Biology subjects

Noyce, A.

Publications and source records attributed to Noyce, A..

12 recordsLinked to original sources

bioRxiv2026

Voice and linguistic features support early attentional filtering of irrelevant stimuli

In cocktail party-type environments, listeners must segregate simultaneous acoustic sources and select one for processing. Behavioral studies have established that both low-level voice differences (e.g., pitch) and high-level linguistic differences (e.g., word content) aid these processes. Electroencephalography (EEG) studies have demonstrated that the brain filters voices based on low-level features early in sensory processing, but it is unclear whether linguistic differences also support early filtering. In this EEG study, listeners attended to a lateralized auditory stream of target syllables while ignoring distractor stimuli from the opposite hemifield. In contrast to previous auditory attention studies, target stimuli were always attended and statistically identical across conditions; instead, we manipulated the distractors. Specifically, we manipulated whether targets and distractors were spoken in the same voice or different voices, as well as whether they were composed of the same or different linguistic content. Critically, distractors were temporally offset from targets, enabling an investigation of whether acoustically matched, always attended target stimuli are differentially encoded as a function of surrounding context. Differences in either voice or linguistic features supported target recall: Recall was poor only when both streams were in the same voice and had similar linguistic content. However, voice and linguistic features had independent, additive effects on neural processing. Specifically, target dissimilarity in acoustics and linguistic content both contributed to more robust, larger-magnitude target-evoked EEG responses. Overall, results demonstrate that both linguistic dissimilarity and low-level acoustic dissimilarity improve early attentional filtering of distractors in multi-source acoustic scenes.

neuroscience↗

bioRxiv2026

Investigating Invariances in Auditory Event Categorization with Model Metamers

Real-world acoustic inputs contain rich sensory information that we parse into discrete auditory objects and categories. Although deep neural networks (DNNs) are increasingly used to model auditory perception, the field lacks rigorous behavioral benchmarks, particularly for auditory event categorization. Here, we developed a 25-way categorization paradigm for broad classes of natural sounds to test whether categorical invariances of DNNs align with those of human observers. We first confirmed that humans could reliably categorize the natural sounds, demonstrating that our paradigm is well-suited for testing invariances in auditory categories. To probe model invariances, we evaluated human recognition of model metamers (synthetic stimuli matched to the models internal activations for each natural stimulus) for a wide range of architectures trained on speech or auditory event recognition. Evaluating widely-used public models, we found that human recognition of auditory event model metamers was generally influenced by the training task and data distribution; speech models trained on standard, curated datasets produced less recognizable metamers than auditory event recognition models. We additionally analyzed a controlled set of models to directly investigate the influence of training task and adversarial training, revealing that improved metamer recognition induced by adversarial training is task-dependent. However, even in the best-performing models, we observed a sharp decline in human recognition at the final classification layer compared to the penultimate representation layer. Overall, our results suggest that while invariances in modern architectures better align with human observers for auditory event categorization, there is still a large discrepancy between the categorical invariances of auditory neural networks and the invariances of human observers. Code and models are available at https://github.com/Feather-Lab/env-sound-metamers

neuroscience↗

bioRxiv2026

Representational similarity analysis of EEG reveals multiple spatiotemporal dynamics of selective attention

Selective attention allows listeners to follow a target speaker in complex scenes, but less is known about the neural mechanisms that control this capacity. We collected electroencephalography (EEG) during an auditory attention task encompassing spatial attention, talker-based attention, and passive listening. Time-resolved representational similarity analysis (RSA) of broadband scalp voltage and alpha-band oscillations revealed distinct representational trajectories in the two measurements: scalp voltage produced brief peaks in task discriminability shortly after the target cue and around 300 ms after target onset, whereas alpha-band power emerged more slowly and remained elevated throughout the trial. These patterns distinguish attentive from passive conditions and spatial from talker attention. Furthermore, broadband activity and alpha power encode complementary information, while other frequency bands contribute little independent explanatory power. These results demonstrate that RSA allows a unified interpretation of evoked and induced EEG measures, revealing how distinct neural computations jointly encode the spatiotemporal dynamics of selective attention.

neuroscience↗

bioRxiv2025

Attention to Psuedo-Tone Melodies Enhances Cortical but Not Brainstem Responses in Humans

