Linguistic changes in neurodegenerative diseases relate to clinical symptoms.

Background: The detection and characterization of speech changes may help in the identification and monitoring of neurodegenerative diseases. However, there is limited research validating the relationship between speech changes and clinical symptoms across a wide range of neurodegenerative diseases. Method: We analyzed speech recordings from 109 patients who were diagnosed with various neurodegenerative diseases, including Alzheimer's disease, Frontotemporal Dementia, and Vascular Cognitive Impairment, in a cognitive neurology memory clinic. Speech recordings of an open-ended picture description task were processed using the Winterlight speech analysis platform which generates >500 speech features, including the acoustics of speech and linguistic properties of spoken language. We investigated the relationship between the speech features and clinical assessments including the Mini Mental State Examination (MMSE), Mattis Dementia Rating Scale (DRS), Western Aphasia Battery (WAB), and Boston Naming Task (BNT) in a heterogeneous patient population. Result: Linguistic features including lexical and syntactic features were significantly correlated with clinical assessments in patients, across diagnoses. Lower MMSE and DRS scores were associated with the use of shorter words and fewer prepositional phrases. Increased impairment on WAB and BNT was correlated with the use of fewer nouns but more pronouns. Patients also differed from healthy adults as their speech duration was significantly shorter with more pauses. Conclusion: Linguistic changes such as the use of simpler vocabularies and syntax were detectable in patients with different neurodegenerative diseases and correlated with cognitive decline. Speech has the potential to be a sensitive measure for detecting cognitive impairments across various neurodegenerative diseases.

Evaluating the utility of daily speech assessments for monitoring depression symptoms.

Objective: Depression is a common mental health disorder and a major public health concern, significantly interfering with the lives of those affected. The complex clinical presentation of depression complicates symptom assessments. Day-to-day fluctuations of depression symptoms within an individual bring an additional barrier, since infrequent testing may not reveal symptom fluctuation. Digital measures such as speech can facilitate daily objective symptom evaluation. Here, we evaluated the effectiveness of daily speech assessment in characterizing speech fluctuations in the context of depression symptoms, which can be completed remotely, at a low cost and with relatively low administrative resources. Methods: Community volunteers (N = 16) completed a daily speech assessment, using the Winterlight Speech App, and Patient Health Questionnaire-9 (PHQ-9) for 30 consecutive business days. We calculated 230 acoustic and 290 linguistic features from individual's speech and investigated their relationship to depression symptoms at the intra-individual level through repeated measures analyses. Results: We observed that depression symptoms were linked to linguistic features, such as less frequent use of dominant and positive words. Greater depression symptomatology was also significantly correlated with acoustic features: reduced variability in speech intensity and increased jitter. Conclusions: Our findings support the feasibility of using acoustic and linguistic features as a measure of depression symptoms and propose daily speech assessment as a tool for better characterization of symptom fluctuations.

Functional brain activity constrained by structural connectivity reveals cohort-specific features for serum neurofilament light chain.

Background: Neuro-axonal brain damage releases neurofilament light chain (NfL) proteins, which enter the blood. Serum NfL has recently emerged as a promising biomarker for grading axonal damage, monitoring treatment responses, and prognosis in neurological diseases. Importantly, serum NfL levels also increase with aging, and the interpretation of serum NfL levels in neurological diseases is incomplete due to lack of a reliable model for age-related variation in serum NfL levels in healthy subjects. Methods: Graph signal processing (GSP) provides analytical tools, such as graph Fourier transform (GFT), to produce measures from functional dynamics of brain activity constrained by white matter anatomy. Here, we leveraged a set of features using GFT that quantified the coupling between blood oxygen level dependent signals and structural connectome to investigate their associations with serum NfL levels collected from healthy subjects and former athletes with history of concussions. Results: Here we show that GSP feature from isthmus cingulate in the right hemisphere (r-iCg) is strongly linked with serum NfL in healthy controls. In contrast, GSP features from temporal lobe and lingual areas in the left hemisphere and posterior cingulate in the right hemisphere are the most associated with serum NfL in former athletes. Additional analysis reveals that the GSP feature from r-iCg is associated with behavioral and structural measures that predict aggressive behavior in healthy controls and former athletes. Conclusions: Our results suggest that GSP-derived brain features may be included in models of baseline variance when evaluating NfL as a biomarker of neurological diseases and studying their impact on personality traits.

Brain Connectivity Changes in Postconcussion Syndrome as the Neural Substrate of a Heterogeneous Syndrome.

