Neurological impressions on the organization of language networks in the human brain

Cerebral Glucose Metabolism in Patients with Chronic Disorders of Consciousness

OBJECTIVE

To measure regional cerebral metabolic rate of glucose (CMRGlu) in patients with chronic disorders of consciousness (DOCs) using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET).

METHODS

This retrospective cohort study examined 50 patients (mean age: 40.9 ± 20.1 years) with traumatic brain injury (TBI)-induced chronic DOCs [minimally conscious state (MCS)+, n = 20; MCS-, n = 15 and vegetative state (VS), n = 15]. We measured FDG-PET-based CMRGlu values in 12 regions of both brain hemispheres and compared those among MCS+, MCS - and VS patients.

RESULTS

In both hemispheres, the regional CMRGlu reduced with consciousness deterioration in 11 of 12 regions (91.7%). In seven right hemisphere regions, CMRGlu values were markedly higher in MCS+ patients than in MCS- patients. Furthermore, CMRGlu was suggestively higher in the left occipital region in MCS- patients than in VS patients.

CONCLUSION

Functional preservation in the left occipital region in patients with chronic DOCs might reflect an awareness of external environments, whereas extensive functional preservation in the right cerebral hemisphere might reflect communication motivation.

Dual-functional network regulation underlies the central executive system in working memory

The frontoparietal network (FPN) and cingulo-opercular network (CON) may exert top-down regulation corresponding to the central executive system (CES) in working memory (WM); however, contributions and regulatory mechanisms remain unclear. We examined network interaction mechanisms underpinning the CES by depicting CON- and FPN-mediated whole-brain information flow in WM. We used datasets from participants performing verbal and spatial working memory tasks, divided into encoding, maintenance, and probe stages. We used general linear models to obtain task-activated CON and FPN nodes to define regions of interest (ROI); an online meta-analysis defined alternative ROIs for validation. We calculated whole-brain functional connectivity (FC) maps seeded by CON and FPN nodes at each stage using beta sequence analysis. We used Granger causality analysis to obtain the connectivity maps and assess task-level information flow patterns. For verbal working memory, the CON functionally connected positively and negatively to task-dependent and task-independent networks, respectively, at all stages. FPN FC patterns were similar only in the encoding and maintenance stages. The CON elicited stronger task-level outputs. Main effects were: stable CON→FPN, CON→DMN, CON→visual areas, FPN→visual areas, and phonological areas→FPN. The CON and FPN both up-regulated task-dependent and down-regulated task-independent networks during encoding and probing. Task-level output was slightly stronger for the CON. CON→FPN, CON→DMN, visual areas→CON, and visual areas→FPN showed consistent effects. The CON and FPN might together underlie the CES's neural basis and achieve top-down regulation through information interaction with other large-scale functional networks, and the CON may be a higher-level regulatory core in WM.

Language discordance as a marker of disparities in cerebrovascular risk and stroke outcomes: A multi-center Canadian study

Background

Differences in ischemic stroke outcomes occur in those with limited English proficiency. These health disparities might arise when a patient's spoken language is discordant from the primary language utilized by the health system. Language concordance is an understudied concept. We examined whether language concordance is associated with differences in vascular risk or post-stroke functional outcomes, depression, obstructive sleep apnea and cognitive impairment.

Methods

This was a multi-center observational cross-sectional cohort study. Patients with ischemic stroke/transient ischemic attack (TIA) were consecutively recruited across eight regional stroke centers in Ontario, Canada (2012 - 2018). Participants were language concordant (LC) if they spoke English as their native language, ESL if they used English as a second language, or language discordant (LD) if non-English speaking and requiring translation.

Results

8156 screened patients. 6,556 met inclusion criteria: 5067 LC, 1207 ESL and 282 LD. Compared to LC patients: (i) ESL had increased odds of diabetes (OR = 1.28, p = 0.002), dyslipidemia (OR = 1.20, p = 0.007), and hypertension (OR = 1.37, p<0.001) (ii) LD speaking patients had an increased odds of having dyslipidemia (OR = 1.35, p = 0.034), hypertension (OR = 1.37, p<0.001), and worse functional outcome (OR = 1.66, p<0.0001). ESL (OR = 1.88, p<0.0001) and LD (OR = 1.71, p<0.0001) patients were more likely to have lower cognitive scores. No associations were noted with obstructive sleep apnea (OSA) or depression.

