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.

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Our study aimed to: 1)investigate the diagnostic utility of CSF Aβ42, 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 Aβ42 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-Aβ42 was low in both the probable-NPH and AD. CSF-Aβ42 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 agerelated atrophy results in better Aβ42 clearance in the CSF because of the increase in the interstitial space. Normal pressure hydrocephalus (NPH) is a syndrome associated with enlarged ventricles without marked elevation in cerebrospinal fluid (CSF) pressure 1. Clinical symptoms include gait and balance impairment, cognitive deficits, and urinary urgency/incontinence 1. In both NPH and Alzheimer's disease (AD) decreased CSF levels of the amyloid β−42 (Aβ42) have been found; however, in contrast to AD, total tau (t-tau) and phospho-tau (p-tau) levels are not increased in NPH cases 2,3. It has been hypothesized that the low Aβ42 in the presence of low t-tau and p-tau is due to the decrease in interstitial space that precludes amyloid precursor protein (APP) fragments and tau proteins from being effectively cleared by CSF and consequently the levels of these proteins decrease in the CSF 4. This hypothesis was based on two major observations: 1) low levels of all APP fragments (i.e., Aβ38, Aβ40, Aβ42, sAPPα, and sAPPβ) in CSF obtained both by lumbar and ventricular methods of patients with NPH compared to health controls, which all increased back to normal after shunting 1 and 2) Aβ clearance from the interstitial fluid is increased during sleep when the size of interstitial space increases by 60% 5. The aims of our study were to: 1) investigate the diagnostic utility of CSF Aβ42, t-tau, and p-tau to differentiate NPH from AD and normal controls; and 2) investigate if age and ventricular size affect the levels of these CSF biomarkers in NPH patients. Answers to these questions would improve understanding of CSF Aβ42, t-tau, and p-tau production/clearance and facilitate ...

The wide variety of cell types and their inherent biophysical complexities pose a challenge to our understanding of oscillatory activities produced by cellular-based computational models. This challenge stems from the high-dimensional and multi-parametric nature of these systems. To overcome this issue, we implement systematic comparisons of minimal and detailed models of CA1 microcircuits that generate intra-hippocampal theta rhythms (3-12 Hz). We leverage insights from minimal models to guide detailed model explorations 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 an ‘inhibition-based tuning’ mechanism. We find a strong correlation between the pyramidal cell input current and the resulting LFP theta frequency, establishing that the intrinsic pyramidal cell properties underpin network frequency characteristics. This work provides a cellular-based foundation from which in vivo theta activities can be explored.

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.

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 CA 3 and CA 1 ) and MSP (entorhinal cortex and CA 1 ) 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 TSPsupported 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 CA 3 -CA 1 white matter connections and exception learning. These results highlight the significant role of HPC circuitry in complex human learning.

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.

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.