CLINICAL BRAIN PROFILING Avi Peled1 and Michael Moursalimov2 1

2

Bruce and Ruth Rappaport Faculty of Medicine, Technion - Israel Institute of Technology Haifa, Israel

Faculty of Industrial Engineering and Management, Technion – Israel Institute of Technology. Haifa, Israel

ABSTRACT In recent years a growing volume of literature attributes mental disorders to disturbances of neural network organization in the brain. The idea of relating different mental disorders to diverse breakdown patterns of neuralnetwork organization in the brain has been developed under the term 'Clinical brain profiling.' A computer program was assembled to effectuate clinical brain profiling for clinical usage, the input to the program involves clinical findings from the clinical examination and the output is constructed from a set of hypothesized disturbances to brain dynamic organization. A translational matrix converts the input of clinical examination to the output of hypothesized brain disturbances. While inputs involve common signs and symptoms the output proposes brain disturbances such as connectivity disintegration or over integration, optimization dynamic breakdowns, neuronal constraints frustration and context processing decline. Clinical brain profiling has the potential to generate a patient-specific testableprediction for the imaging analysis of that patient, and as such is a first step for a potential new outlook on psychiatric diagnosis, one that is brain-related and thus open to empirical validation. .

OBJECTIVES OF THE STUDY In recent years a growing volume of literature attributes mental disorders to

disturbances of neural network organization in the brain (Andreasen 1997, Peled et al 2000, Brambilla et al 2007, Ragland et al 2007, Casanova 2007). Specifically for schizophrenia there is accumulating findings toward disturbances of neural network connectivity organization (Cohen et al 1993, Friston and Frith 1995, Tononi and Edelman 2000). As early as 18th century Theodor Menynert has assumed that psychosis may result from 'weakness' of pathways connecting neuronal activations representing ideas and thoughts. The idea of relating different mental disorders to diverse breakdown patterns of neural-network organization is worth following, it may even propose a brainrelated diagnostic terminology for describing mental disorders (Peled 1999; Peled and Gevaq 1999; 2004; 2004; 2005; 2006; Peled and brand 2006; 2007). Clinical Brain profiling (CBP) is such an attempt, attributing sets of psychiatric signs and symptoms to hypotheticallydefined disturbances to brain dynamics (e.g. connectivity disintegration and segregation, over connected integration, optimization breakdown and so on, Peled 2004; 2006; 2007). CBP involves a translational matrix (table 1) which translates the clinical findings to the hypothesized disturbances of brain dynamic organization. The CBP matrix attributes to each clinical finding (i.e.,

singe or symptom) a possible correlated set of dynamic brain disturbances.

understanding the reader is referred to the mentioned literature.

A computer program (CBP_system) was assembled to effectuate the CBP for clinical usage. According to the CBP formulation the input to the CBP_system involves clinical findings from the clinical examination and the output is constructed from a set of hypothesized disturbances to brain dynamic organization.

Using insights from neural computation, and computational neuroscience (Hertz et al 1991) the normal brain is described as a complex assembly of neural processors interconnected in complicated connectivity architectures.

CBP could offer a set of testable predictions for imaging research of mental disorders, for example patients that score highly on certain psychiatric phenomena could be suffering from a disconnection disturbance or ‘segregated mode’ of disturbance in the brain. Modern imaging analysis techniques (e.g., Kiebel et al 2006; 2007) can detect such segregated dynamics offering a staggering potential to diagnose this mental disorder on the basis of brain-related objective imaging examination. Before CBP can be proposed as a testableprediction-theory it must be found to be ‘as-good-as’ currently existing assessment tools, i.e., approved psychiatric clinical scales. In this study, we compare CBP_system assessment results with relevant clinical scales i.e., SAPS Scale for Assessment of Positive Symptoms (Andreasen 1984), SANS Scale for Assessment of Negative Symptoms (Andreasen 1983), HAMD Hamilton Depression assessment scale (Hamilton 1967) and the HAMA Hamilton Anxiety assessment scale (Hamilton 1959). Our results show that CBP can equivalent proved consensual psychiatric scales for assessing mental disorders.

