep cluster.

Background: Associations between phenotypic traits, environmental exposures, and Parkinson's disease have largely been evaluated one-by-one, piecemeal, and pre-selections. A comprehensive picture of comorbidities, phenotypes, exposures, and polypharmacy characterizing the complexity and heterogeneity of real-world patients presenting to academic movement disorders clinics in the US is missing. Objectives: To portrait the complexity of features associated with patients with Parkinson's disease in a study of 933 cases and 291 controls enrolled in the Harvard Biomarkers Study. Methods: The primary analysis evaluated 64 health features for associations with Parkinson's using logistic regression adjusting for age and sex. We adjusted for multiple testing using the false discovery rate (FDR) with £ 0.05 indicating statistical significance. Exploratory analyses examined feature correlation clusters and feature combinations. Results: Depression (OR = 3.11, 95% CI 2.1 to 4.71), anxiety (OR = 3.31, 95% CI 2.01-5.75), sleep apnea (OR 2.58, 95% CI 1.47-4.92), and restless leg syndrome (RLS; OR 4.12, 95% CI 1.81-12.1) were significantly more common in patients with Parkinson's than in controls adjusting for age and sex with FDR £ 0.05. The prevalence of depression, anxiety, sleep apnea, and RLS were correlated, and these diseases formed part of a larger cluster of mood traits and sleep traits linked to PD. Exposures to pesticides (OR 1.87, 95% CI 1.37-2.6), head trauma (OR 2.33, 95% CI 1.51-3.73), and smoking (OR 0.57, 95% CI 0.43-0.75) were significantly associated with the disease consistent with previous studies. Vitamin supplementation with cholecalciferol (OR 2.18, 95% CI 1.4-3.45) and coenzyme Q10 (OR 2.98, 95% CI 1.89-4.92) was more commonly used by patients than controls. Cumulatively, 43% (398 of 933) of Parkinson's patients had at least one psychiatric or sleep disorder, compared to 21% (60 of 291) of healthy controls. Conclusions: 43% of Parkinson's patients seen at Harvard-affiliated teaching hospitals have depression, anxiety, and disordered sleep. This syndromic cluster of mood and sleep traits may be pathophysiologically linked and clinically important.

Personalizing Deep Brain Stimulation Using Advanced Imaging Sequences.

OBJECTIVE: With a growing appreciation for interindividual anatomical variability and patient-specific brain connectivity, advanced imaging sequences offer the opportunity to directly visualize anatomical targets for deep brain stimulation (DBS). The lack of quantitative evidence demonstrating their clinical utility, however, has hindered their broad implementation in clinical practice. METHODS: Using fast gray matter acquisition T1 inversion recovery (FGATIR) sequences, the present study identified a thalamic hypointensity that holds promise as a visual marker in DBS. To validate the clinical utility of the identified hypointensity, we retrospectively analyzed 65 patients (26 female, mean age = 69.1 ± 12.7 years) who underwent DBS in the treatment of essential tremor. We characterized its neuroanatomical substrates and evaluated the hypointensity's ability to predict clinical outcome using stimulation volume modeling and voxelwise mapping. Finally, we determined whether the hypointensity marker could predict symptom improvement on a patient-specific level. RESULTS: Anatomical characterization suggested that the identified hypointensity constituted the terminal part of the dentatorubrothalamic tract. Overlap between DBS stimulation volumes and the hypointensity in standard space significantly correlated with tremor improvement (R2  = 0.16, p = 0.017) and distance to hotspots previously reported in the literature (R2  = 0.49, p = 7.9e-4). In contrast, the amount of variance explained by other anatomical atlas structures was reduced. When accounting for interindividual neuroanatomical variability, the predictive power of the hypointensity increased further (R2  = 0.37, p = 0.002). INTERPRETATION: Our findings introduce and validate a novel imaging-based marker attainable from FGATIR sequences that has the potential to personalize and inform targeting and programming in DBS for essential tremor. ANN NEUROL 2022;91:613-628.

Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data.

The clinical presentation of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, varies widely across patients, making it challenging to determine if potential therapeutics slow progression. We sought to determine whether there were common patterns of disease progression that could aid in the design and analysis of clinical trials. We developed an approach based on a mixture of Gaussian processes to identify clusters of patients sharing similar disease progression patterns, modeling their average trajectories and the variability in each cluster. We show that ALS progression is frequently nonlinear, with periods of stable disease preceded or followed by rapid decline. We also show that our approach can be extended to Alzheimer's and Parkinson's diseases. Our results advance the characterization of disease progression of ALS and provide a flexible modeling approach that can be applied to other progressive diseases.

