Abraham Peled

Impairment in predictive processes during auditory mismatch negativity in ScZ: Evidence from event-related fields

Patients with schizophrenia (ScZ) show pronounced dysfunctions in auditory perception but the underlying mechanisms as well as the localization of the deficit remain unclear. To examine these questions, the current study examined whether alterations in the neuromagnetic mismatch negativity (MMNm) in ScZ‐patients could involve an impairment in sensory predictions in local sensory and higher auditory areas. Using a whole‐head MEG‐approach, we investigated the MMNm as well as P300m and N100m amplitudes during a hierarchical auditory novelty paradigm in 16 medicated ScZ‐patients and 16 controls. In addition, responses to omitted sounds were investigated, allowing for a critical test of the predictive coding hypothesis. Source‐localization was performed to identify the generators of the MMNm, omission responses as well as the P300m. Clinical symptoms were examined with the positive and negative syndrome scale. Event‐related fields (ERFs) to standard sounds were intact in ScZ‐patients. However, the ScZ‐group showed a reduction in the amplitude of the MMNm during both local (within trials) and global (across trials) conditions as well as an absent P300m at the global level. Importantly, responses to sound omissions were reduced in ScZ‐patients which overlapped both in latency and generators with the MMNm sources. Thus, our data suggest that auditory dysfunctions in ScZ involve impaired predictive processes that involve deficits in both automatic and conscious detection of auditory regularities. Hum Brain Mapp 38:5082–5093, 2017.

“Clinical brain profiling”: A neuroscientific diagnostic approach for mental disorders

Clinical brain profiling is an attempt to map a descriptive nosology in psychiatry to underlying constructs in neurobiology and brain dynamics. This paper briefly reviews the motivation behind clinical brain profiling (CBP) and presents some provisional validation using clinical assessments and meta-analyses of neuroscientific publications. The paper has four sections. In the first, we review the nature and motivation for clinical brain profiling. This involves a description of the key aspects of functional anatomy that can lead to psychopathology. These features constitute the dimensions or categories for a profile of brain disorders based upon pathophysiology. The second section describes a mapping or translation matrix that maps from symptoms and signs, of a descriptive sort, to the CBP dimensions that provide a more mechanistic explanation. We will describe how this mapping engenders archetypal diagnoses, referring readers to tables and figures. The third section addresses the construct validity of clinical brain profiling by establishing correlations between profiles based on clinical ratings of symptoms and signs under classical diagnostic categories with the corresponding profiles generated automatically using archetypal diagnoses. We then provide further validation by performing a cluster analysis on the symptoms and signs and showing how they correspond to the equivalent brain profiles based upon clinical and automatic diagnosis. In the fourth section, we address the construct validity of clinical brain profiling by looking for associations between pathophysiological mechanisms (such as connectivity and plasticity) and nosological diagnoses (such as schizophrenia and depression). Based upon the mechanistic perspective offered in the first section, we test some particular hypotheses about double dissociations using a meta-analysis of PubMed searches. The final section concludes with perspectives for the future and outstanding validation issues for clinical brain profiling.

Automated Facial Expressions Analysis in Schizophrenia: A Continuous Dynamic Approach

Facial expressions play a major role in psychiatric diagnosis, monitoring and treatment adjustment. We recorded 34 schizophrenia patients and matched controls during a clinical interview, and extracted the activity level of 23 facial Action Units (AUs), using 3D structured light cameras and dedicated software. By defining dynamic and intensity AUs activation characteristic features, we found evidence for blunted affect and reduced positive emotional expressions in patients. Further, we designed learning algorithms which achieved up to 85 % correct schizophrenia classification rate, and significant correlation with negative symptoms severity. Our results emphasize the clinical importance of facial dynamics, and illustrate the possible advantages of employing affective computing tools in clinical settings.