CLINICAL THEORY

BRAIN

PROFILING

The theory behind CBP has been detailed extensively in three books (Peled 2004; Peled and brand 2006; Peled 2007) and few papers (Peled 1999; Peled and Gevaq Peled 2004; 2005; 2006) here only a brief outline of CBP is given, for more in-depth

In general the well functioning brain achieves an optimization (favorable condition) of balanced activity. The brain balances specialized segregate neuronal activity and coherent integrated global processing via connectivity equilibrium (Tononi et al 1994). The brain is also hierarchically organized having higher mental functions emerging at higher hierarchical levels of brain organization (Mesulam 1998). It balances hierarchal top-down and bottom-up equilibrium (Peled 2004; 2005; 2006). The brain is in constant dynamic change moving from less optimized organizations to better organized neuronal activations. Such dynamics exerts perturbations (and frustrations) to connections within and between cell ensembles, as a whole brain organization such dynamics can optimize or deoptimize brain processing (Peled 2004; 2005; 2006). Finally the brain is organized around experience, experiencedependent-plasticity is the term describing connectivity ensembles forming by Hebbian-like (strengthening connections; Hebb 1949) organizations allowing to store information and create an internal representation of the outer experience and the world of the individual. Such experience-dependent internal representations of the world determine how one reacts to his psychosocial world of occurrences, such reactions and internally determined experience shapes ones behavior and reflects his personality stile. Thus personality can be described by assessing the level of organization of internal representations. In the CBP theory mental disorders are attributed to disturbances of balanced activity of the optimally functioning brain. Thus connectivity, hierarchy, optimization dynamics, frustration on constraints, and

internal representations can be disturbed to various degrees. As a fully integrated system disturbance to each of the mentioned organizations naturally affects all the others, nevertheless certain brain organizations can be perturbed more then others giving raise to the heterogeneous overlapping clinical manifestations of mental disorders. CBP specifies a set of disturbances to brain organizations as follows: ‘Cs’ (connectivity segregation) when neuronal ensembles disconnect from each other giving raise to a disconnection dynamics where neural processing becomes statistically independent, allowing brain dynamics to 'loosen' 'jumping' from one state to the other randomly. ‘Ci’ (connectivity integration) where neural ensembles are overly connected overly constraining mutual activities driving the brain organization to fixed states (Geva and Peled 2000). These parameters express the two possible breakdowns to an optimal connectivity balance in the brain titled also ‘neural complexity’ (Tononi 1994). Similarly ‘Hbu’ and ‘Htd’ reflect hierarchical bottom-up insufficiency and hierarchical top-down shift respectively. The former is correlated to a deficit of higher-level brain organizations and their related mental deficiencies; the latter is correlated with excessive top-down control with its rigid non adaptive consequences. Considering optimization dynamic balances ‘O’ (optimization shift) and ‘D’ (deoptimization shifts) are the disturbances to the most favorable optimization balance of brain dynamics. Frustration to constraints (i.e., strains on connectivity values see Peled 1999; Peled and Gevaq 1999; 2004; 2004; 2005; 2006; Peled and brand 2006; 2007) can be bound to a stimulus ‘CFb’ or generally distributed ‘CFg.’ Finally the term 'context-sensitive processing' is reserved for internal representations that determine personality style, when this organization is perturbed then context-sensitive processing decline occurs and is scored as

‘CSPD’(i.e., context-sensitive processing decline). From the traditional psychiatric diagnostic point of view 'Cs,' 'Ci,' 'Hbu' and 'Htd' relate to the clinical spectrum found in serious mental disorders such as schizophrenia spectrum disorders. when 'Cs' prevails loosening of associations hallucinations and delusions prevail (Peled 1999; Peled and Geva 1999; 2004; 2004; 2005; 2006; Peled and brand 2006; 2007), when 'Ci' prevails in the brain then poverty of thought and perseveration clinically manifest (Peled 1999; Peled and Geva 1999; 2004; 2004; 2005; 2006; Peled and brand 2006; 2007). The clinical correlates of 'Hbu' when higher-levels of brain organization collapse is the occurrence of negative-symptoms schizophrenia, similar also to DLPF (dorsolateral prefrontal) syndrome. Disturbances of ‘optimization’ and ‘frustration’ of constraint dynamics emerge as mood and anxiety disorders respectively (the explanation to these insights is lengthy and can be found at Peled 1999; Peled and Geva 1999; 2004; 2004; 2005; 2006; Peled and brand 2006; 2007). In short, deoptimization dynamics ‘D’ emerges as depressed mood and hyper-optimization dynamics ‘O’ emerges as manic mood. Constraints frustration emerges as generalized anxiety sensation 'CFg' but when bound to a stimulus (as in phobia) it is limited to that stimulus i.e., 'CFb.' As already mentioned above personality assessment relates to levels of CSPD.

