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Highlights: [•] Reaction times and P3b latencies of predicted targets were prolonged in depression. [•] P3b amplitudes were attenuated in major depressive disorder during processing of predictive contextual information, as well as target N1 amplitudes. [•] Processing of local contextual processing is altered in major depressive disorder. [ABSTRACT FROM AUTHOR]

Abstract: 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 &y& Elsevier]

Abstract: Background: As an Ancient Chinese proverb says “The beginning of wisdom is to call things by their right names” thus we must start calling mental disorders by the names of their underlying brain disturbances. Without knowledge of the causes of mental disorders, their cures will remain elusive. Methods: Neuroanalysis is a literature-based re-conceptualization of mental disorders as disturbances of brain organization. Psychosis and schizophrenia can be re-conceptualized as disturbances to connectivity and hierarchical dynamics in the brain; mood disorders can be re-conceptualized as disturbances to optimization dynamics and free energy in the brain, and finally personality disorders can be re-conceptualized as disordered default-mode networks in the brain. Results and conclusions: Knowledge and awareness of the disease algorithms of mental disorders will become critical because powerful technologies for controlling brain activity are developing and becoming available. The time will soon come when psychiatrists will be asked to define the exact ‘algorithms’ of disturbances in their psychiatric patients. Neuroanalysis can be a starting point for the response to that challenge. [Copyright &y& Elsevier]

Abstract: Schizophrenia is a serious mental disorder characterized by a heterogonous spectrum of clinical manifestations. Schizophrenia is basically incurable. The discovery of antipsychotic medications in the late 1940s has helped control some of the symptoms but has not reversed the course of the disorder and has had limited effect on the debilitating symptoms of the illness. In recent years brain stimulation technologies have emerged in the bio-scientific scenery. Deep brain stimulation now plays an important role in the treatment of many neurological disorders, and seems promising in treating depression. Optogenetics is a new technology that offers control over neuronal activity by turning on and off distinct neuronal populations. It has a great advantage over previous brain stimulation technologies in that it is accurate and specific to the neurons intended for activation and control. There is no evidence that brain stimulation has been investigated in schizophrenia patients. This possibility was discussed in a single commentary that proposed the hippocampus and nucleus accumbence as targets for DBS in schizophrenia, however it was emphasized that the neurophysiology and neuroanatomy of schizophrenia have not been elucidated to the extent that brain stimulation can be planned. In light of new optogenetic technology time is ripe to seriously consider optional targets of intervention in the brain of schizophrenia patients. Any such target should involve neuronal circuits (1) known to be relevant for schizophrenia, (2) involved in cognitive and brain functions that are disturbed in schizophrenia, and (3) relevant to alleged neuronal network mechanisms that are presumably damaged or malfunctioning in schizophrenia. This paper reviews the relevant literature and proposes that optogenetic interventions in schizophrenia should begin in the prefrontal cortex and the Globus-Pallidus Subthalamic nuclei systems. In the protocol for the prefrontal cortex, wide-arbore and chandelier inhibitory interneurons should be targets for optogenetic intervention and in the Globus-Pallidus Subthalamic nuclei the fast spiking neurons should be targets for optogenetic intervention. These subsystems are critical modulators of neural complexity which is directly relevant to connectivity organization in the brain. Schizophrenia is described as a disturbance of connectivity organization in the brain treatable by the relevant optogenetic interventions promoted in this paper. [Copyright &y& Elsevier]

Summary: 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 &y& Elsevier]

Summary: The DSM is not brain-related and is thus unable to relate clinical assessments to putative brain disturbances. ‘Clinical brain profiling’ (CBP) involves the rearrangement of clinical findings to assess the relevant disturbances in brain dynamics. CBP has three major pathological dimensions, (1) disorders of basic brain organization and development (2) disorders of connectivity dynamics and balance and (3) disorders of plasticity dynamics and neural resilience. CBP is a useful platform for the development of a brain-related neuroscientific diagnosis for psychiatry. Once the underlying pathology of a mental disorder is known an effective intervention can be designed to cure the disorder. [Copyright &y& Elsevier]

Summary: 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”. [Copyright &y& Elsevier]

Summary: 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. [Copyright &y& Elsevier]

Background: Collins and Quillian (Acta Psychol 1970;33:304-314) proposed that semantic representations in the human brain could have a "networklike" theoretical construct. Thought disorders in schizophrenia have been described as disturbances in the spread of activation within semantic networks. Semantic networks are typically evaluated indirectly via reaction times of priming tasks. Medications may interfere with the reaction time of patients, thus, we sought to investigate semantic networks, independent of time, by having patients and controls rate textual associations in sentences organized to various degrees. Methods: Twenty-eight schizophrenic patients (17 non-thought-disordered and 11 thought-disordered) and 27 healthy controls performed a rating of textual associations task in which they were asked to rate the associative relationship between concepts in sentences on a scale from 1 (totally dissociated) to 10 (completely associated). The task contained 3 sets of sentences; organized meaningful sentences, vague sentences (intermediately disorganized), and completely disorganized sentences. To avoid order effects, sentences were randomly mixed at presentation. Results: Analysis of variance calculations indicated significant differences among the 3 groups (controls, thought- disordered, and nonthought-disordered). The differences were greater for the vague sentences. Compared with controls, schizophrenic patients demonstrated increased SDs in rating associative values between concepts in the sentences, which is higher in disorganized sentences. Inadequate ability to identify and rate associations in disorganized sentences is discussed in the context of disordered semantic networks of schizophrenic patients. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

• Increased functional connectivity during processing of predictive stimuli in MDD. • Functional connectivity changes in beta band and within frontal networks in MDD. • Findings may indicate compensatory mechanisms for neural insufficiency in MDD. 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. [ABSTRACT FROM AUTHOR]