The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces.

BACKGROUND: Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. OBJECTIVES: Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. METHODS: Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. RESULTS: The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. CONCLUSIONS: Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration.

Genome-wide survival study identifies a novel synaptic locus and polygenic score for cognitive progression in Parkinson's disease.

A key driver of patients' well-being and clinical trials for Parkinson's disease (PD) is the course that the disease takes over time (progression and prognosis). To assess how genetic variation influences the progression of PD over time to dementia, a major determinant for quality of life, we performed a longitudinal genome-wide survival study of 11.2 million variants in 3,821 patients with PD over 31,053 visits. We discover RIMS2 as a progression locus and confirm this in a replicate population (hazard ratio (HR) = 4.77, P = 2.78 × 10-11), identify suggestive evidence for TMEM108 (HR = 2.86, P = 2.09 × 10-8) and WWOX (HR = 2.12, P = 2.37 × 10-8) as progression loci, and confirm associations for GBA (HR = 1.93, P = 0.0002) and APOE (HR = 1.48, P = 0.001). Polygenic progression scores exhibit a substantial aggregate association with dementia risk, while polygenic susceptibility scores are not predictive. This study identifies a novel synaptic locus and polygenic score for cognitive disease progression in PD and proposes diverging genetic architectures of progression and susceptibility.

Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson's disease.

Longitudinal, remote monitoring of motor symptoms in Parkinson's disease (PD) could enable more precise treatment decisions. We developed the Motor fluctuations Monitor for Parkinson's Disease (MM4PD), an ambulatory monitoring system that used smartwatch inertial sensors to continuously track fluctuations in resting tremor and dyskinesia. We designed and validated MM4PD in 343 participants with PD, including a longitudinal study of up to 6 months in a 225-subject cohort. MM4PD measurements correlated to clinical evaluations of tremor severity (ρ = 0.80) and mapped to expert ratings of dyskinesia presence (P < 0.001) during in-clinic tasks. MM4PD captured symptom changes in response to treatment that matched the clinician's expectations in 94% of evaluated subjects. In the remaining 6% of cases, symptom data from MM4PD identified opportunities to make improvements in pharmacologic strategy. These results demonstrate the promise of MM4PD as a tool to support patient-clinician communication, medication titration, and clinical trial design.

ß-Glucocerebrosidase activity in GBA-linked Parkinson disease: The type of mutation matters.

OBJECTIVE: To test the relationship between clinically relevant types of GBA mutations (none, risk variants, mild mutations, severe mutations) and ß-glucocerebrosidase activity in patients with Parkinson disease (PD) in cross-sectional and longitudinal case-control studies. METHODS: A total of 481 participants from the Harvard Biomarkers Study (HBS) and the NIH Parkinson's Disease Biomarkers Program (PDBP) were analyzed, including 47 patients with PD carrying GBA variants (GBA-PD), 247 without a GBA variant (idiopathic PD), and 187 healthy controls. Longitudinal analysis comprised 195 participants with 548 longitudinal measurements over a median follow-up period of 2.0 years (interquartile range, 1-2 years). RESULTS: ß-Glucocerebrosidase activity was low in blood of patients with GBA-PD compared to healthy controls and patients with idiopathic PD, respectively, in HBS (p < 0.001) and PDBP (p < 0.05) in multivariate analyses adjusting for age, sex, blood storage time, and batch. Enzyme activity in patients with idiopathic PD was unchanged. Innovative enzymatic quantitative trait locus (xQTL) analysis revealed a negative linear association between residual ß-glucocerebrosidase activity and mutation type with p < 0.0001. For each increment in the severity of mutation type, a reduction of mean ß-glucocerebrosidase activity by 0.85 µmol/L/h (95% confidence interval, -1.17, -0.54) was predicted. In a first longitudinal analysis, increasing mutation severity types were prospectively associated with steeper declines in ß-glucocerebrosidase activity during a median 2 years of follow-up (p = 0.02). CONCLUSIONS: Residual activity of the ß-glucocerebrosidase enzyme measured in blood inversely correlates with clinical severity types of GBA mutations in PD. ß-Glucocerebrosidase activity is a quantitative endophenotype that can be monitored noninvasively and targeted therapeutically.

Personalized iPSC-Derived Dopamine Progenitor Cells for Parkinson's Disease.