MATERIAL AND METHODS To implement the CBP theory turning it to a clinically-useful tool, a computer program was devised (CBP_system). It was designed to collect the clinical manifestation from the psychiatric assessment of the patient and translate them into values of disturbances to brain dynamics.

Figure 1 roughly clusters psychiatrist findings according to the assumed brain disturbances according to CBP.

Listed on the left hand side of the figure are the signs and symptoms assessed by the CBP computer program, these cluster to indicate which type of brain disturbance is outputted from the program on the right hand side of the figure.

A translational matrix (table 1) was devised to support the CBP computation. Table 1. CBP translation matrix

Figure 1. CBP Symptoms and signs clustering Detected Is the patient disorderly? Is the patient very messy Is the patient with excessive jewelry makeup and colored clothing? Moves slowly? Stiff frozen? Restless moves a lot? Agitated looks as on verge of blowing up? Bizarre unexplainable movement Repetitive stereotype movements? Speaks slowly? Speaks little, gives short responses? Speaks little, few words only or non at all Speech at low tone or whisper Speaks fast? Speaks a lot, gives long spontaneous responses? Speaks without stopping jumping from one issue to another? Speech with elevated tone? Speech associations are loose; jumps from one sentence to another each different topic? Words are unrelated within sentences ‘word salad’? Repeating same topics of conversation? Repeating perseverating the same sentences? Responding to previous question? Obsessions and compulsions? Delusion, false unshakable belief? Systemized delusion? Illogical conclusions are non logical? Mood incongruent delusion? Flight of ideas Speech content includes mainly issues of despair, hopelessness, and pessimism. Speech content includes mainly issues of megalomania, over empowerment and unrealistic optimism (and plans) Bizarre or overly abstract response to categorization (proverbs) and abstraction? Concrete interpretation of proverbs and low abstraction? Auditory hallucinations? Visual tactile olphactory hallucinations? Hypomimic affect Blunt affect? Expansive mood elevated affect? Dysphoric (suffering) affect? Depressed affect? Anxious affect? Detached from examiner? Perplex ambivalent? Inappropriately close to examiner (no boundaries)? Suspicious with examiner? Threatening to examiner? Seductive toward examiner (theatrical)? Sensitive easily offended? Childish dependent regressive? Manipulating demanding? Stubborn obsessive non adaptable? Tend to idealize or devaluate examiner? Egocentric un-empathic? Distractible? Disoriented? Memory lose?

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Complaining of Insomnia or hypersomnia? Complaining of Early insomnia? Complaining of Late insomnia? Complaining of Anorexia Wight loss Complaining of palpitations, dizziness, abdominal cramps and tingling. Complaining of anxiety fear of dying or loosing control panic Complaining of fear of dying or loosing control panic in specific conditions. Complaining of tension restlessness and agitation Complaining of avolition indifference apathy Anhedonia Complaining of depressed mood Complaining of depressed mood especially in the morning Complaining about Flight of ideas? Complaining that thing are strange unfamiliar changing not as usual (dereisim depersonalization) Complaining of external control, mind reading, bugging, persecution (about delusions) Complaining related to Systemized delusion Complaining of low self esteem Complaining about being easily offended, oversensitive? Complaining of being impulsive, over imposing? History of Delusions? History of Hallucinations? History of thought disorders loosening of associations History of thought disorders perseverations poverty of thought? History of depressions? History of mania? History of anxiety History of phobias History of disturbed upbringing, parental loose History of behavioral problems History of coping deficiency work and social? History of instable interpersonal relationships History of psychosocial or other stress (regular life stressors) History of trauma (stressor exceeding regular life stress)

Table 1. CBP translation matrix A translation matrix converts scores of 'yes' 'no' for clinical findings to parameters of brain disturbances i.e., Cs Connectivity segregation, Ci Connectivity integration, Hbu Hierarchical bottom-up, HTD Hierarchical top-down, O hyperoptimization, D Deoptimization, CFg general constraint frustration CFb bound constraint frustration, CSPD contextsensitive processing decline. For simplicity of usage each symptom, sign or amnestic parameter is scored and inputted on a 'yes' (existent) or 'none' (non-existent) entry value. The table relates to clinical findings from 1) mental status examination, 2) from patient’s complaints and 3) from patient’s history, as such CBP attempts to cover most of the clinical information that is available at a clinical psychiatric examination. During regular clinical routine of assessing inpatients and outpatients CBP evaluation was added to the regular diagnosis procedure (clinical interview and assessment of symptoms), in addition the patient’s clinical findings were scored according to 4 major consensual psychiatric scales, the SAPS (Scale for Assessment of Positive Symptoms Andreasen 1984), SANS (Scale for Assessment of Negative Symptoms Andreasen 1983), HAMD (Hamilton Depression assessment scale; Hamilton 1967) and the HAMA (Hamilton Anxiety

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assessment scale; Hamilton 1959 ). The results from 45 patients are presented.