Background: Postconcussion syndrome (PCS) or persistent symptoms of concussion refers to a constellation of symptoms that persist for weeks and months after a concussion. To better capture the heterogeneity of the symptoms of patients with PCS, we aimed to separate patients into clinical subtypes based on brain connectivity changes. Methods: Subject-specific structural and functional connectomes were created based on diffusion weighted and resting state functional magnetic resonance imaging, respectively. Following an informed dimensionality reduction, a Gaussian mixture model was used on patient-specific structural and functional connectivity matrices to find potential patient clusters. For validation, the resulting patient subtypes were compared in terms of cognitive, neuropsychiatric, and postconcussive symptom differences. Results: Multimodal analyses of brain connectivity were predictive of behavioral outcomes. Our modeling revealed two patient subtypes: mild and severe. The severe subgroup showed significantly higher levels of depression, anxiety, aggression, and a greater number of symptoms than the mild patient subgroup. Conclusion: This study suggests that structural and functional connectivity changes together can help us better understand the symptom severity and neuropsychiatric profiles of patients with PCS. This work allows us to move toward precision medicine in concussions and provides a novel machine learning approach that can be applicable to other heterogeneous conditions.

Diffusion and functional MRI findings and their relationship to behaviour in postconcussion syndrome: a scoping review.

Postconcussion syndrome (PCS) is a term attributed to the constellation of symptoms that fail to recover after a concussion. PCS is associated with a variety of symptoms such as headaches, concentration deficits, fatigue, depression and anxiety that have an enormous impact on patients' lives. There is currently no diagnostic biomarker for PCS. There have been attempts at identifying structural and functional brain changes in patients with PCS, using diffusion tensor imaging (DTI) and functional MRI (fMRI), respectively, and relate them to specific PCS symptoms. In this scoping review, we appraised, synthesised and summarised all empirical studies that (1) investigated structural or functional brain changes in PCS using DTI or fMRI, respectively, and (2) assessed behavioural alterations in patients with PCS. We performed a literature search in MEDLINE (Ovid), Embase (Ovid) and PsycINFO (Ovid) for primary research articles published up to February 2020. We identified 8306 articles and included 45 articles that investigated the relationship between DTI and fMRI parameters and behavioural changes in patients with PCS: 20 diffusion, 20 fMRI studies and 5 papers with both modalities. Most frequently studied structures were the corpus callosum, superior longitudinal fasciculus in diffusion and the dorsolateral prefrontal cortex and default mode network in the fMRI literature. Although some white matter and fMRI changes were correlated with cognitive or neuropsychiatric symptoms, there were no consistent, converging findings on the relationship between neuroimaging abnormalities and behavioural changes which could be largely due to the complex and heterogeneous presentation of PCS. Furthermore, the heterogeneity of symptoms in PCS may preclude discovery of one biomarker for all patients. Further research should take advantage of multimodal neuroimaging to better understand the brain-behaviour relationship, with a focus on individual differences rather than on group comparisons.

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression.

Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.

The structure of hippocampal circuitry relates to rapid category learning in humans.

Prior work suggests that complementary white matter pathways within the hippocampus (HPC) differentially support the learning of specific versus general information. In particular, while the trisynaptic pathway (TSP) rapidly forms memories for specific experiences, the monosynaptic pathway (MSP) slowly learns generalities. However, despite the theorized significance of such circuitry, characterizing how information flows within the HPC to support learning in humans remains a challenge. We leveraged diffusion-weighted imaging as a proxy for individual differences in white matter structure linking key regions along with TSP (HPC subfields CA3 and CA1 ) and MSP (entorhinal cortex and CA1 ) and related these differences in hippocampal structure to category learning ability. We hypothesized that learning to categorize the "exception" items that deviated from category rules would benefit from TSP-supported mnemonic specificity. Participant-level estimates of TSP- and MSP-related integrity were constructed from HPC subfield connectomes of white matter streamline density. Consistent with theories of TSP-supported learning mechanisms, we found a specific association between the integrity of CA3 -CA1 white matter connections and exception learning. These results highlight the significant role of HPC circuitry in complex human learning.

Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and their frequencies controlled.

The wide variety of cell types and their biophysical complexities pose a challenge in our ability to understand oscillatory activities produced by cellular-based computational network models. This challenge stems from their high-dimensional and multiparametric natures. To overcome this, we implement a solution by linking minimal and detailed models of CA1 microcircuits that generate intrahippocampal (3-12 Hz) theta rhythms. We leverage insights from minimal models to guide explorations of more detailed models and obtain a cellular perspective of theta generation. Our findings distinguish the pyramidal cells as the theta rhythm initiators and reveal that their activity is regularized by the inhibitory cell populations, supporting a proposed hypothesis of an "inhibition-based tuning" mechanism. We find a strong correlation between input current to the pyramidal cells and the resulting local field potential theta frequency, indicating that intrinsic pyramidal cell properties underpin network frequency characteristics. This work provides a cellular-based foundation from which in vivo theta activities can be explored.

