forager: a Python package and web interface for modeling mental search.

Analyzing data from the verbal fluency task (e.g., "name all the animals you can in a minute") is of interest to both memory researchers and clinicians due to its broader implications for memory search and retrieval. Recent work has proposed several computational models to examine nuanced differences in search behavior, which can provide insights into the mechanisms underlying memory search. A prominent account of memory search within the fluency task was proposed by Hills et al. (2012), where mental search is modeled after how animals forage for food in physical space. Despite the broad potential utility of these models to scientists and clinicians, there is currently no open-source program to apply and compare existing foraging models or clustering algorithms without extensive, often redundant programming. To remove this barrier to studying search patterns in the fluency task, we created forager, a Python package ( https://github.com/thelexiconlab/forager ) and web interface ( https://forager.research.bowdoin.edu/ ). forager provides multiple automated methods to designate clusters and switches within a fluency list, implements a novel set of computational models that can examine the influence of multiple lexical sources (semantic, phonological, and frequency) on memory search using semantic embeddings, and also enables researchers to evaluate relative model performance at the individual and group level. The package and web interface cater to users with various levels of programming experience. In this work, we introduce forager's basic functionality and use cases that demonstrate its utility with pre-existing behavioral and clinical data sets of the semantic fluency task.

MicroRNA-195 rescues ApoE4-induced cognitive deficits and lysosomal defects in Alzheimer's disease pathogenesis.

Our recent findings link the apolipoprotein E4 (ApoE4)-specific changes in brain phosphoinositol biphosphate (PIP2) homeostasis to the susceptibility of developing Alzheimer's Disease (AD). In the present study, we have identified miR-195 as a top micro-RNA candidate involved in the ApoE/PIP2 pathway using miRNA profiles in human ROSMAP datasets and mouse microarray studies. Further validation studies have demonstrated that levels of miR-195 are significantly lower in human brain tissue of ApoE4+/- patients with clinical diagnosis of mild cognitive impairment (MCI) or early AD when compared to ApoE4-/- subjects. In addition, brain miR-195 levels are reduced along with disease progression from normal aging to early AD, and cerebrospinal fluid (CSF) miR-195 levels of MCI subjects are positively correlated with cognitive performances as measured by mini-mental status examination (MMSE) and negatively correlated with CSF tau levels, suggesting the involvement of miR-195 in early development of AD with a potential impact on cognition. Similar differences in miR-195 levels are seen in ApoE4+/+ mouse hippocampal brain tissue and cultured neurons when compared to ApoE3+/+ counterparts. Over-expressing miR-195 reduces expression levels of its top predicted target synaptojanin 1 (synj1), a brain PIP2-degrading enzyme. Furthermore, elevating miR-195 ameliorates cognitive deficits, amyloid plaque burden, and tau hyper-phosphorylation in ApoE4+/+ mice. In addition, elevating miR-195 rescues AD-related lysosomal defects in inducible pluripotent stem cells (iPSCs)-derived brain cells of ApoE4+/+ AD subjects while inhibiting miR-195 exacerbates these phenotypes. Together, our data uncover a novel regulatory mechanism of miR-195 targeted at ApoE4-associated brain PIP2 dyshomeostasis, cognitive deficits, and AD pathology.

Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative.

IMPORTANCE: Depression is an illness affecting a large percentage of the world's population throughout the lifetime. To date, there is no available biomarker for depression detection and tracking of symptoms relies on patient self-report. OBJECTIVE: To explore and validate features extracted from recorded voice samples of depressed subjects as digital biomarkers for suicidality, psychomotor disturbance, and depression severity. DESIGN: We conducted a cross-sectional study over the course of 12 months using a frequently visited web form version of the PHQ9 hosted by Mental Health America (MHA) to ask subjects for anonymous voice samples via a separate web form hosted by NeuroLex Laboratories. Subjects were asked to provide demographics, answers to the PHQ9, and two voice samples. SETTING: Online only. PARTICIPANTS: Users of the MHA website. MAIN OUTCOMES AND MEASURES: Performance of statistical models using extracted voice features to predict psychomotor disturbance, suicidality, and depression severity as indicated by the PHQ9. RESULTS: Voice features extracted from recorded audio of depressed subjects were able to predict PHQ9 question 9 and total scores with an area under the curve of 0.821 and a mean absolute error of 4.7, respectively. Psychomotor Disturbance prediction was less powerful with an area under the curve of 0.61. CONCLUSION AND RELEVANCE: Automated voice analysis using short recordings of patient speech may be used to augment depression screen and symptom management.

Macroscopic theory for capillary-pressure hysteresis.