MEG resting-state oscillations and their relationship to clinical symptoms in schizophrenia

Neuroimaging studies suggest that schizophrenia is characterized by disturbances in oscillatory activity, although at present it remains unclear whether these neural abnormalities are driven by dimensions of symptomatology. Examining different subgroups of patients based on their symptomatology is thus very informative in understanding the role of neural oscillation patterns in schizophrenia. In the present study we examined whether neural oscillations in the delta, theta, alpha, beta and gamma bands correlate with positive and negative symptoms in individuals with schizophrenia (SZ) during rest. Resting-state brain activity of 39 SZ and 25 neurotypical controls was recorded using magnetoencephalography. Patients were categorized based on the severity of their positive and negative symptoms. Spectral analyses of beamformer data revealed that patients high in positive symptoms showed widespread low alpha power, and alpha power was negatively correlated with positive symptoms. In contrast, patients high in negative symptoms showed greater beta power in left hemisphere regions than those low in negative symptoms, and beta power was positively correlated with negative symptoms. We further discuss these findings and suggest that different neural mechanisms may underlie positive and negative symptoms in schizophrenia.

Altered Brain Network Dynamics in Schizophrenia: A Cognitive Electroencephalography Study

BACKGROUND Alterations in the dynamic coordination of widespread brain networks are proposed to underlie cognitive symptoms of schizophrenia. However, there is limited understanding of the temporal evolution of these networks and how they relate to cognitive impairment. The current study was designed to explore dynamic patterns of network connectivity underlying cognitive features of schizophrenia. METHODS In total, 21 inpatients with schizophrenia and 28 healthy control participants completed a cognitive task while electroencephalography data were simultaneously acquired. For each participant, Pearson cross-correlation was applied to electroencephalography data to construct correlation matrices that represent the static network (averaged over 1200 ms) and dynamic network (1200 ms divided into four windows of 300 ms) in response to cognitive stimuli. Global and regional network measures were extracted for comparison between groups. RESULTS Dynamic network analysis identified increased global efficiency; decreased clustering (globally and locally); reduced strength (weighted connectivity) around the frontal, parietal, and sensory-motor areas; and increased strength around the occipital lobes (a peripheral hub) in patients with schizophrenia. Regional network measures also correlated with clinical features of schizophrenia. Network differences were prominent 900 ms following the cognitive stimuli before returning to levels comparable to those of healthy control participants. CONCLUSIONS Patients with schizophrenia exhibited altered dynamic patterns of network connectivity across both global and regional measures. These network differences were time sensitive and may reflect abnormalities in the flexibility of the network that underlies aspects of cognitive function. Further research into network dynamics is critical to better understanding cognitive features of schizophrenia and identification of network biomarkers to improve diagnosis and treatment models.

Real-Time Schizophrenia Monitoring Using Wearable Motion Sensitive Devices.

Motor peculiarity is an integral part of the schizophrenia disorder, having various manifestations both throughout the phases of the disease, and as a response to treatment. The current subjective non-quantitative evaluation of these traits leads to multiple interpretations of phenomenology, which impairs the reliability and validity of psychiatric diagnosis. Our long-term objective is to quantitatively measure motor behavior in schizophrenia patients, and develop automatic tools and methods for patient monitoring and treatment adjustment. In the present study, wearable devices were distributed among 25 inpatients in the closed wards of a Mental Health Center. Motor activity was measured using embedded accelerometers, as well as light and temperature sensors. The devices were worn continuously by participants throughout the duration of the experiment, approximately one month. During this period participants were also clinically evaluated twice weekly, including patients’ mental, motor, and neurological symptom severity. Medication regimes and outstanding events were also recorded by hospital staff. Below we discuss the general framework for monitoring psychiatric patients with wearable devices. We then present results showing correlations between features of activity in various daily time-windows, and measures derived from the psychiatrist’s clinical assessment or abnormal events in the patients’ routine.

Differentiating facial incongruity and flatness in schizophrenia, using structured light camera data

Incongruity between emotional experience and its outwardly expression is one of the prominent symptoms in schizophrenia. Though widely reported and used in clinical evaluation, this symptom is inadequately defined in the literature and may be confused with mere affect flattening. In this study we used structured-light depth camera and dedicated software to automatically measure facial activity of schizophrenia patients and healthy individuals during an emotionally evocative task. We defined novel measures for the congruence of emotional experience and emotional expression and for Flat Affect, compared them between patients and controls, and examined their consistency with clinical evaluation. We found incongruity in schizophrenia to be manifested in a less specific range of facial expressions in response to similar emotional stimuli, while the emotional experience remains intact. Our study also suggests that when taking into consideration affect flatness, no contextually inappropriate facial expressions are evident.