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Proceedings of the 12th annual deep brain stimulation think tank: cutting edge technology meets novel applications.
The Deep Brain Stimulation (DBS) Think Tank XII was held on August 21st to 23rd. This year we showcased groundbreaking advancements in neuromodulation technology, focusing heavily on the novel uses of existing technology as well as next-generation technology. Our keynote speaker shared the vision of using neuro artificial intelligence to predict depression using brain electrophysiology. Innovative applications are currently being explored in stroke, disorders of consciousness, and sleep, while established treatments for movement disorders like Parkinson's disease are being refined with adaptive stimulation. Neuromodulation is solidifying its role in treating psychiatric disorders such as depression and obsessive-compulsive disorder, particularly for patients with treatment-resistant symptoms. We estimate that 300,000 leads have been implanted to date for neurologic and neuropsychiatric indications. Magnetoencephalography has provided insights into the post-DBS physiological changes. The field is also critically examining the ethical implications of implants, considering the long-term impacts on clinicians, patients, and manufacturers.
Relationships between depression, anxiety, and motivation in the real-world: Effects of physical activity and screentime.
BACKGROUND: Mood and anxiety disorders are highly prevalent and comorbid worldwide, with variability in symptom severity that fluctuates over time. Digital phenotyping, a growing field that aims to characterize clinical, cognitive and behavioral features via personal digital devices, enables continuous quantification of symptom severity in the real world, and in real-time. METHODS: In this study, N=114 individuals with a mood or anxiety disorder (MA) or healthy controls (HC) were enrolled and completed 30-days of ecological momentary assessments (EMA) of symptom severity. Novel real-world measures of anxiety, distress and depression were developed based on the established Mood and Anxiety Symptom Questionnaire (MASQ). The full MASQ was also completed in the laboratory (in-lab). Additional EMA measures related to extrinsic and intrinsic motivation, and passive activity data were also collected over the same 30-days. Mixed-effects models adjusting for time and individual tested the association between real-world symptom severity EMA and the corresponding full MASQ sub-scores. A graph theory neural network model (DEPNA) was applied to all data to estimate symptom interactions. RESULTS: There was overall good adherence over 30-days (MA=69.5%, HC=71.2% completion), with no group difference (t(58)=0.874, p=0.386). Real-world measures of anxiety/distress/depression were associated with their corresponding MASQ measure within the MA group (t's > 2.33, p's < 0.024). Physical activity (steps) was negatively associated with real-world distress and depression (IRRs > 0.93, p's ≤ 0.05). Both intrinsic and extrinsic motivation were negatively associated with real-world distress/depression (IRR's > 0.82, p's < 0.001). DEPNA revealed that both extrinsic and intrinsic motivation significantly influenced other symptom severity measures to a greater extent in the MA group compared to the HC group (extrinsic/intrinsic motivation: t(46) = 2.62, p < 0.02, q FDR < 0.05, Cohen's d = 0.76; t(46) = 2.69, p < 0.01, q FDR < 0.05, Cohen's d = 0.78 respectively), and that intrinsic motivation significantly influenced steps (t(46) = 3.24, p < 0.003, q FDR < 0.05, Cohen's d = 0.94). CONCLUSIONS: Novel real-world measures of anxiety, distress and depression significantly related to their corresponding established in-lab measures of these symptom domains in individuals with mood and anxiety disorders. Novel, exploratory measures of extrinsic and intrinsic motivation also significantly related to real-world mood and anxiety symptoms and had the greatest influencing degree on patients' overall symptom profile. This suggests that measures of cognitive constructs related to drive and activity may be useful in characterizing phenotypes in the real-world.
Development of a diagnostic checklist to identify functional cognitive disorder versus other neurocognitive disorders.
BACKGROUND: Functional cognitive disorder (FCD) poses a diagnostic challenge due to its resemblance to other neurocognitive disorders and limited biomarker accuracy. We aimed to develop a new diagnostic checklist to identify FCD versus other neurocognitive disorders. METHODS: The clinical checklist was developed through mixed methods: (1) a literature review, (2) a three-round Delphi study with 45 clinicians from 12 countries and (3) a pilot discriminative accuracy study in consecutive patients attending seven memory services across the UK. Items gathering consensus were incorporated into a pilot checklist. Item redundancy was evaluated with phi coefficients. A briefer checklist was produced by removing items with >10% missing data. Internal validity was tested using Cronbach's alpha. Optimal cut-off scores were determined using receiver operating characteristic curve analysis. RESULTS: A full 11-item checklist and a 7-item briefer checklist were produced. Overall, 239 patients (143 FCD, 96 non-FCD diagnoses) were included. The checklist scores were significantly different across subgroups (FCD and other neurocognitive disorders) (F(2, 236)=313.3, p<0.001). The area under the curve was excellent for both the full checklist (0.97, 95% CI 0.95 to 0.99) and its brief version (0.96, 95% CI 0.93 to 0.98). Optimal cut-off scores corresponded to a specificity of 97% and positive predictive value of 91% for identifying FCD. Both versions showed good internal validity (>0.80). CONCLUSIONS: This pilot study shows that a brief clinical checklist may serve as a quick complementary tool to differentiate patients with neurodegeneration from those with FCD. Prospective blind large-scale validation in diverse populations is warranted.Cite Now.