In this article, we present a theory of macroscopic contact angle hysteresis by considering the minimization of the Helmholtz free energy of a solid-liquid-gas system over a convex set, subject to a constant volume constraint. The liquid and solid surfaces in contact are assumed to adhere weakly to each other, causing the interfacial energy to be set-valued. A simple calculus of variations argument for the minimization of the Helmholtz energy leads to the Young-Laplace equation for the drop surface in contact with the gas and a variational inequality that yields contact angle hysteresis for advancing/receding flow. We also show that the Young-Laplace equation with a Dirichlet boundary condition together with the variational inequality yields a basic hysteresis operator that describes the relationship between capillary pressure and volume. We validate the theory using results from the experiment for a sessile macroscopic drop. Although the capillary effect is a complex phenomenon even for a droplet as various points along the contact line might be pinned, the capillary pressure and volume of the drop are scalar variables that encapsulate the global quasistatic energy information for the entire droplet. Studying the capillary pressure versus volume relationship greatly simplifies the understanding and modeling of the phenomenon just as scalar magnetic hysteresis graphs greatly aided the modeling of devices with magnetic materials.

Sex specific molecular networks and key drivers of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a progressive and age-associated neurodegenerative disorder that affects women disproportionally. However, the underlying mechanisms are poorly characterized. Moreover, while the interplay between sex and ApoE genotype in AD has been investigated, multi-omics studies to understand this interaction are limited. Therefore, we applied systems biology approaches to investigate sex-specific molecular networks of AD. METHODS: We integrated large-scale human postmortem brain transcriptomic data of AD from two cohorts (MSBB and ROSMAP) via multiscale network analysis and identified key drivers with sexually dimorphic expression patterns and/or different responses to APOE genotypes between sexes. The expression patterns and functional relevance of the top sex-specific network driver of AD were further investigated using postmortem human brain samples and gene perturbation experiments in AD mouse models. RESULTS: Gene expression changes in AD versus control were identified for each sex. Gene co-expression networks were constructed for each sex to identify AD-associated co-expressed gene modules shared by males and females or specific to each sex. Key network regulators were further identified as potential drivers of sex differences in AD development. LRP10 was identified as a top driver of the sex differences in AD pathogenesis and manifestation. Changes of LRP10 expression at the mRNA and protein levels were further validated in human AD brain samples. Gene perturbation experiments in EFAD mouse models demonstrated that LRP10 differentially affected cognitive function and AD pathology in sex- and APOE genotype-specific manners. A comprehensive mapping of brain cells in LRP10 over-expressed (OE) female E4FAD mice suggested neurons and microglia as the most affected cell populations. The female-specific targets of LRP10 identified from the single cell RNA-sequencing (scRNA-seq) data of the LRP10 OE E4FAD mouse brains were significantly enriched in the LRP10-centered subnetworks in female AD subjects, validating LRP10 as a key network regulator of AD in females. Eight LRP10 binding partners were identified by the yeast two-hybrid system screening, and LRP10 over-expression reduced the association of LRP10 with one binding partner CD34. CONCLUSIONS: These findings provide insights into key mechanisms mediating sex differences in AD pathogenesis and will facilitate the development of sex- and APOE genotype-specific therapies for AD.

Sex Differences in Alzheimer's Disease: Insights From the Multiomics Landscape.

Alzheimer's disease (AD) has complex etiologies, and the impact of sex on AD varies over the course of disease development. The literature provides some evidence of sex-specific contributions to AD. However, molecular mechanisms of sex-biased differences in AD remain elusive. Multiomics data in tandem with systems biology approaches offer a new avenue to dissect sex-stratified molecular mechanisms of AD and to develop sex-specific diagnostic and therapeutic strategies for AD. Single-cell transcriptomic datasets and cell deconvolution of bulk tissue transcriptomic data provide additional insights into brain cell type-specific impact on sex-biased differences in AD. In this review, we summarize the impact of sex chromosomes and sex hormones on AD, the impact of sex-biased differences during AD development, and the interplay between sex and a major AD genetic risk factor, the APOE ε4 genotype, through the multiomics landscape. Several sex-biased molecular pathways such as neuroinflammation and bioenergetic metabolism have been identified. The importance of sex chromosome and sex hormones, as well as the associated pathways in AD pathogenesis, is further strengthened by findings from omics studies. Future research efforts should integrate the multiomics data from different brain regions and different cell types using systems biology approaches, and leverage the knowledge into a holistic examination of sex differences in AD. Advances in systems biology technologies and increasingly available large-scale multiomics datasets will facilitate future studies dissecting such complex signaling mechanisms to better understand AD pathogenesis in both sexes, with the ultimate goals of developing efficacious sex- and APOE-stratified preventive and therapeutic interventions for AD.

Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort.

AIM: Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905-22) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features. METHODS: Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants' Mini-Mental State Examination (MMSE) scores. RESULTS: Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869]. CONCLUSIONS: Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment.

Multimodal Automatic Coding of Client Behavior in Motivational Interviewing.

Motivational Interviewing (MI) is defined as a collaborative conversation style that evokes the client's own intrinsic reasons for behavioral change. In MI research, the clients' attitude (willingness or resistance) toward change as expressed through language, has been identified as an important indicator of their subsequent behavior change. Automated coding of these indicators provides systematic and efficient means for the analysis and assessment of MI therapy sessions. In this paper, we study and analyze behavioral cues in client language and speech that bear indications of the client's behavior toward change during a therapy session, using a database of dyadic motivational interviews between therapists and clients with alcohol-related problems. Deep language and voice encoders, i.e., BERT and VGGish, trained on large amounts of data are used to extract features from each utterance. We develop a neural network to automatically detect the MI codes using both the clients' and therapists' language and clients' voice, and demonstrate the importance of semantic context in such detection. Additionally, we develop machine learning models for predicting alcohol-use behavioral outcomes of clients through language and voice analysis. Our analysis demonstrates that we are able to estimate MI codes using clients' textual utterances along with preceding textual context from both the therapist and client, reaching an F1-score of 0.72 for a speaker-independent three-class classification. We also report initial results for using the clients' data for predicting behavioral outcomes, which outlines the direction for future work.

Assessing the Utility of Language and Voice Biomarkers to Predict Cognitive Impairment in the Framingham Heart Study Cognitive Aging Cohort Data.

BACKGROUND: There is a need for fast, accessible, low-cost, and accurate diagnostic methods for early detection of cognitive decline. Dementia diagnoses are usually made years after symptom onset, missing a window of opportunity for early intervention. OBJECTIVE: To evaluate the use of recorded voice features as proxies for cognitive function by using neuropsychological test measures and existing dementia diagnoses. METHODS: This study analyzed 170 audio recordings, transcripts, and paired neuropsychological test results from 135 participants selected from the Framingham Heart Study (FHS), which includes 97 recordings of cognitively normal participants and 73 recordings of cognitively impaired participants. Acoustic and linguistic features of the voice samples were correlated with cognitive performance measures to verify their association. RESULTS: Language and voice features, when combined with demographic variables, performed with an AUC of 0.942 (95% CI 0.929-0.983) in predicting cognitive status. Features with good predictive power included the acoustic features mean spectral slope in the 500-1500 Hz band, variation in the F2 bandwidth, and variation in the Mel-Frequency Cepstral Coefficient (MFCC) 1; the demographic features employment, education, and age; and the text features of number of words, number of compound words, number of unique nouns, and number of proper names. CONCLUSION: Several linguistic and acoustic biomarkers show correlations and predictive power with regard to neuropsychological testing results and cognitive impairment diagnoses, including dementia. This initial study paves the way for a follow-up comprehensive study incorporating the entire FHS cohort.

Sex differences in Alzheimer's disease: Understanding the molecular impact.

Alzheimer's disease (AD) is a common neurodegenerative disorder that presents with cognitive impairment and behavioral disturbance. Approximately 5.5 million people in the United States live with AD, most of whom are over the age of 65 with two-thirds being woman. There have been major advancements over the last decade or so in the understanding of AD neuropathological changes and genetic involvement. However, studies of sex impact in AD have not been adequately integrated into the investigation of disease development and progression. It becomes indispensable to acknowledge in both basic science and clinical research studies the importance of understanding sex-specific differences in AD pathophysiology and pathogenesis, which could guide future effort in the discovery of novel targets for AD. Here, we review the latest and most relevant literature on this topic, highlighting the importance of understanding sex dimorphism from a molecular perspective and its association to clinical trial design and development in AD research field.

Endo-lysosomal pathway and ubiquitin-proteasome system dysfunction in Alzheimer's disease pathogenesis.