RESULTS Each clinical case is presented (X axis) with its related score value (Y axis) both for CBP, as well as for the relevant psychiatric scale. The CBP values are percentages from collected ones (1 entries) score of the CBP matrix (table 1) while the scales values are sums of scored items related to the relevant scale. The result graphs give a visual estimation of the correlations.

Figure 2. SAPS, ‘Cs,’ and ‘Htd’ comparison

The results from the scale for the assessment of positive symptoms (SAPS) in blue are compared with the values of ‘Cs’ connectivity segregation and ‘Htd’ hierarchical top-down shift. Superposition

indicates correlations parameters.

between

these

Figure 2 shows values of SAPS scores that are highly correlated with ‘Cs’ and ‘Htd’ estimates. It is clearly visual that for the majority of the subjects with high values of SAPS scale also the ‘Cs’ and the ‘Htd’ values are high, thus forming superposition of the three graphs from the three assessments the SAPS, ‘Cs’ and ‘Htd.’ Superposition is thus the visual indication of highly correlated scores.

Figure 3. SANS, ‘Ci,’ and ‘Hbu’ comparison The results from the scale for the assessment of Negative symptoms (SANS) in blue are compared with the values of ‘Ci’ connectivity integration and ‘Hbu’ hierarchical bottom-up insufficiency. Superposition indicates correlations between these parameters.

Figure 3 shows values of SANS scores that are highly correlated with ‘Ci’ and ‘Hbu’ estimates. It is clearly visual that for the majority of the subjects with high values of SANS scale also the ‘Ci’ and the ‘Hbu’ values are high, thus forming superposition of the three graphs from the three assessments the SANS, ‘Ci’ and ‘Hbu’

Figure 4. HAMD, ‘D,’ and ‘O’ comparison

The results from the Hamilton depression rating scale (HAMD) in blue are compared with the values of ‘D’ deoptimization and ‘O’ hyper-optimization. Superposition between HAMD and ‘D’ scores indicates that depressed mood correlates with deoptimization dynamics. HAMD and ‘O’ scores do not superimpose, conversely they counter-impose because the hyperoptimization dynamic counteracts the deoptimization dynamics.

Figure 4 Compares deoptimization ‘D’ and hyper-optimization ‘O’ with the values of Hamilton depression scale (the HAMD) since deoptimization and hyperoptimization are opposing dynamics with deoptimization related to depression and hyper-optimization to mania (Peled 2004; 2005;2006) then values of HAMD and ‘D’ are superimposed while those of ‘O’ are typically opposed to ;D’ thus nonsuperimposed with HAMD.

Figure 5. HAMA, ‘CFg,’ and ‘CFb’ comparison The results from the scale for Hamilton anxiety rating scale (HAMA) in blue are compared with the values of ‘CFg’ general constrain frustration and ‘CFb’ bound constrain frustration. Here superposition and correlations apply to a lesser degree.

Figure 5 compares values of constraint frustrations ‘CFg’ and ‘CFb’ with values of the Hamilton anxiety scale (HAMA). Here it is clear that although superposition is the rule for ‘CFg’ and ‘CFb’ this is not the case with the HAMA Scale.

DISCUSSION As mentioned above, the aim of the study is to show that the various parameters of the CBP representing different disturbances to optimal brain dynamics are correlated to consensual clinical psychiatric scales evaluating relevant mental disorders. In addition we try to show that a program device designed to translate clinical findings into supposed theoretical brain dynamic disturbances is kept within the function of traditional psychiatric assessment. Superposition of relevant graphs indicates the correlations between the relevant evaluations. Graph superposition of CBP parameters and consensual scale indicate that CBP parameters are correlated with regular consensual psychiatric scales.

Figure 6. CSPD comparison with all CBP parameters.

