Deficits in Auditory and Visual Sensory Discrimination Reflect a Genetic Liability for Psychosis and Predict Disruptions in Global Cognitive Functioning

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation

BACKGROUND

Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders.

HYPOTHESIS

Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD).

STUDY DESIGN

From a single-center cohort (N = 735, 51.6% male) comprising healthy control individuals (HC, N = 245) and inpatients suffering from SCZ (N = 250) or MDD (N = 240), we acquired resting-state 19 channel-EEG recordings. Using repeated nested cross-validation, support vector machine models were trained to (1) classify patients with SCZ or MDD and HC individuals and (2) predict age in HC individuals. The age model was applied to patient groups to calculate Electrophysiological Age Gap Estimation (EphysAGE) as the difference between predicted and chronological age. The links between EphysAGE, diagnosis, and medication were then further explored.

STUDY RESULTS

The classification models robustly discriminated SCZ from HC (balanced accuracy, BAC = 72.7%, P < .001), MDD from HC (BAC = 67.0%, P < .001), and SCZ from MDD individuals (BAC = 63.2%, P < .001). Notably, central alpha (8-11 Hz) power decrease was the most consistently predictive feature for SCZ and MDD. Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC = 0.23, P < .001; ρMDD = 0.17, P = .01).

CONCLUSIONS

ML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.

Early auditory processing abnormalities alter individual learning trajectories and sensitivity to computerized cognitive training in schizophrenia

BACKGROUND

Auditory system plasticity is a promising target for neuromodulation, cognitive rehabilitation and therapeutic development in schizophrenia (SZ). Auditory-based targeted cognitive training (TCT) is a 'bottom up' intervention designed to enhance the speed and accuracy of auditory information processing, which has been shown to improve neurocognition in certain SZ patients. However, the dynamics of TCT learning as a function of training exercises and their impact on neurocognitive functioning and therapeutic outcomes are unknown.

METHODS

Forty subjects (SZ, n = 21; healthy subjects (HS), n = 19) underwent comprehensive clinical, cognitive, and auditory assessments, including measurements of auditory processing speed (APS) at baseline and after 1-h of TCT. SZ patients additionally completed 30-hours of TCT and repeated assessments ~10-12 weeks later.

RESULTS

SZ patients were deficient in APS at baseline (d = 0.96, p < 0.005) relative to HS. After 1-h of TCT, analyses revealed significant main effects of diagnosis (d = 1.75, p = 0.002) and time (d = 1.04, p < 0.001), and a diagnosis × time interaction (d = 0.85, p = 0.02) on APS. APS learning effects were robust after 1-h in SZ patients (d = 1.47, p < 0.001) and persisted throughout the 30-h of training. Baseline APS was associated with verbal learning gains after 30-h of TCT (r = 0.51, p = 0.02) in SZ.

CONCLUSIONS

TCT learning metrics may have prognostic utility and aid in the prospective identification of individuals likely to benefit from TCT. Future experimental medicine studies may advance predictive algorithms that enhance TCT-related clinical, cognitive and functional outcomes.

The Real-World Usability, Feasibility, and Performance Distributions of Deploying a Digital Toolbox of Computerized Assessments to Remotely Evaluate Brain Health: Development and Usability Study

BACKGROUND

An ongoing global challenge is managing brain health and understanding how performance changes across the lifespan.

OBJECTIVE

We developed and deployed a set of self-administrable, computerized assessments designed to measure key indexes of brain health across the visual and auditory sensory modalities. In this pilot study, we evaluated the usability, feasibility, and performance distributions of the assessments in a home-based, real-world setting without supervision.

METHODS

Potential participants were untrained users who self-registered on an existing brain training app called BrainHQ. Participants were contacted via a recruitment email and registered remotely to complete a demographics questionnaire and 29 unique assessments on their personal devices. We examined participant engagement, descriptive and psychometric properties of the assessments, associations between performance and self-reported demographic variables, cognitive profiles, and factor loadings.

RESULTS

Of the 365,782 potential participants contacted via a recruitment email, 414 (0.11%) registered, of whom 367 (88.6%) completed at least one assessment and 104 (25.1%) completed all 29 assessments. Registered participants were, on average, aged 63.6 (SD 14.8; range 13-107) years, mostly female (265/414, 64%), educated (329/414, 79.5% with a degree), and White (349/414, 84.3% White and 48/414, 11.6% people of color). A total of 72% (21/29) of the assessments showed no ceiling or floor effects or had easily modifiable score bounds to eliminate these effects. When correlating performance with self-reported demographic variables, 72% (21/29) of the assessments were sensitive to age, 72% (21/29) of the assessments were insensitive to gender, 93% (27/29) of the assessments were insensitive to race and ethnicity, and 93% (27/29) of the assessments were insensitive to education-based differences. Assessments were brief, with a mean duration of 3 (SD 1.0) minutes per task. The pattern of performance across the assessments revealed distinctive cognitive profiles and loaded onto 4 independent factors.