Several lines of evidence have shown that defects in the endo-lysosomal autophagy degradation pathway and the ubiquitin-proteasome system play a role in Alzheimer's Disease (AD) pathogenesis and pathophysiology. Early pathological changes, such as marked enlargement of endosomal compartments, gradual accumulation of autophagic vacuoles (AVs) and lysosome dyshomeostasis, are well-recognized in AD. In addition to these pathological indicators, many genetic variants of key regulators in the endo-lysosomal autophagy networks and the ubiquitin-proteasome system have been found to be associated with AD. Furthermore, altered expression levels of key proteins in these pathways have been found in AD human brain tissues, primary cells and AD mouse models. In this review, we discuss potential disease mechanisms underlying the dysregulation of protein homeostasis governing systems. While the importance of two major protein degradation pathways in AD pathogenesis has been highlighted, targeted therapy at key components of these pathways has great potential in developing novel therapeutic interventions for AD. Future investigations are needed to define molecular mechanisms by which these complex regulatory systems become malfunctional at specific stages of AD development and progression, which will facilitate future development of novel therapeutic interventions. It is also critical to investigate all key components of the protein degradation pathways, both upstream and downstream, to improve our abilities to manipulate transport pathways with higher efficacy and less side effects.

Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network.

The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI observations of the gravid abdomen that are acquired at low spatial resolution but relatively high temporal resolution and over long durations (10-30 minutes). We estimate fetal pose per frame in MRI volumes of the pregnant abdomen via deep learning algorithms that detect key fetal landmarks. Evaluation of the proposed method shows that our framework achieves quantitatively an average error of 4.47 mm and 96.4% accuracy (with error less than 10 mm). Fetal pose estimation in MRI time series yields novel means of quantifying fetal movements in health and disease, and enables the learning of kinematic models that may enhance prospective mitigation of fetal motion artifacts during MRI acquisition.

Neuropathic pain modulation after spinal cord injury by breathing-controlled electrical stimulation (BreEStim) is associated with restoration of autonomic dysfunction.

BACKGROUND: Recent findings have implicated supraspinal origins from the pain neuromatrix- central autonomic network (PNM-CAN) in the generation of neuropathic pain (NP) after spinal cord injury (SCI). The aim of this study was to further investigate the theorized PNM-CAN mechanisms in persons with SCI by using a centrally directed pain intervention, provided by breathing-controlled electrical stimulation (BreEStim), to measure resultant autonomic changes measured by time and frequency domain heart rate variability (HRV) analysis. METHODS: Null and active BreEStim interventions were administered to SCI+NP subjects (n=10) in a random order. HRV data and VAS pain scores were collected at resting pre-test and 30 minutes post-test time points. Resting HRV data were also collected from SCI-NP subjects (n=11). RESULTS: SCI+NP subjects demonstrated a lower baseline HRV and parasympathetic tone, via SD of the normal-to-normal intervals (SDNN) and low frequency (LF) parameters, compared with SCI-NP subjects. However, following active BreEStim, SCI+NP subjects exhibited an increase in HRV and parasympathetic tone, most notably via pairs of successive R-R beat lengths varying by greater than 50 ms (NN50) and proportion of NN50 for total number of beats (pNN50) parameters along with lower VAS scores. Additionally, the post-test SCI+NP group was found to have a statistically comparable autonomic profile to the SCI-NP group across all HRV variables, including SDNN and LF parameters. CONCLUSION: The analgesic effects of active BreEStim in SCI+NP subjects were associated with restoration of autonomic dysfunction in this population.

Heart Rate Variability: A Novel Modality for Diagnosing Neuropathic Pain after Spinal Cord Injury.

Background: Heart rate variability (HRV), the physiological variance in the heart's R-R interval length, can be analyzed to produce various parameters reflective of one's autonomic balance. HRV analysis may be used to capture those autonomic aberrations associated with chronic neuropathic pain (NP) in spinal cord injury (SCI). This study assesses the capacity of HRV parameters to diagnose NP in an SCI cohort. Methods: An electrocardiogram (ECG) was collected at rest from able bodied participants (AB, n = 15), participants with SCI only (SCI-NP, n = 11), and those with SCI and NP (SCI+NP, n = 20). HRV parameters were analyzed using conventional time and frequency analysis. Results: At rest, there were no heart rate differences amongst groups. However, SCI+NP participants demonstrated lower overall HRV, as determined by the SDNN time domain parameter, compared to either AB (p < 0.01) or SCI-NP (p < 0.05) groups. Moreover, AB and SCI-NP participants were statistically comparable for all HRV time and frequency domain parameters. Additional analyses demonstrated no differences in HRV parameters between T4, above vs. T5, below SCI groups (for all parameters: p > 0.15) or between C8, above vs. T1, below SCI groups (p > 0.30). Conclusions: Participants with SCI and NP exhibit a lower overall HRV, which can be determined by HRV time domain parameter SDNN. HRV analysis is an innovative modality with the capacity for objective quantification of chronic NP in participants with SCI.