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The study investigated the underlying mechanisms associated with the ability of patients with major depressive disorder (MDD) to utilize predictive contextual information in order to facilitate detection of predictable versus random targets. To this end we evaluated EEG event-related functional connectivity during the processing of predictive stimuli in MDD and control subjects. A target detection task was used where targets were either preceded by randomized sequences of standards, or by sequences that included a predictive sequence. Functional connectivity was evaluated using synchronization likelihood and graph theory. The cluster coefficient and local efficiency values were greater in MDD compared to controls, during the processing of the three stimuli consisting of the predictive sequence, in the beta frequency band, suggesting an increased structured network organization. These changes were associated with increased functional connectivity within frontal networks in MDD patients compared to controls. However, no significant functional connectivity group-changes were observed for target conditions or randomized standards. These findings suggest that MDD is associated with context-specific functional connectivity abnormalities during the processing of predictive stimuli., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

This paper tests the hypothesis that patients with schizophrenia have a deficit in selectively attending to predictable events. We used dynamic causal modeling (DCM) of electrophysiological responses - to predictable and unpredictable visual targets - to quantify the effective connectivity within and between cortical sources in the visual hierarchy in 25 schizophrenia patients and 25 age-matched controls. We found evidence for marked differences between normal subjects and schizophrenia patients in the strength of extrinsic backward connections from higher hierarchical levels to lower levels within the visual system. In addition, we show that not only do schizophrenia subjects have abnormal connectivity but also that they fail to adjust or optimize this connectivity when events can be predicted. Thus, the differential intrinsic recurrent connectivity observed during processing of predictable versus unpredictable targets was markedly attenuated in schizophrenia patients compared with controls, suggesting a failure to modulate the sensitivity of neurons responsible for passing sensory information of prediction errors up the visual cortical hierarchy. The findings support the proposed role of abnormal connectivity in the neuropathology and pathophysiology of schizophrenia., (Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.)

Functional connectivity was evaluated in patients with schizophrenia (SC) and in patients with Parkinson's disease (PD) during the performance of a local contextual processing paradigm, to investigate the proposition that functional disconnection is involved with contextual processing deficits in these populations. To this end, we utilized event-related EEG signals, synchronization likelihood and graph theoretical analysis. Local context was defined as the occurrence of a predictive sequence of stimuli before the presentation of a target event. In the SC patients, we observed a decrease in path length (L) in the beta band, for the predictive sequence and for predicted and random targets, compared with controls. These abnormalities were associated with weaker frontal-temporal-parietal connections. In the PD patients we found longer L (theta band) for predicted targets, and higher cluster coefficients for both the predictive sequence (theta band) and predicted targets (alpha and theta bands), compared with controls. Detection of predicted targets was associated with weaker frontal-parietal connections in PD. No group differences were found for randomized standard stimuli in both SC and PD patients. These findings provide evidence of task-specific functional connectivity abnormalities within frontal networks during local contextual processing., (Copyright © 2013 Elsevier Inc. All rights reserved.)

How can physical systems of the brain, explain a psychological phenomenon such as personality? Personality is an emergent property of the brain as such it requires interacting elements that generate a whole. Per definition a physical system is a compound whole made of interacting interdependent elements. The brain is composed of multiple levels of elements ranging from single neurons interconnected by axons dendrites and synapses, up to brain regions and neural network ensembles connected by multiple modalities, from direct physical pathways to synchronized functional connectivity. Today we know that the brain develops and wires according to the influences of its environment, this is called "experience dependent plasticity" and follows Hebbian-like algorithms. Such process "embeds" into the brain internal representations in the form of physical attractor configurations distributed within the brain neural-networks. Development entails formation of personal individual-specific network configurations found recently as resting-state networks or "default-mode networks." These internal configurations represent the outer world to us and determine the way we perceive it and react to it. In other words these internal configurations determine our personality styles. The internal representations continuously adapt to the changing worlds offering good adaptability and effective functionality in our changing environments. Personality disorders are reconceptualized in terms of altered disturbed mal-developed default-mode-networks, such that the internal representations are biased, limited, fixated and non-adaptive. In this context therapy of personality disorders can be reconceptualized as experience-depended plasticity therapy., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