Preprocessing for fMRI (and a little bit for diffusion MRI)
This chapter is about the processing of fMRI data that is performed after the acquisition of the data, and before using a statistical model to try to infer what parts of the brain were involved in the task. It is aimed primarily at people who are about to undertake their first fMRI project, or who have already completed one or two and who want a greater understanding of the analysis steps they have been taught to perform. It will focus on explaining the different steps that constitute what is traditionally referred to as “preprocessing.” But it will also touch upon some of the MR-physics relevant to the acquisition, as well as on the statistical modeling that is used for the inference, as we think this makes it easier to understand why we do some of the preprocessing. The aim of preprocessing is twofold: 1. To improve location accuracy, i.e. to ensure that we are able to accurately assign an observed activation to the right part of the brain anatomy. 2. To increase statistical power, i.e. to try to detect and remove as much variance unrelated to the experimental task as possible, thereby making it more likely that any activation is statistically significant. The division is not necessarily as clear cut as that. For example, correcting for subject movement over time will primarily be aimed at increasing statistical power, but it is easily realized that large, uncorrected, movement would also impair localization. This chapter will give an overview of the following preprocessing steps. Distortion correction : fMRI images are distorted. It will be explained why this is, and how it can be corrected. Movement correction: Subject movement is the greatest source of unwanted variance in fMRI. The different ways in which movement can affect the data will be discussed, along with methods for how it can be corrected. Slice timing correction: The different slices of an fMRI volume will be acquired at different times, while the statistical modeling often assumes a single time point. This will be explained, along with ways to correct it. Physiological noise correction: It will be discussed how breathing and cardiac pulsation introduce unwanted variance to the data, and how it can be corrected. Removal of unwanted variance: Independent Component Analysis (ICA) and “Scrubbing” are methods for removal of unwanted variance that may remain even after applying the corrections above. Co-registration of functional and structural data: fMRI images often have poor resolution and tissue contrast, which makes anatomical orientation difficult. It is therefore useful to align them to a high resolution structural (e.g., T1-weighted) image.
Serum neurofilament light chain and structural and Functional nerve fiber loss in painful and painless diabetic polyneuropathy.
AIMS: To explore associations between the axonal protein Neurofilament Light (NfL) and severity of Diabetic Polyneuropathy (DPN) and pain. METHODS: We performed cross-sectional analysis of a subset of the PiNS/DOLORisk cohort of people with DPN with and without neuropathic pain. Biobank samples were analyzed for serum NfL (s-NfL) using single molecule array. DPN was defined by Toronto criteria for probable or confirmed DPN. Painful DPN (PDPN) was evaluated according to IASP criteria. Measures of DPN severity included clinical DPN scales, Quantitative Sensory Testing (QST) and Intraepidermal Nerve Fiber Density (IENFD). RESULTS: Participants with confirmed (N = 172) or probable DPN (N = 29) were included. There was no s-NfL difference between participants with DPN (N = 79, 22.8 ng/L [IQR 17.4; 31.3]) and PDPN (N = 122, 22.2 ng/L [16.0; 34.4]). S-NfL was not associated with pain severity or DPN severity evaluated by clinical DPN scales. Higher s-NfL was associated with lower IENFD (13.6 % [95 % CI 3.1; 22.9], unit = 1 fiber/mm, N = 24) and more pronounced loss of nerve fiber function measured by QST (p-trend = 0.02). CONCLUSIONS: Higher s-NfL was associated with nerve fiber dysfunction and loss quantified by QST and IENFD, but not with pain or clinical DPN scales. S-NfL may reflect the severity of nerve fiber damage underlying DPN.
Assessing the impact of COmorbidities and Sociodemographic factors on Multiorgan Injury following COVID-19: rationale and protocol design of COSMIC, a UK multicentre observational study of COVID-negative controls.