The results from rating context-sensitive processing decline (CSPD) are compared with all other CBP results. The values of ‘D’ deoptimization, ‘O’ hyperoptimization, ‘CFg’ general constrain frustration and ‘CFb’ bound constrain frustration are more likely to correlate with CSPD levels then the values of ‘Cs’ connectivity segregation, ‘Htd’ hierarchical top-down shift, ‘Ci’ connectivity integration and ‘Hbu’ hierarchical bottom-up insufficiency. Figure 6 compares CSPD values with all other CBP assessments. Since the values of CSPD roughly correlate to personality disorders, it is expected that typically this parameter will correlate more with ‘D,’ ‘O,’ ‘CFg’ and ‘CFb’ then with ‘Cs,’ ‘Ci,’ ‘Htd’ and ‘Hbu.’ This is because clinically mood and anxiety disorders go together with the complaints of patients suffering from personality disorders. Psychotic positive signs may correlate with borderline personality disorders but they are limited to this one personality disorder and in a more rare coincidence. Figure 6 shows higher superposition of ‘D,’ ‘O,’ ‘CFg’ and ‘CFb’ with CSPD than of ‘Cs,’ ‘Ci,’ ‘Htd’ and ‘Hbu’ with CSPD, which is in accordance with the clinical prediction.

Patients scoring high on 'Cs' and 'Htd' CBP parameters are also patients that would score highly on SAPS scale, thus they are patients with psychotic positive symptoms schizophrenia. If we use CBP diagnosis for a patient that suffers mainly from positive symptoms and psychotic symptoms we would generate a brain hypothesis for his disturbance (i.e., 'Cs' and 'Htd'), one that supposes a disconnection disintegrated brain activity. Patients scoring high on 'Ci' and 'Hbu' CBP parameters are also patients that would score highly on SANS scale, thus they are patients with negative poverty symptoms schizophrenia. If we use CBP diagnosis for a patient that suffers mainly from negative symptoms and poverty symptoms, we would generate a brain hypothesis for his disturbance (i.e., 'Ci' and 'Hbu'), one that supposes an overintegration and over-connection for brain dynamics, with collapse and insufficiency of higher-level brain organizations. Deoptimization dynamics 'D' correlates very well with Hamilton Depression scale, thus applying CBP to a patient that suffers from depression (or depressed symptoms together with, or as part of, another disorder) would result in high values of

deoptimization dynamics proposing this as the underlying brain disturbance for that patients. Such dynamics can predominate standing alone, or as part of other additional perturbations to brain organization. The study did not include manic assessment scale but it is clear that hyperoptimization dynamics opposes deoptimization dynamics as seen on the relevant graph (Fig 4). Hamilton Anxiety scale correlated to a lesser degree then other scales with the relevant ‘CFg’ and ‘CFb’ this may account to different emphasize between the two assessment tools. The CBP made les emphasize on body sensations concentrating more on the psychological complaints of anxiety. This can be corrected by adding the relevant questions to the matrix format of table 1. Alternatively, reconsideration of body sensation and its relevance to actual brain disturbances of constraint satisfaction can be revisited. The clinical findings which are typical to those suffering from traditionallydiagnosed personality disorders result in high levels of CSPD when assessment with CBP is made. Thus comparison of CSPD with the other CBP parameters show that ‘D’ and ‘CFg’ are the parameters that correlate best with CSPD. This is in line with the clinical experience of having anxiety and depression as representative symptoms of patients suffering from personality disorders. The limitation of this work involves its theoretical foundation. As a theoretical model the CBP is inherently exposed to criticism, anyone can question its validity and argue against its foundational assumptions. One can argue that having highly superimposed parameters of CBP with regular psychiatric assessments means there is no real meaning to the CBP values other then a semantic exchange with traditional diagnosis. This argument is not accurate because the CBP matrix allows spectrum rather then entity- based diagnosis, thus full correlation between

CBP diagnosis and traditional DSM entities don’t exist. Having an incremental and overlapping scoring, the CBP diagnosis is suited for the assessment of spectrum-based phenomena such as that of clinical manifestations of mental disorders. As mentioned above, the aim of this work is to show the superposition of CBP with traditional clinical assessment of mental disorder. More work and future development to CBP is needed, but once the general framework of the CBP diagnosis becomes consistent with consensual clinical assessment in psychiatry, it can be proposed for an individually-related hypothesis generator about brain disturbance of the individual patient. The patient assessed based on the CBP receives a testable-perdition profile, one that proposes to diagnose his underlying brain disturbances specifically related to his clinical manifestation. For example if loosening of associations and delusions dominate his clinical manifestation his proposed brain disturbance will involve high probability of disconnection segregated brain disorder. Similarly if his clinical manifestation is dominated by depression then deoptimization dynamics dominate his brain organization. As already mentioned imaging analysis methods are beginning to gain the potential of detecting disconnection dynamics in the brain (Kiebel et al 2006; 2007), it is ssumed that in the future these same methods will also be able to detect over-integration dynamics. Accordingly, when applied in the time domain, also optimization dynamics could be revealed by these methods of signal processing. CBP when developed further may generate a patient-specific testableprediction for the imaging analysis of the specific patient. This would be the critical proof, or refute, of the CBP theory. If proven (and accordingly developed) then the CBP has the potential to provide the basis for an objective and etiologic (brainrelated) psychiatric diagnosis.