As early as the end of the 19th century Ernest Bruck declared that the brain is a physical entity and should be studied using the science of mathematics and physics. The brain is an extremely intricate physical entity and we have only recently begun to develop the conceptual tools to decipher this complexity. We can begin to comprehend many of the mental functions and dysfunctions by using insights about brain organization as a developing physical entity of connectivity structures. A comprehensive theoretical framework for the re-conceptualization of mental disorders as real brain-disorders, called "Clinical brain profiling" can be generated to make testable predictions about the etiopathology of psychiatric disorders. If validated, this framework has groundbreaking relevance for psychiatry, not only by providing an etiological diagnostic system, in itself revolutionary, but in its potential to develop effective curative interventions. According to the proposed brain profiling all mental disturbances can be defined in a 3 dimensional space of brain disturbances (1) neural-complexity organization, (2) to neural resilience optimization dynamics and (3) to connectivity constructs for context and internal representations. Neural complexity relates to the ability of the brain to balance connectivity dynamics, neural resilience relates to brain plasticity and changeability for optimizing overall brain dynamics and contextual configurations shape the internal representations of outer world that pattern out reaction and personality styles. Each of these organizational brain functions is predicted to involve a relatively specific neuronal circuitry system in the brain. The circuitry of the nigra-striautum-cortex, are a component of the connectivity balance stabilizers and regulators, a type of neural complexity pacemaker. Thus a patient that rates high on phenomenology related to functional psychosis indicating a disturbance to connectivity balance will have disturbances that will show up in appropriate signal processing imaging of the nigra-striautum-cortex circuitry. The circuitry of thalamus-amygdala-cortex and related pathways are relevant for neuronal matching and constraint frustration. In this respect the patients scoring high on mood and anxiety disorders are predicted to suffer from perturbation shown on appropriate imaging involving the thalamus-amygdala-cortex circuitries. The hippocampus is related to the formation of internal configurations thus those patients rating highest on parameters related to personality organization and maturation will show alterations in the hippocampal organization and activation indicating deficient organizations of internal configurations., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Objective: The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been hampered by the large artifact that the TMS generates in the EEG. Using TMS with EEG necessitates a sophisticated artifact-resistant EEG system that can acquire reliable signals in the crucial several tens of milliseconds immediately following the TMS pulse. Here, we demonstrate the use of a novel artifact removal algorithm together with a 24-bit EEG system to achieve similar recordings as those obtained with the dedicated TMS-compatible EEG system., Methods: This setup was used to compare TMS-evoked responses between a group of healthy controls and a group of patients with schizophrenia, a condition in which effective neural connectivity is thought to be compromised., Results: We observe differences in TMS-evoked responses between the two groups, similar to those recently reported in a study that used a dedicated TMS-compatible EEG system., Conclusions: The standard 24-bit EEG system combined with an artifact removal algorithm produces results similar to the dedicated TMS-compatible system., Significance: This paves the way for more researchers and clinicians to use TMS-evoked responses for research and diagnosis of a wide spectrum of disorders.

