Elevated P3b latency variability in carriers of ZNF804A risk allele for psychosis.

Increased intra-subject variability (ISV) in reaction times (RTs) is a candidate endophenotype for several psychiatric and neurological conditions, including schizophrenia. ISV reflects the degree of variability in RTs and is thought to be an index of the stability of cognition. It is generally assumed to have the same underlying physiological basis across conditions, but recent evidence raises the possibility that the neural underpinnings of ISV vary with aetiology. Combining genetics with single-trial event-related potentials is an ideal method for investigating the neural basis of ISV in groups where ISV may vary for relatively homogenous reasons. Here we examine the association between P3b latency variability and a polymorphism on the ZNF804A gene associated with psychosis. Ninety-one healthy volunteers genotyped for rs1344706, a polymorphism on ZNF804A, had electroencephalographic data recorded while carrying out three n-back tasks. Data were analysed with a single-trial approach and latency variability of the P3b was compared between the AA homozygous risk group (N=30) and C allele carriers (N=61). P3b latencies were more variable for AA carriers than C carriers. Behavioural ISV, however, was not associated with genotype. The increase in neurophysiological variability, unaccompanied by increased behavioural variability, suggests that this risk gene is associated with an attenuated form of an endophenotype associated with the psychosis phenotype. The increase in both stimulus and response-locked variability also contrasts with previous work into attention-deficit hyperactivity disorder, where only response-locked P3b variability was elevated, suggesting that increased ISV may not signify the same underlying processes in all conditions with which it is associated.

Effectiveness of remote neuropsychological interventions: A systematic review.

OBJECTIVE: Remote healthcare services is an upgrowing dynamic field that has been used to reduce potential disease spread and prevent overloading of the healthcare system during COVID-19 pandemic. The need for online interventions during the pandemic required immediate response with sometimes inadequate preparation. The aim of the present study is to investigate the effectiveness of remote healthcare services in the field of neuropsychological interventions. METHODS: A systematic literature search was conducted in the electronic databases of PubMed, PsychINFO and Google Scholar. The main search terms were "remote neuropsychological intervention or training." The included articles were RCT studies published in English, examining the effectiveness of remote healthcare services in neuropsychological interventions for adults with neurological disease diagnoses. Studies involving psychiatric disorders were excluded. Two reviewers assessed the quality of the studies and risk of bias using the PEDro Scale. RESULTS: A total of 10 studies with 2.221 participants were included. All studies concluded that remote healthcare intervention programs can be feasible, safe and effective in the rehabilitation process of neurological diseases. DISCUSSION: The present review demonstrated that the domains of neuropsychology have opportunities to forge ahead beyond traditional settings and have the ability to adapt to constantly changing environmental conditions with a view to providing patient care. Health policy plans should therefore be reformulated to include these needs in accordance with the social and cultural context of implementation.

Multisensory integration deficits in Schizophrenia and Autism evidenced in behaviour but not event related potentials.

The process of integrating information from different sensory channels, known as multisensory integration (MSI) was assessed in two disorders, Autism Spectrum Disorder (ASD) and Schizophrenia (SCZ). 32 healthy controls (HC), 35 SCZ patients, and 23 ASD patients performed an audiovisual (AV) synchronous target detection task while reaction time (RT) and scalp recorded electrophysiological (EEG) activity were measured. MSI in the AV condition resulted in faster and less variable RTs compared to the unimodal conditions. Using our novel bootstrap method, MSI gain was observed in 78 % of HC, 26 % of ASD, and 48 % of SCZ patients. At the neural level, MSI in the AV condition resulted in larger amplitude of sensory evoked responses and cognitive P3 response compared to the corresponding unimodal conditions. These neural effects of MSI were not related to the behavioural MSI gain identified at the individual level and could not explain the deficits in behavioural MSI of patient groups. In conclusion, a robust MSI gain deficit in RT was observed in both patient groups that was not reflected in early perceptual and cognitive electro-cortical responses, suggesting that behavioural MSI deficits in ASD and SCZ may arise at late processing stages such as response selection.

Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia.

OBJECTIVE: Electroencephalographic analysis (EEG) has emerged as a powerful tool for brain state interpretation. Studies have shown distinct deviances of patients with schizophrenia in EEG activation at specific frequency bands. METHODS: Evidence is presented for the validation of a Convolutional Neural Network (CNN) model using transfer learning for scalp EEGs of patients and controls during the performance of a speeded sensorimotor task and a working memory task. First, we trained a CNN on EEG data of 41 schizophrenia patients (SCZ) and 31 healthy controls (HC). Secondly, we used a pretrained model for training. Both models were tested in an external validation set of 15 SCZ, 16 HC, and 12 first-degree relatives. RESULTS: Using the layer-wise relevance propagation on the classification decision, a heatmap was produced for each subject, specifying the pixel-wise relevance. The CNN model resulted in the first case in a balanced accuracy of 63.7% and 81.5% in the second case, on the external validation test 64.5% and 83.2%, respectively. CONCLUSIONS: The theta and alpha frequency bands of the EEG signals had significant relevance to the CNN classification decision and predict the first-degree relatives indicating potential heritable functional deviances. SIGNIFICANCE: The proposed methodology results in important advancements for the identification of biomarkers in schizophrenia heritability.

Modelling reaction time distribution of fast decision tasks in schizophrenia: Evidence for novel candidate endophenotypes.

Increased reaction time (RT) and variability of RT in fast decision tasks is observed in patients with schizophrenia and their first degree relatives. This study used modelling of the RT distribution with the aim of identifying novel candidate endophenotypes for schizophrenia. 20 patients with schizophrenia, 15 siblings of patients and 25 healthy controls performed an oddball task of varying working memory load. Increases in mean and standard deviation (SD) of RT were observed for both patients and siblings compared to controls and they were again independent of working memory load. Ex-Gaussian modelling of the RT distribution confirmed that parameters µ, σ and τ increased significantly in patients and siblings compared to controls. The Drift Diffusion Model was applied on RT distributions. A decrease in the diffusion drift rate (v) modeling the accumulation of evidence for reaching the decision to choose one stimulus over the other, was observed in patients and siblings compared to controls. The mean time of the non-decisional sensorimotor processes (t0) and it's variance (st0) was also increased in patients and siblings compared to controls. In conclusion modeling of the RT distribution revealed novel potential cognitive endophenotypes in the quest of heritable risk factors for schizophrenia.