Conclusions

Measuring language concordance in stroke/TIA reveals differences in neurovascular risk and functional outcome among patients with limited proficiency in the primary language of their health system. Lower cognitive scores must be interpreted with caution as they may be influenced by translation and/or greater vascular risk. Language concordance is a simple, readily available marker to identify those at risk of worse functional outcome. Stroke systems and practitioners must now study why these differences exist and devise adaptive care models, treatments and education strategies to mitigate barriers influenced by language discordance.

RNN Language Processing Model-Driven Spoken Dialogue System Modeling Method

Speech recognition and semantic understanding of spoken language are critical components in determining the dialogue system's performance in SDS. In the study of SDS, the improvement of SLU performance is critical. By influencing the factors before and after the input text sequence information, RNN predicts the next text information. The RNN language model's probability score is introduced, and the recognition's intermediate result is rescored. A method of combining cache RNN models to optimize the decoding process and improve the accuracy of word sequence probability calculation of language model on test data is proposed to address the problem of mismatch between test data and training data in recognition. The results of the experiments show that the method proposed in this paper can effectively improve the recognition system's performance on the test set. It has the potential to achieve a higher SLU score. It is useful for future research on spoken dialogue and SLU issues.

Detecting Alzheimer's Disease Using Natural Language Processing of Referential Communication Task Transcripts

BACKGROUND

People with Alzheimer's disease (AD) often demonstrate difficulties in discourse production. Referential communication tasks (RCTs) are used to examine a speaker's capability to select and verbally code the characteristics of an object in interactive conversation.

OBJECTIVE

In this study, we used contextualized word representations from Natural language processing (NLP) to evaluate how well RCTs are able to distinguish between people with AD and cognitively healthy older adults.

METHODS

We adapted machine learning techniques to analyze manually transcribed speech transcripts in an RCT from 28 older adults, including 12 with AD and 16 cognitively healthy older adults. Two approaches were applied to classify these speech transcript samples: 1) using clinically relevant linguistic features, 2) using machine learned representations derived by a state-of-art pretrained NLP transfer learning model, Bidirectional Encoder Representation from Transformer (BERT) based classification model.

RESULTS

The results demonstrated the superior performance of AD detection using a designed transfer learning NLP algorithm. Moreover, the analysis showed that transcripts of a single image yielded high accuracies in AD detection.

CONCLUSION

The results indicated that RCT may be useful as a diagnostic tool for AD, and that the task can be simplified to a subset of images without significant sacrifice to diagnostic accuracy, which can make RCT an easier and more practical tool for AD diagnosis. The results also demonstrate the potential of RCT as a tool to better understand cognitive deficits from the perspective of discourse production in people with AD.

Automatic Subtyping of Individuals with Primary Progressive Aphasia

BACKGROUND

The classification of patients with primary progressive aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.

OBJECTIVE

The aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.

METHODS

In this paper, we present a machine learning model based on deep neural networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as to expert clinicians' classifications.

RESULTS

The DNN model outperformed the other machine learning models as well as expert clinicians' classifications with 80% classification accuracy. Importantly, 90% of patients with nfvPPA and 95% of patients with lvPPA was identified correctly, providing reliable subtyping of these patients into their corresponding PPA variants.

CONCLUSION

We show that the combined speech and language markers from connected speech productions can inform variant subtyping in patients with PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA.

Human brain function and brain-computer interfaces

Human brain function research has evolved dramatically in the last decades. In this chapter the role of modern methods of recording brain activity in understanding human brain function is explained. Current knowledge of brain function relevant to brain-computer interface (BCI) research is detailed, with an emphasis on the motor system which provides an exceptional level of detail to decoding of intended or attempted movements in paralyzed beneficiaries of BCI technology and translation to computer-mediated actions. BCI technologies that stand to benefit the most of the detailed organization of the human cortex are, and for the foreseeable future are likely to be, reliant on intracranial electrodes. These evolving technologies are expected to enable severely paralyzed people to regain the faculty of movement and speech in the coming decades.

Sentence Processing in Aphasia: An Examination of Material-Specific and General Cognitive Factors

The purpose of this study was to characterize further the nature of sentence processing deficits in acquired aphasia. Adults with aphasia and age-and education-matched adults with no brain damage completed a battery of formal cognitive-linguistic tests and an experimental sentence judgment task, which was performed alone and during focused attention and divided attention or dual-task conditions. The specific aims were to determine whether (a) increased extra-linguistic cognitive demands (i.e., focused and divided conditions) differentially affected the sentence judgement performances of the aphasic and control groups, (b) increased extra- linguistic cognitive demands interact with stimulus parameters (i.e., syntactic complexity, number of propositions) known to influence sentence processing, and (c) syntactic- or material specific resource limitations (e.g., sentence judgment in isolation), general cognitive abilities (e.g., short-term and working memory test scores), or both share a significant relationship with dual-task outcomes. Accuracy, grammatical sensitivity, and reaction time findings were consistent with resource models of aphasia and processing accounts of aphasic syntactic limitations, underscoring the theoretical and clinical importance of acknowledging and specifying the strength and nature of interactions between linguistic and extra-linguistic cognitive processes in not only individuals with aphasia, but also other patient and typical aging populations.