INTRODUCTION: SARS-CoV-2 disease (COVID-19) has had an enormous health and economic impact globally. Although primarily a respiratory illness, multi-organ involvement is common in COVID-19, with evidence of vascular-mediated damage in the heart, liver, kidneys and brain in a substantial proportion of patients following moderate-to-severe infection. The pathophysiology and long-term clinical implications of multi-organ injury remain to be fully elucidated. Age, gender, ethnicity, frailty and deprivation are key determinants of infection severity, and both morbidity and mortality appear higher in patients with underlying comorbidities such as ischaemic heart disease, hypertension and diabetes. Our aim is to gain mechanistic insights into the pathophysiology of multiorgan dysfunction in people with COVID-19 and maximise the impact of national COVID-19 studies with a comparison group of COVID-negative controls. METHODS AND ANALYSIS: COmorbidities and Sociodemographic factors on Multiorgan Injury following COVID-19 (COSMIC) is a prospective, multicentre UK study which will recruit 200 subjects without clinical evidence of prior COVID-19 and perform extensive phenotyping with multiorgan imaging, biobank serum storage, functional assessment and patient reported outcome measures, providing a robust control population to facilitate current work and serve as an invaluable bioresource for future observational studies. ETHICS AND DISSEMINATION: Approved by the National Research Ethics Service Committee East Midlands (REC reference 19/EM/0295). Results will be disseminated via peer-reviewed journals and scientific meetings. TRIAL REGISTRATION NUMBER: COSMIC is registered as an extension of C-MORE (Capturing Multi-ORgan Effects of COVID-19) on ClinicalTrials.gov (NCT04510025).
Changes in iPSC-astrocyte morphology reflect Alzheimer’s disease patient clinical markers
Abstract Human induced pluripotent stem cells (iPSCs) provide powerful cellular models of Alzheimer’s disease (AD) and offer many advantages over non-human models, including the potential to reflect variation in individual-specific pathophysiology and clinical symptoms. Previous studies have demonstrated that iPSC-neurons from individuals with Alzheimer’s disease (AD) reflect clinical markers, including β-amyloid (Aβ) levels and synaptic vulnerability. However, despite neuronal loss being a key hallmark of AD pathology, many risk genes are predominantly expressed in glia, highlighting them as potential therapeutic targets. In this work iPSC-derived astrocytes were generated from a cohort of individuals with high versus low levels of the inflammatory marker YKL-40, in their cerebrospinal fluid (CSF). iPSC-derived astrocytes were treated with exogenous Aβ oligomers and high content imaging demonstrated a correlation between astrocytes that underwent the greatest morphology change from patients with low levels of CSF-YKL-40 and more protective APOE genotypes. This finding was subsequently verified using similarity learning as an unbiased approach. This study shows that iPSC-derived astrocytes from AD patients reflect key aspects of the pathophysiological phenotype of those same patients, thereby offering a novel means of modelling AD, stratifying AD patients and conducting therapeutic screens.
Autistic behavior is a common outcome of biallelic disruption of PDZD8 in humans and mice.
BACKGROUND: Intellectual developmental disorder with autism and dysmorphic facies (IDDADF) is a rare syndromic intellectual disability (ID) caused by homozygous disruption of PDZD8 (PDZ domain-containing protein 8), an integral endoplasmic reticulum (ER) protein. All four previously identified IDDADF cases exhibit autistic behavior, with autism spectrum disorder (ASD) diagnosed in three cases. To determine whether autistic behavior is a common outcome of PDZD8 disruption, we studied a third family with biallelic mutation of PDZD8 (family C) and further characterized PDZD8-deficient (Pdzd8tm1b) mice that exhibit stereotyped motor behavior relevant to ASD. METHODS: Homozygosity mapping, whole-exome sequencing, and cosegregation analysis were used to identify the PDZD8 variant responsible for IDDADF, including diagnoses of ASD, in consanguineous family C. To assess the in vivo effect of PDZD8 disruption on social responses and related phenotypes, behavioral, structural magnetic resonance imaging, and microscopy analyses were conducted on the Pdzd8tm1b mouse line. Metabolic activity was profiled using sealed metabolic cages. RESULTS: The discovery of a third family with IDDADF caused by biallelic disruption of PDZD8 permitted identification of a core clinical phenotype consisting of developmental delay, ID, autism, and facial dysmorphism. In addition to impairments in social recognition and social odor discrimination, Pdzd8tm1b mice exhibit increases in locomotor activity (dark phase only) and metabolic rate (both lights-on and dark phases), and decreased plasma triglyceride in males. In the brain, Pdzd8tm1b mice exhibit increased levels of accessory olfactory bulb volume, primary olfactory cortex volume, dendritic spine density, and ER stress- and mitochondrial fusion-related transcripts, as well as decreased levels of cerebellar nuclei volume and adult neurogenesis. LIMITATIONS: The total number of known cases of PDZD8-related IDDADF remains low. Some mouse experiments in the study did not use balanced numbers of males and females. The assessment of ER stress and mitochondrial fusion markers did not extend beyond mRNA levels. CONCLUSIONS: Our finding that the Pdzd8tm1b mouse model and all six known cases of IDDADF exhibit autistic behavior, with ASD diagnosed in five cases, identifies this trait as a common outcome of biallelic disruption of PDZD8 in humans and mice. Other abnormalities exhibited by Pdzd8tm1b mice suggest that the range of comorbidities associated with PDZD8 deficiency may be wider than presently recognized.