The current psychiatric diagnostic system, the diagnostic statistic manual, has recently come under increasing criticism. The major reason for the shortcomings of the current psychiatric diagnosis is the lack of a scientific brain-related etiological knowledge about mental disorders. The advancement toward such knowledge is further hampered by the lack of a theoretical framework or "language" that translates clinical findings of mental disorders to brain disturbances and insufficiencies. Here such a theoretical construct is proposed based on insights from neuroscience and neural-computation models. Correlates between clinical manifestations and presumed neuronal network disturbances are proposed in the form of a practical diagnostic system titled "Brain Profiling". Three dimensions make-up brain profiling, "neural complexity disorders", "neuronal resilience insufficiency", and "context-sensitive processing decline". The first dimension relates to disturbances occurring to fast neuronal activations in the millisecond range, it incorporates connectivity and hierarchical imbalances appertaining typically to psychotic and schizophrenic clinical manifestations. The second dimension relates to disturbances that alter slower changes namely long-term synaptic modulations, and incorporates disturbances to optimization and constraint satisfactions within relevant neuronal circuitry. Finally, the level of internal representations related to personality disorders is presented by a "context-sensitive process decline" as the third dimension. For practical use of brain profiling diagnosis a consensual list of psychiatric clinical manifestations provides a "diagnostic input vector", clinical findings are coded 1 for "detection" and 0 for "non-detection", 0.5 is coded for "questionable". The entries are clustered according to their presumed neuronal dynamic relationships and coefficients determine their relevance to the specific related brain disturbance. Relevant equations calculate and normalize the different values attributed to relevant brain disturbances culminating in a three-digit estimation representing the three diagnostic dimensions. brain profiling has the promise for a future brain-related diagnosis. It offers testable predictions about the etiology of mental disorders because being brain-related it lends readily to brain imaging investigations. Being presented also as a one-point representation in a three-dimensional space, multiple follow-up diagnoses trace a trajectory representing an easy-to-see clinical history of the patient. Additional, more immediate, advantages involve reduced stigma because it relaters the disorder to the brain not the person, in addition the three-digit diagnostic code is clinically informative unlike the DSM codes that have no clinical relevance. To conclude, brain profiling diagnosis of mental disorders could be a bold new step toward a "clinical-neuroscience" substituting "psychiatry".

In 1895 Freud tried to explain mental disorders using the neurophysiological knowledge of his time. He soon abandoned this attempt realizing it was immature considering the neuroscientific knowledge available to him. For the rest of his career he limited himself to psychological formulations. Along the same lines, lacking etiology for mental disorders, the diagnostic system of psychiatry is exclusively descriptive. The need for a brain related diagnosis of mental disorders is important for developing better treatments and more reliable diagnosis. Today with the development of neuroscience it is time to go back to Freud's initial attempts and explain mental disorders as altered neuronal organizations in the brain. The neural network level is chosen as the relevant description level for mental functions. Plasticity is chosen as a general concept for neuronal dynamics explaining neuropathology of psychiatric disorders. Plasticity is divided according to timescales into "fast plasticity," "slow plasticity" and "stable plasticity". It is proposed that normal mental functions require optimal balance among all the plasticity timescales. Mental disorders arise when such balance is disturbed, thus mental disorders could be reformulated as deficiencies of the different plasticity processes. Changes in coherence synchrony and phase-locking membrane potentials in cortically spread neuronal ensembles are all expressions of fast plasticity. Synaptogenic and neurogenic processes, such as brain derived neurotrophic factor-dependent processes, are defined as slow plasticity. Finally those synaptic and neuronal pathways that consolidated into long lasting circuits are referred to as stable plasticity. With the aid of a neural network model simulating the plasticity imbalance, a mathematical formulation could be realized for mental disorders. Once achieved this mathematical formulation could form a guiding framework for interpreting brain-imaging data collected from psychiatric patients. Such a model is realized using interconnected "modules" each simulating the relevant plasticity dynamics relevant for the model. Diagnosing plasticity imbalance has some advantages over current descriptive psychiatric diagnosis. It is brain-related thus less stigmatising in the sense that mental disorders are brain disorders and not "person" disorders. The diagnostic system is much more flexible allowing for a high degree of variations and combinations in the description of the disorders thus naturally accounting for comorbidities. Most importantly, this diagnostic model is brain-related offering research targets for intervention and a theoretical framework guiding such interventions.

The rubber-hand illusion (RHI), an illusion in which tactile sensations are referred to a synthetic alien limb, is enhanced in schizophrenia patients. Somatosensory evoked responses of the illusion were compared between schizophrenia patients and normal control subjects. Schizophrenia patients had significant alterations in long latency evoked responses during the illusion. These findings support the hypothesis of alterations in associative higher-level neuronal activity in schizophrenia. The findings support previous results pointing to alterations in associative brain regions in schizophrenia. [Copyright &y& Elsevier]