CONCLUSIONS

The assessments were both usable and feasible and warrant a full normative study. A digital toolbox of scalable and self-administrable assessments that can evaluate brain health at a glance (and longitudinally) may lead to novel future applications across clinical trials, diagnostics, and performance optimization.

Neurological soft signs in first episode psychosis among psychiatric hospital patients and its relationship with dimensions of psychopathology: A comparative study

Background

Neurological soft signs (NSS), as subtle, nonlocalising neurological abnormalities, are considered as the potential markers of psychosis. However, comparative studies of antipsychotic-naïve patients with first-episode psychosis (FEP) and first degree relatives (FDRs) are uncommon. We compared the prevalence and pattern of NSS in FEPs, their healthy FDRs and a healthy non-relatives' control group (HC), highlighted the relationship between NSS and psychopathology and proposed cut-off scores for prevalence studies.

Materials and Methods

Two hundred and two participants per group were recruited. The FEPs were consecutive attendees; FDRs were accompanying caregivers; while the HC were from hospital staff. The Brief Psychiatric Rating Scale and the Neurological Evaluation Scale were used to assess psychopathology dimensions and NSS, respectively.

Results

Using an item score of two ('substantial impairment'), the prevalence of at least one NSS was: 91.5% (95% confidence interval [CI]: 86.7%-94.9%), 16.8% (95% CI: 11.8%-22.7%) and 6.5% (95% CI: 3.5%-10.9%), respectively, for FEP, FDRs and HC. FEPs were impaired in a broad range of signs. The noteworthy relationships were as follows: (i) a significant correlation between the negative symptoms' dimension versus number of NSS (r = 0.4), and NSS total score (r = 0.3), (ii) the anxiety/depression dimension correlated negatively with number of NSS (r = -0.3) and (iii) NSS cut across psychosis categories. We propose a cut-off score of ≥ 4 for the number of signs signifying probable impairment.

Conclusion

The findings indicate that, subject to further studies, NSS could be regarded as a broader phenotype of neurologic dysfunction associated with psychosis proness.

People with high schizotypy experience more illusions in the Pattern Glare Test: Consistent with the hyperexcitability hypothesis

Individuals diagnosed with schizophrenia spectrum disorders (SSD) exhibit a constellation of sensory and perceptual impairments, including hyporeactivity to external input. However, individuals with SSD also report subjective experiences of sensory flooding, suggesting sensory hyperexcitability. To identify the extent to which behavioural indices of hyperexcitability are related to non-psychotic symptoms of schizophrenia, we tested a non-clinical population measured for schizophrenia-like traits (schizotypy), and a behavioural measure of sensory hyperexcitability, specifically the number of illusions seen in the Pattern Glare Test. Two samples totaling 913 individuals completed an online version of the Schizotypal Personality Questionnaire - Brief Revised (SPQ-BR) and the Pattern Glare Test. Individuals with higher schizotypy traits reported more illusions in the Pattern Glare Test. Additionally, one of the three SPQ-BR factors, the disorganized factor, significantly predicted the number of illusions reported. These data illustrate the potential for research in non-clinical samples to inform clinically relevant research.

A novel neighborhood rough set-based feature selection method and its application to biomarker identification of schizophrenia

Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.

Sweet spot in music-Is predictability preferred among persons with psychotic-like experiences or autistic traits?

People prefer music with an intermediate level of predictability; not so predictable as to be boring, yet not so unpredictable that it ceases to be music. This sweet spot for predictability varies due to differences in the perception of predictability. The symptoms of both psychosis and Autism Spectrum Disorder have been attributed to overestimation of uncertainty, which predicts a preference for predictable stimuli and environments. In a pre-registered study, we tested this prediction by investigating whether psychotic and autistic traits were associated with a higher preference for predictability in music. Participants from the general population were presented with twenty-nine pre-composed music excerpts, scored on their complexity by musical experts. A participant's preferred level of predictability corresponded to the peak of the inverted U-shaped curve between music complexity and liking (i.e., a Wundt curve). We found that the sweet spot for predictability did indeed vary between individuals. Contrary to predictions, we did not find support for these variations being associated with autistic and psychotic traits. The findings are discussed in the context of the Wundt curve and the use of naturalistic stimuli. We also provide recommendations for further exploration.