Auditory selective attention, the ability to focus on specific sounds while ignoring competitors, enables communication in complex soundscapes. Though attention clearly modulates cortical responses to sound, whether and where this modulation occurs in subcortical structures remains disputed. Here, we use electroencephalography to record cortical and subcortical (auditory brainstem responses, ABRs) activity during a selective attention task. Human participants attend to a 3-note melody in one pitch range presented to one ear while ignoring a competing, interleaved melody in a different pitch range played to the other ear (Laffere et al., 2020, 2021). The melodies consist of pitch-evoking pseudo-tones formed by convolving a periodic impulse train with a brief tone pip. These stimuli allow us to measure both ABRs (elicited by each individual tone pip within a pseudo-note) and cortical responses (elicited by the onsets of pseudo-notes) simultaneously. We observed robust ABRs, but no evidence of modulation by attention. Conversely, cortical responses, measured by event related potentials (ERPs), demonstrated attentional modulation of the P1-N1 peak. We conclude that attentional modulation within the brainstem is not measurable in the well-defined peaks of the ABR, which themselves reflect processing up to the input stage to the inferior colliculus.

neuroscience↗

bioRxiv2022

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

BackgroundThe clinical manifestations of Parkinsons disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need. Methods and FindingsWe used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinsons Disease Progression Marker Initiative (PPMI) (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinsons Disease Biomarker Program (PDBP) (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression five years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 {+/-} 0.01 for the slower progressing group (PDvec1), 0.87 {+/-} 0.03 for moderate progressors, and 0.95 {+/-} 0.02 for the fast progressing group (PDvec3). We identified serum neurofilament light (Nfl) as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent validation cohort, released the analytical code, and developed models in an open science manner. ConclusionsOur data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources, and ultimately individualized patient care.

bioinformatics↗

bioRxiv2020

Large-scale pathway-specific polygenic risk, transcriptomic community networks and functional inferences in Parkinson disease

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological pathways underlying PD using the largest currently available cohorts of genetic data and gene expression data from International Parkinsons Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership - Parkinsons disease initiative (AMP-PD), among other sources. We placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

genetics↗

bioRxiv2020

A bidirectional Mendelian randomization study of glycemic and anthropometric traits and Parkinsons disease

BackgroundImpaired glucose and obesity are frequently observed in patients with Parkinsons disease (PD), although it is unclear whether the impairment precedes or results from the neurodegeneration. ObjectiveWe aimed to assess whether glycemic and anthropometric traits can influence the risk of PD in 33,674 cases and 449,056 healthy controls using the Mendelian randomization (MR) framework. MethodsWe investigated causality with a two-sample MR approach in the European population to compute effect estimates with summary statistics from available discovery meta-analyses of genome-wide association studies (GWAS) on glycemic and anthropometric traits. ResultsWe considered a threshold of p-value=0.0038 as significant after accounting for multiple testing, and p-value<0.05 was considered to be a suggestive evidence for a potential association. We observed a protective effect of waist-hip ratio (WHR) on PD (Inverse variance-weighted (IVW): OR IVW=0.735; 95%CI= 0.622-0.868; p-value=0.0003; I2 index=22.0%; MR-Egger intercept p-value=0.1508; Cochran Q test p-value=0.0003). The association was further retained after the exclusion of overlapping UK biobank (UKB) samples between the WHR and PD datasets (ORIVW=0.791; 95%CI=0.659-0.950; p-value=0.012; I2 index=13.0%; MR-Egger intercept p-value=0.733; Cochran Q test p-value=0.035). The sensitivity analysis provided suggestive evidence of an increased risk of PD on fasting glucose (FG) ({beta} IVW=0.0188; 95%CI=0.0062-0.0313, p-value=0.0055; I2 index=0.0%; MR-Egger intercept p-value=0.0957; Cochran Q test p-value=0.4555) and protective effect of PD on T2D (Weighted median effect: ORWME=0.946; 95%CI=0.9290.983; p-value=0.0051; Weighted mode effect: ORMBE=0.943; 95%CI=0.904-0.983; p-value=0.0116). ConclusionsOur results showed that central or abdominal obesity may be protective against PD development, independent of glucose levels.