The Effect of Speech Repetition Rate on Neural Activation in Healthy Adults: Implications for Treatment of Aphasia and Other Fluency Disorders

Functional imaging studies have provided insight into the effect of rate on production of syllables, pseudowords, and naturalistic speech, but the influence of rate on repetition of commonly-used words/phrases suitable for therapeutic use merits closer examination. Aim: To identify speech-motor regions responsive to rate and test the hypothesis that those regions would provide greater support as rates increase, we used an overt speech repetition task and functional magnetic resonance imaging (fMRI) to capture rate-modulated activation within speech-motor regions and determine whether modulations occur linearly and/or show hemispheric preference. Methods: Twelve healthy, right-handed adults participated in an fMRI task requiring overt repetition of commonly-used words/phrases at rates of 1, 2, and 3 syllables/second (syll./sec.). Results: Across all rates, bilateral activation was found both in ventral portions of primary sensorimotor cortex and middle and superior temporal regions. A repeated measures analysis of variance with pairwise comparisons revealed an overall difference between rates in temporal lobe regions of interest (ROIs) bilaterally (p < 0.001); all six comparisons reached significance (p < 0.05). Five of the six were highly significant (p < 0.008), while the left-hemisphere 2- vs. 3-syll./sec. comparison, though still significant, was less robust (p = 0.037). Temporal ROI mean beta-values increased linearly across the three rates bilaterally. Significant rate effects observed in the temporal lobes were slightly more pronounced in the right-hemisphere. No significant overall rate differences were seen in sensorimotor ROIs, nor was there a clear hemispheric effect. Conclusion: Linear effects in superior temporal ROIs suggest that sensory feedback corresponds directly to task demands. The lesser degree of significance in left-hemisphere activation at the faster, closer-to-normal rate may represent an increase in neural efficiency (and therefore, decreased demand) when the task so closely approximates a highly-practiced function. The presence of significant bilateral activation during overt repetition of words/phrases at all three rates suggests that repetition-based speech production may draw support from either or both hemispheres. This bihemispheric redundancy in regions associated with speech-motor control and their sensitivity to changes in rate may play an important role in interventions for nonfluent aphasia and other fluency disorders, particularly when right-hemisphere structures are the sole remaining pathway for production of meaningful speech.

Pre-stroke employment results in better patient-reported outcomes after minor stroke: Short title: Functional outcomes after minor stroke

OBJECTIVE

Individuals with "minor stroke" lack a dense hemiparesis or aphasia; however, commonly endorse persistent cognitive and motor problems despite low NIHSS scores. They also report problems with mood, energy, and the ability to think clearly that are less well characterized. Socioeconomic factors and stroke severity can influence patient-reported outcomes. In this study we explore patient-reported outcomes and the influence of these factors after minor stroke.

PATIENTS AND METHODS

Patients returning to clinic post-stroke with an NIHSS of ≤ 4 were administered a scale to quantify problems with daily activities and resulting functional burden, along with measures of fatigue and depression. T-tests, chi square analysis, and linear regression were used to compare functional outcomes of patients to controls (TIA or stroke mimic (n = 40)), and evaluate the association between patient-reported outcomes, stroke characteristics, and socioeconomic factors.

RESULTS

151 stroke patients were seen a mean 83.6 days post-infarct. Patients reported more problems (11.7 versus 6.9, p = 0.02), resulting in higher functional burden (26.5 versus 12.3, p = 0.01), increased depression (p = 0.07), and greater fatigue (p = 0.02) compared to controls. There was no relationship between stroke characteristics (other than NIHSS), baseline education, income, marital status, or living situation and perception of recovery; however, those actively working prior to their stroke reported better outcomes across all categories (p for each <0.02), and differences persisted in multivariable regression models.

CONCLUSION

Prior occupational status may represent an important prognostic indicator for patients with minor stroke. Individuals working at the time of their infarct report better functional outcomes irrespective of age or stroke severity.