Associations Between Stroke Type, Ischemic Stroke Subtypes, and Poststroke Cognitive Trajectories.
BACKGROUND: It is unclear how poststroke cognitive trajectories differ by stroke type and ischemic stroke subtype. We studied associations between stroke types (ischemic and hemorrhagic), ischemic stroke subtypes (cardioembolic, large artery atherosclerotic, lacunar/small vessel, and cryptogenic/other determined causes), and poststroke cognitive decline. METHODS: We pooled participants from 4 US cohort studies (1971-2019). Outcomes were change in global cognition (primary) and changes in executive function and memory (secondary). Outcomes were standardized as T scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1 SD difference in cognition. The median follow-up for the primary outcome was 6.0 (interquartile range, 3.2-9.2) years. Linear mixed-effects models estimated changes in cognition after stroke. RESULTS: We identified 1143 dementia-free individuals with acute stroke during follow-up: 1061 (92.8%) ischemic, 82 (7.2%) hemorrhagic, 49.9% female, and 30.8% Black. The median age at stroke was 74.1 (interquartile range, 68.6-79.3) years. On average, ischemic stroke survivors showed declines in global cognition (-0.35 [95% CI, -0.43 to -0.27] points/y; P<0.001), executive function (-0.48 [95% CI, -0.59 to -0.36] points/y; P<0.001), and memory (-0.27 [95% CI, -0.36 to -0.19] points/y; P<0.001). Poststroke declines in global cognition, executive function, and memory did not differ between hemorrhagic and ischemic stroke survivors. Differences in poststroke cognitive slope between hemorrhagic and ischemic stroke survivors were global cognition (0.02 [95% CI, -0.21 to 0.26] points/y; P=0.85), executive function (-0.13 [95% CI, -0.48 to 0.23] points/y; P=0.48), and memory (0.19 [95% CI, -0.05 to 0.43] points/y; P=0.12). On average, small vessel stroke survivors showed declines in global cognition (-0.33 [95% CI, -0.49 to -0.16] points/y; P<0.001), executive function (-0.44 [95% CI, -0.68 to -0.19] points/y; P<0.001), and memory (-0.19 [95% CI, -0.35 to -0.03] points/y; P=0.02). Poststroke cognitive declines did not differ between small vessel survivors and survivors of other ischemic stroke subtypes. CONCLUSIONS: Stroke survivors had cognitive decline in multiple domains. Declines did not differ by stroke type or ischemic stroke subtype.
Emotional Processing Following Digital Cognitive Behavioral Therapy for Insomnia in People With Depressive Symptoms: A Randomized Clinical Trial.