We report the implantation of patient-derived midbrain dopaminergic progenitor cells, differentiated in vitro from autologous induced pluripotent stem cells (iPSCs), in a patient with idiopathic Parkinson's disease. The patient-specific progenitor cells were produced under Good Manufacturing Practice conditions and characterized as having the phenotypic properties of substantia nigra pars compacta neurons; testing in a humanized mouse model (involving peripheral-blood mononuclear cells) indicated an absence of immunogenicity to these cells. The cells were implanted into the putamen (left hemisphere followed by right hemisphere, 6 months apart) of a patient with Parkinson's disease, without the need for immunosuppression. Positron-emission tomography with the use of fluorine-18-L-dihydroxyphenylalanine suggested graft survival. Clinical measures of symptoms of Parkinson's disease after surgery stabilized or improved at 18 to 24 months after implantation. (Funded by the National Institutes of Health and others.).

The Neural Basis of Approach-Avoidance Conflict: A Model Based Analysis.

Approach-avoidance conflict arises when the drives to pursue reward and avoid harm are incompatible. Previous neuroimaging studies of approach-avoidance conflict have shown large variability in reported neuroanatomical correlates. These prior studies have generally neglected to account for potential sources of variability, such as individual differences in choice preferences and modeling of hemodynamic response during conflict. In the present study, we controlled for these limitations using a hierarchical Bayesian model (HBM). This enabled us to measure participant-specific per-trial estimates of conflict during an approach-avoidance task. We also employed a variable epoch method to identify brain structures specifically sensitive to conflict. In a sample of 28 human participants, we found that only a limited set of brain structures [inferior frontal gyrus (IFG), right dorsolateral prefrontal cortex (dlPFC), and right pre-supplementary motor area (pre-SMA)] are specifically correlated with approach-avoidance conflict. These findings suggest that controlling for previous sources of variability increases the specificity of the neuroanatomical correlates of approach-avoidance conflict.

Dorsolateral prefrontal neurons mediate subjective decisions and their variation in humans.

Subjective decisions play a vital role in human behavior because, while often grounded in fact, they are inherently based on personal beliefs that can vary broadly within and between individuals. While these properties set subjective decisions apart from many other sensorimotor processes and are of wide sociological impact, their single-neuronal basis in humans is unknown. Here we find cells in the dorsolateral prefrontal cortex (dlPFC) that reflect variations in the subjective decisions of humans when performing opinion-based tasks. These neurons changed their activities gradually as the participants transitioned between choice options but also reflected their unique point of conversion at equipoise. Focal disruption of the dlPFC, by contrast, diminished gradation between opposing decisions but had little effect on sensory perceptual choices or their motor report. These findings suggest that the human dlPFC plays an important role in subjective decisions and propose a mechanism for mediating their variation during opinion formation.

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei.

Nonlinear registration of individual brain MRI scans to standard brain templates is common practice in neuroimaging and multiple registration algorithms have been developed and refined over the last 20 years. However, little has been done to quantitatively compare the available algorithms and much of that work has exclusively focused on cortical structures given their importance in the fMRI literature. In contrast, for clinical applications such as functional neurosurgery and deep brain stimulation (DBS), proper alignment of subcortical structures between template and individual space is important. This allows for atlas-based segmentations of anatomical DBS targets such as the subthalamic nucleus (STN) and internal pallidum (GPi). Here, we systematically evaluated the performance of six modern and established algorithms on subcortical normalization and segmentation results by calculating over 11,000 nonlinear warps in over 100 subjects. For each algorithm, we evaluated its performance using T1-or T2-weighted acquisitions alone or a combination of T1-, T2-and PD-weighted acquisitions in parallel. Furthermore, we present optimized parameters for the best performing algorithms. We tested each algorithm on two datasets, a state-of-the-art MRI cohort of young subjects and a cohort of subjects age- and MR-quality-matched to a typical DBS Parkinson's Disease cohort. Our final pipeline is able to segment DBS targets with precision comparable to manual expert segmentations in both cohorts. Although the present study focuses on the two prominent DBS targets, STN and GPi, these methods may extend to other small subcortical structures like thalamic nuclei or the nucleus accumbens.

Intermittent subthalamic nucleus deep brain stimulation induces risk-aversive behavior in human subjects.

The subthalamic nucleus (STN) is a small almond-shaped subcortical structure classically known for its role in motor inhibition through the indirect pathway within the basal ganglia. Little is known about the role of the STN in mediating cognitive functions in humans. Here, we explore the role of the STN in human subjects making decisions under conditions of uncertainty using single-neuron recordings and intermittent deep brain stimulation (DBS) during a financial decision-making task. Intraoperative single-neuronal data from the STN reveals that on high-uncertainty trials, spiking activity encodes the upcoming decision within a brief (500 ms) temporal window during the choice period, prior to the manifestation of the choice. Application of intermittent DBS selectively prior to the choice period alters decisions and biases subject behavior towards conservative wagers.