Early evoked brain activity underlies auditory and audiovisual speech recognition deficits in schizophrenia

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Reduced N1 amplitudes reflect speech processing deficits in schizophrenia (SZ).

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Crossmodal N1 amplitude suppression in audiovisual speech is preserved in SZ.

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N1 amplitudes correlate with speech recognition performance in controls but not in SZ.

Objectives

People with Schizophrenia (SZ) show deficits in auditory and audiovisual speech recognition. It is possible that these deficits are related to aberrant early sensory processing, combined with an impaired ability to utilize visual cues to improve speech recognition. In this electroencephalography study we tested this by having SZ and healthy controls (HC) identify different unisensory auditory and bisensory audiovisual syllables at different auditory noise levels.

Methods

SZ (N = 24) and HC (N = 21) identified one of three different syllables (/da/, /ga/, /ta/) at three different noise levels (no, low, high). Half the trials were unisensory auditory and the other half provided additional visual input of moving lips. Task-evoked mediofrontal N1 and P2 brain potentials triggered to the onset of the auditory syllables were derived and related to behavioral performance.

Results

In comparison to HC, SZ showed speech recognition deficits for unisensory and bisensory stimuli. These deficits were primarily found in the no noise condition. Paralleling these observations, reduced N1 amplitudes to unisensory and bisensory stimuli in SZ were found in the no noise condition. In HC the N1 amplitudes were positively related to the speech recognition performance, whereas no such relationships were found in SZ. Moreover, no group differences in multisensory speech recognition benefits and N1 suppression effects for bisensory stimuli were observed.

Conclusion

Our study suggests that reduced N1 amplitudes reflect early auditory and audiovisual speech processing deficits in SZ. The findings that the amplitude effects were confined to salient speech stimuli and the attenuated relationship with behavioral performance in patients compared to HC, indicates a diminished decoding of the auditory speech signals in SZs. Our study also revealed relatively intact multisensory benefits in SZs, which implies that the observed auditory and audiovisual speech recognition deficits were primarily related to aberrant processing of the auditory syllables.

Working Memory Impairment as an Endophenotypic Marker in Patients with Schizophrenia: Failures in Encoding or Maintenance?

INTRODUCTION

Although working memory (WM) dysfunction has been proposed as a schizophrenia (SZ) endophenotype, the specific impaired component (encoding or maintenance) in patients and unaffected relatives remains inconclusive. We compared auditory-verbal and visuospatial WM in patients with SZ, unaffected siblings (USs), and healthy controls under 2 response conditions: immediate (encoding condition) and delayed (maintenance condition).

METHODS

We included 22 participants per group, similar in age and gender. Three WM tests (Spatial Span, Backward Digit Span, and Letter-Number Span) were administered under both conditions in a counterbalanced manner to all participants.

RESULTS

Poorer performance was found in the SZ group for all tests (p < 0.001). USs showed a better performance than patients, but worse than controls (p < 0.05), except for the Backward Digit Span test, in which their performance was similar to that of the SZ group. The effect of the delayed response in all tasks was not significant in any group.

CONCLUSION

Our results indicate that WM impairment, including auditory-verbal and visuospatial modalities, corresponds to a stable feature of the disease as it is present in USs, thus confirming its potential endophenotypic property in SZ patients. No effect of the delayed response was observed, suggesting failures in encoding in both patients and USs.

A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders

Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.

The psychosis human connectome project: An overview

Investigations within the Human Connectome Project have expanded to include studies focusing on brain disorders. This paper describes one of the investigations focused on psychotic psychopathology: The psychosis Human Connectome Project (P-HCP). The data collected as part of this project were multimodal and derived from clinical assessments of psychopathology, cognitive assessments, instrument-based motor assessments, blood specimens, and magnetic resonance imaging (MRI) data. The dataset will be made publicly available through the NIMH Data Archive. In this report we provide specific information on how the sample of participants was obtained and characterized and describe the experimental tasks and procedures used to probe neural functions involved in psychotic disorders that may also mark genetic liability for psychotic psychopathology. Our goal in this paper is to outline the data acquisition process so that researchers intending to use these publicly available data can plan their analyses. MRI data described in this paper are limited to data acquired at 3 Tesla. A companion paper describes the study's 7 Tesla image acquisition protocol in detail, which is focused on visual perceptual functions in psychotic psychopathology.