IMPORTANCE: Cognitive behavioral therapy for insomnia (CBT-I) has been shown to reduce depressive symptoms, but the underlying mechanisms are not well understood and warrant further examination. OBJECTIVE: To investigate whether CBT-I modifies negative bias in the perception of emotional facial expressions and whether such changes mediate improvement in depressive symptoms. DESIGN, SETTING, AND PARTICIPANTS: A randomized clinical trial of digital CBT-I vs sleep hygiene education was conducted. Adults living in the UK who met diagnostic criteria for insomnia disorder and Patient Health Questionnaire-9 criteria (score ≥10) for depression were recruited online from the community and randomly assigned to either a 6-session digital CBT-I program or a sleep hygiene webpage. Participant recruitment took place between April 26, 2021, and January 24, 2022, and outcomes were assessed at 5 and 10 weeks post randomization. Data analysis was performed from December 1, 2022, to March 1, 2023. MAIN OUTCOMES AND MEASURES: Coprimary outcomes were recognition accuracy (percentage) of happy and sad facial expressions at 10 weeks assessed with the facial expression recognition task. Secondary outcomes were self-reported measures of insomnia, depressive symptoms, affect, emotional regulation difficulties, worry, perseverative thinking, midpoint of sleep, social jet lag, and the categorization of and recognition memory for emotional words. Intention-to-treat analysis was used. RESULTS: A total of 205 participants were randomly assigned to CBT-I (n = 101) or sleep hygiene education (n = 104). The sample had a mean (SD) age of 49.3 (10.1) years and was predominately female (165 [80.8%]). Retention was 85.7% (n = 175). At 10 weeks, the estimated adjusted mean difference for recognition accuracy was 3.01 (97.5% CI, -1.67 to 7.69; P = .15; Cohen d = 0.24) for happy facial expressions and -0.54 (97.5% CI, -3.92 to 2.84; P = .72; Cohen d = -0.05) for sad facial expressions. At 10 weeks, CBT-I compared with control decreased insomnia severity (adjusted difference, -4.27; 95% CI, -5.67 to -2.87), depressive symptoms (adjusted difference, -3.91; 95% CI, -5.20 to -2.62), negative affect (adjusted difference, -2.75; 95% CI, -4.58 to -0.92), emotional regulation difficulties (adjusted difference, -5.96; 95% CI, -10.61 to -1.31), worry (adjusted difference, -8.07; 95% CI, -11.81 to -4.33), and perseverative thinking (adjusted difference, -4.21; 95% CI, -7.03 to -1.39) and increased positive affect (adjusted difference, 4.99; 95% CI, 3.13-6.85). Improvement in negative affect, emotional regulation difficulties, and worry at week 5 mediated the effect of CBT-I on depression severity at 10 weeks (% mediated: 21.9% Emotion regulation difficulties; 24.4% Worry; and 29.7% Negative affect). No serious adverse events were reported to the trial team. CONCLUSIONS AND RELEVANCE: This randomized clinical trial did not find evidence that CBT-I engenders change in the perception of facial expressions at post treatment, despite improvements in insomnia and depressive symptoms. Early change in negative affect, emotional regulation difficulties, and worry mediated lagged depression outcomes and deserve further empirical scrutiny. TRIAL REGISTRATION: isrctn.org Identifier: ISRCTN17117237.
Sparse haplotype-based fine-scale local ancestry inference at scale reveals recent selection on immune responses.
Increasingly efficient methods for inferring the ancestral origin of genome regions are needed to gain insights into genetic function and history as biobanks grow in scale. Here we describe two near-linear time algorithms to learn ancestry harnessing the strengths of a Positional Burrows-Wheeler Transform. SparsePainter is a faster, sparse replacement of previous model-based 'chromosome painting' algorithms to identify recently shared haplotypes, whilst PBWTpaint uses further approximations to obtain lightning-fast estimation optimized for genome-wide relatedness estimation. The computational efficiency gains of these tools for fine-scale local ancestry inference offer the possibility to analyse large-scale genomic datasets using different approaches. Application to the UK Biobank shows that haplotypes better represent ancestries than principal components, whilst linkage-disequilibrium of ancestry identifies signals of recent changes to population-specific selection for many genomic regions associated with immune responses, suggesting avenues for understanding the pathogen-immune system interplay on a historical timescale.
Post-stroke changes in brain structure and function can both influence acute upper limb function and subsequent recovery.
Improving outcomes after stroke depends on understanding both the causes of initial function/impairment and the mechanisms of recovery. Recovery in patients with initially low function/high impairment is variable, suggesting the factors relating to initial function/impairment are different to the factors important for subsequent recovery. Here we aimed to determine the contribution of altered brain structure and function to initial severity and subsequent recovery of the upper limb post-stroke. The Nine-Hole Peg Test was recorded in week 1 and one-month post-stroke and used to divide 36 stroke patients (18 females, age: M = 66.56 years) into those with high/low initial function and high/low subsequent recovery. We determined differences in week 1 brain structure (Magnetic Resonance Imaging) and function (Magnetoencephalography, tactile stimulation) between high/low patients for both initial function and subsequent recovery. Lastly, we examined the relative contribution of changes in brain structure and function to recovery in patients with low levels of initial function. Low initial function and low subsequent recovery are related to lower sensorimotor β power and greater lesion-induced disconnection of contralateral [ipsilesional] white-matter motor projection connections. Moreover, differences in intra-hemispheric connectivity (structural and functional) are unique to initial motor function, while differences in inter-hemispheric connectivity (structural and functional) are unique to subsequent motor recovery. Function-related and recovery-related differences in brain function and structure after stroke are related, yet not identical. Separating out the factors that contribute to each process is key to identifying potential therapeutic targets for improving outcomes.