Fluid biomarkers of white matter hyperintensities in cerebrovascular disease and neurodegeneration: a systematic review protocol.

OBJECTIVE: The goal of this systematic review is to evaluate the association between fluid biomarkers and white matter hyperintensities (WMH) in cerebrovascular disease and neurodegenerative disorders. While previous research has examined the etiology of WMH in specific diseases, we propose a comprehensive framework encompassing WMH of both vascular and non-vascular origin. INTRODUCTION: Although WMH have been mostly described in aging populations with cerebrovascular disease, extensive lesions also occur in non-vascular diseases. Such lesions are traditionally treated as a separate pathological entity from vascular ones, but recent work has challenged the appropriateness of that framework when probing WMH etiology. Comparing biomarkers associated with WMH across various pathologies may improve our understanding of their etiology. INCLUSION CRITERIA: The review will focus on cerebrovascular disease and neurodegenerative disorders and exclude infectious, metabolic, drug-induced, or radiation-induced white matter diseases. Original, peer-reviewed research on the relationship of WMH on magnetic resonance imaging with blood/cerebrospinal fluid biomarkers will be considered for inclusion. Postmortem studies will guide the selection of biomarkers of interest and the interpretation of our findings. Genomic markers will be excluded. METHODS: The review will be conducted in accordance with PRISMA and JBI guidelines. English articles of interest published between 2000 and 2020 will be identified in MEDLINE and Embase. Two reviewers will perform abstract and full-text screening, standardized data extraction, and quality assessments of the selected studies. The relationship between each biomarker and WMH burden will be meta-analyzed, if possible, with subgroup or meta-regression analyses to assess differences between diseases. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42020218298.

Progression of neuropsychiatric symptoms in young-onset versus late-onset Alzheimer's disease.

Young-onset and late-onset Alzheimer's disease has different clinical presentations with late-onset presenting most often with memory deficits while young-onset often presents with a non-amnestic syndrome. However, it is unknown whether there are differences in presentation and progression of neuropsychiatric symptoms in young- versus late-onset Alzheimer's disease. We aimed to investigate differences in the prevalence and severity of neuropsychiatric symptoms in patients with young- and late-onset Alzheimer's disease longitudinally with and without accounting for the effect of medication usage. Sex differences were also considered in these patient groups. We included 126 young-onset and 505 late-onset Alzheimer's disease patients from National Alzheimer's Coordinating Center-Uniform Data Set (NACC-UDS) and Alzheimer's Disease Neuroimaging Initiative (ADNI). We investigated the prevalence and severity of neuropsychiatric symptoms using the Neuropsychiatric Inventory-Questionnaire over 4 visits with 1-year intervals, using a linear mixed-effects model. The prevalence of depression was significantly higher in young-onset than late-onset Alzheimer's disease over a 4-year interval when antidepressant usage was included in our analyses. Our findings suggest that neuropsychiatric symptom profiles of young- and late-onset Alzheimer's disease differ cross-sectionally but also display significant differences in progression.

Association Between Cerebrospinal Fluid Biomarkers and Age-related Brain Changes in Patients with Normal Pressure Hydrocephalus.

Our study aimed to: 1)investigate the diagnostic utility of CSF Aß42, t-tau, and p-tau to differentiate normal-pressure-hydrocephalus(NPH) from Alzheimer's-disease(AD) and normal-controls; and 2)investigate if age and ventricular size affect the levels of CSF biomarkers in NPH patients. We recruited 131 participants: (a)Suspected-NPH: 72 with ventriculomegaly and clinical symptoms of NPH. These participants were then divided into two groups of 1)Probable-NPH (N = 38) and 2)Unlikely-NPH (N = 34) based on whether participants experienced gait improvement after removal of a large amount of CSF; (b)AD group: 30 participants with CSF biomarkers and cognitive symptoms consistent with AD; (c)Control-group: 29 participants who were cognitively and functionally normal. Lower levels of CSF Aß42 and p-tau were observed in the probable-NPH compared to the normal controls(444.22 ± 163.3 vs. 1213.75 ± 556.5; and 26.05 ± 9.2 vs. 46.16 ± 13.3 pg/mL; respectively). Lower levels of CSF p-tau and t-tau were found in the probable-NPH compared to the AD(26.05 ± 9.2 vs. 114.95 ± 28.2; and 193.29 ± 92.3 vs. 822.65 ± 311.5 pg/mL; respectively) but the CSF-Aß42 was low in both the probable-NPH and AD. CSF-Aß42 correlated with age and Evans-index only in the probable-NPH(r = 0.460, p = 0.004; and r = -0.530, p = 0.001; respectively). Our study supports the hypothesis that age-related atrophy results in better Aß42 clearance in the CSF because of the increase in the interstitial space.