genetics↗

bioRxiv2020

Gene-environment interactions in Multiple Sclerosis: a UK Biobank study

ImportanceMultiple Sclerosis (MS) is a neuro-inflammatory disorder caused by a combination of environmental exposures and genetic risk factors. We sought to determine whether genetic risk modifies the effect of environmental MS risk factors. MethodsPeople with MS were identified within UK Biobank using ICD10-coded MS or self-report. Associations between environmental risk factors and MS risk were quantified with a case-control design using multivariable logistic regression. Polygenic risk scores (PRS) were derived using the clumping-and-thresholding approach with external weights from the largest genome-wide association study of MS. Separate scores were created including (PRSMHC) and excluding (PRSNon-MHC) the MHC locus. The best performing PRS were identified in 30% of the cohort and validated in the remaining 70%. Interaction between environmental and genetic risk factors was quantified using the Attributable Proportion due to interaction (AP) and multiplicative interaction. ResultsData were available for 2250 people with MS and 486,000 controls. Childhood obesity, earlier age at menarche, and smoking were associated with MS. The optimal PRS were strongly associated with MS in the validation cohort (PRSMHC: Nagelkerkes Pseudo-R2 0.033, p=3.92x10-111; PRSNon-MHC: Nagelkerkes Pseudo-R2 0.013, p=3.73x10-43). There was strong evidence of interaction between polygenic risk for MS and childhood obesity (PRSMHC: AP=0.17, 95% CI 0.06 - 0.25, p=0.004; PRSNon-MHC: AP=0.17, 95% CI 0.06 - 0.27, p=0.006). Conclusions and RelevanceThis study provides novel evidence for an interaction between childhood obesity and a high burden of autosomal genetic risk. These findings may have significant implications for our understanding of MS biology and inform targeted prevention strategies.

genetics↗

bioRxiv2020

Parkinsons disease determinants, prediction and gene-environment interactions in the UK Biobank

ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinsons disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate existing risk prediction algorithms and the impact of including addition genetic risk on the performance of prediction. MethodsWe identified individuals with incident PD diagnoses (n=1276) and unmatched controls (n=500,406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. A polygenic risk score (PRS) was constructed and used to examine gene-environment interactions. The PRS was also incorporated into a previously-developed prediction algorithm for finding incident cases. ResultsStrong evidence of association (Pcorr<0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, and daytime somnolence, and novel associations with epilepsy and earlier menarche. Individuals with the highest 10% of PRS scores had increased risk of PD (OR=3.30, 95% CI 2.57-4.24) compared to the lowest risk decile. Higher PRS scores were associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm improved model performance (Nagelkerke pseudo-R2 0.0053, p=6.87x10-14). We found evidence of interaction between the PRS and diabetes. InterpretationHere we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity and predictive power of a polygenic risk score, and to demonstrate a novel gene-environment interaction, whereby the effect of diabetes on PD risk appears to depend on prior genetic risk for PD.

genetics↗

bioRxiv2019

The Parkinsons Disease Mendelian Randomization Research Portal

BackgroundMendelian randomization (MR) is a method for exploring observational associations to find evidence of causality.\n\nObjectiveTo apply MR between multiple risk factors/phenotypic traits (exposures) and Parkinsons disease (PD) in a large, unbiased manner, and to create a public resource for research.\n\nMethodsWe used two-sample MR in which the summary statistics relating to SNPs from genome wide association studies (GWASes) of 5,839 exposures curated on MR Base were used to assess causal relationships with PD. We selected the highest quality exposure GWASes for this report (n=401). For the disease outcome, summary statistics from the largest published PD GWAS were used. For each exposure, the causal effect on PD was assessed using the inverse variance weighted (IVW) method, followed by a range of sensitivity analyses. We used a false discovery rate (FDR) corrected p-value of <0.05 from the IVW analysis to prioritize traits of interest.\n\nResultsWe observed evidence for causal associations between twelve exposures and risk of PD. Of these, nine were causal effects related to increasing adiposity and decreasing risk of PD. The remaining top exposures that affected PD risk were tea drinking, time spent watching television and forced vital capacity, but the latter two appeared to be biased by violations of underlying MR assumptions.\n\nDiscussionWe present a new platform which offers MR analyses for a total of 5,839 GWASes versus the largest PD GWASes available (https://pdgenetics.shinyapps.io/pdgenetics/). Alongside, we report further evidence to support a causal role for adiposity on lowering the risk of PD.

neuroscience↗

bioRxiv2018

The BRadykinesia Akinesia INcoordination (BRAIN) tap test: capturing the sequence effect

BackgroundThe BRAIN tap test is an online keyboard tapping task that has been previously validated to assess upper limb motor function in Parkinsons disease (PD).\n\nObjectivesTo develop a new parameter which detects a sequence effect and to reliably distinguish between PD patients on and off medication. Alongside, we sought to validate a mobile version of the test for use on smartphones and tablet devices.\n\nMethodsBRAIN test scores in 61 patients with PD and 93 healthy controls were compared. A range of established parameters captured speed and accuracy of alternate taps. The new VS (Velocity Score) recorded the inter-tap speed. Decrement in the VS was used as a marker for the sequence effect. In the validation phase, 19 PD patients and 19 controls were tested using multiple types of hardware platforms including smart devices.\n\nResultsQuantified slopes from the VS demonstrated bradykinesia (sequence effect) in PD patients (slope cut-off -0.002) with sensitivity of 58% and specificity of 81% (discovery phase of the study) and sensitivity of 65% and specificity of 88% (validation phase). All BRAIN test parameters differentiated between on medication and off medication states in PD. Most BRAIN tap test parameters had high test-retest reliability values (ICC>0.75). Differentiation between PD patients and controls was possible on all hardware versions of the test.\n\nConclusionThe BRAIN tap test is a simple, user-friendly and free-to-use tool for assessment of upper limb motor dysfunction in PD, which now includes a measure of bradykinesia.

neuroscience↗

bioRxiv2018

The Parkinson’s Phenome: Traits Associated with Parkinson’s Disease in a Large and Deeply Phenotyped Cohort

BackgroundObservational studies have begun to characterize the wide spectrum of phenotypes associated with Parkinsons disease (PD), but recruiting large numbers of PD cases and assaying a diversity of phenotypes has often been difficult. Here, we set out to systematically describe the PD phenome using a cross-sectional case-control design in a large database.\n\nMethodsWe analyzed the association between PD and 840 phenotypes derived from online surveys. For each phenotype, we ran a logistic regression using an average of 5,141 PD cases and 65,459 age- and sex-matched controls. We selected uncorrelated phenotypes, determined statistical significance after correcting for multiple testing, and systematically assessed the novelty of each significant association. We tested whether significant phenotypes were also associated with disease duration in PD cases.\n\nFindingsPD diagnosis was associated with 149 independent phenotypes. We replicated 32 known associations and discovered 49 associations that have not previously been reported. We found that migraine, obsessive-compulsive disorder, seasonal allergies, and anemia were associated with PD, but were not significantly associated with PD duration, and tend to occur decades before the average age of diagnosis for PD. Further work is needed to determine whether these phenotypes are PD risk factors or whether they share common disease mechanisms.\n\nInterpretationWe used a systematic approach in a single large dataset to assess the spectrum of traits that were associated with PD. Some of these traits may be risk factors for PD, features of the pre-diagnostic phase of disease, or manifestations of PD pathology. The model outputs from all 840 logistic regressions are available to the research community and may be used to generate hypotheses regarding PD etiology.\n\nFundingThe Michael J. Fox Foundation, Parkinsons UK, Barts Charity, National Institute on Aging, and 23andMe, Inc.\n\nResearch in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe used PubMed to perform a MEDLINE database search for review articles published up to January 21st, 2018 that contained the keywords \"Parkinson\" and \"epidemiology\" in the title or abstract. We performed additional MEDLINE searches for each phenotype that was significantly associated with PD. Although dozens of phenotypes have been tested for an association with PD, only a few associations have been consistently repeatable (e.g. pesticide exposure, coffee consumption).\n\nAdded value of this studyWe systematically tested for an association between PD and 840 phenotypes using up to 13,546 cases and 1{middle dot}3 million controls, making this one of the largest PD epidemiology studies ever conducted. We discovered 49 novel associations that will need to be replicated or validated. We found 44 associations for phenotypes that have previously been studied in relation to PD, but for which an association has not been consistently demonstrated.\n\nImplications of all the available evidenceTaken together with results from previous studies, this series of case-control analyses adds evidence for associations between PD and many phenotypes that are not currently thought to be part of the canonical PD phenome. This work paves the way for future studies to assess whether any of these phenotypes represent PD risk factors and whether any of these risk factors are modifiable.

epidemiology↗