This work could be a first step for a potential new outlook on psychiatric diagnosis, one that is brain-related and thus open to investigation and validation. REFERENCES Andreasen N.C., The Scale for the Assessment of Negative Symptoms (SANS). Iowa City: University of Iowa, 1983. Andreasen N.C., The Scale for Assessment of Positive Symptoms (SAPS). Iowa City: University of Iowa, 1984. Andreasen N.C., Linking Mind and Brain in the Study of Mental Illnesses: A Project for a Scientific Psychopathology. Science, 1997; 275 (14 March): 15861596. Brambilla P, Tansella M.The role of white matter for the pathophysiology of schizophrenia.Int Rev Psychiatry. 2007 Aug;19(4):459-68

Peled A and Geva AB. Brain Organization and Psychodynamics. J Psychotherapy Practice and Research 1999 Winter;8(1):24-39 Peled A. Brain dynamics and mental disorders Yozmot Heliger 2004 Peled A. From plasticity to complexity. A new Diagnostic Method for Psychiatry. Med Hypotheses. 2004;63(1):1104. Peled A. Plasticity imbalance in mental disorders the neuroscience of psychiatry: Implications for diagnosis and research. Medical Hypothesis 2005 July 1. Peled A. brain profiling and clinical neuroscience. Medical Hypothesis 2006 May 12; Medical hypothesis 67, 941-946 2006. Peled A, Brand D. Optimizers 2050 Saga Books 2006 ISBN:1-894936-56-6. Peled A. NeuroAnalysis (in preparation Rougtledg 2007)

Casanova MF. Schizophrenia seen as a deficit in the modulation of cortical minicolumns by monoaminergic systems. Int Rev Psychiatry. 2007 Aug;19(4):361-72

Ragland JD, Yoon J, Minzenberg MJ, Carter CS. Neuroimaging of cognitive disability in schizophrenia: search for a pathophysiological mechanism. Int Rev Psychiatry. 2007 Aug;19(4):417-27

Cohen J.D., Servan-Schreiber D., A Theory of Dopamine Function and its Role in Cognitive Deficits in Schizophrenia. Schizophrenia Bulletin 1993; 19(1): 85103.

Tononi G. Edelman G.M., (2000). Schizophrenia and the Mechanisms of Conscious Integration. Brain Research Reviews 31, 391-400.

Friston K.J., Frith C.D., Schizophrenia a Disconnection Syndrome? Clinical Neuroscience 1995; 3: 89-97. Geva AB and Peled A. Simulation of Cognitive Disturbances by a Dynamic Threshold Neural Network Model. Journal of International Neuropsychology, 2000 Jul; 6(5);608-19. Hamilton M. Development of a rating scale for primary depressive illness. Br J clin Soc Psychol 1967; 6: 278-296 Hamilton M. The assessment of anxiety states by rating. Br J Med Psychol. 1959;32(1):50-5. Hebb D.O., The Organization of Behavior. New York: John Wiley & Sons, 1949. Herz J., Krogh A., Richard G.P., Introduction to the Theory of Neural Computation. Santa Fe: Santa Fe Institute Addison Wesley, 1991. Institute S.F., ed. Kiebel SJ, Kloopel S, Weiskopf N, Friston KJ. Dynamic causal modeling: A generative model of slice timing in fMRI. Neuroimage. 2007 Feb 15: 34(4)487-96 Kiebel SJ, David O, Friston KJ. Dynamic causal modeling of evoked responses in EEG/MEG with lead field parameterization. Neuroimage 2006. May 1; 30(4):127384. Mesulam M., From Sensation to Cognition. Brain 1998; 121: 1013-1052. Peled A., Geva A.B., Kremen W.S., Blankfeld H.M., Hoff A.L., Esfandiarfard R., Espinoza S., Nordahl T.E., Functional Connectivity and Working-Memory in Schizophrenia. Press Journal of Neuroscience, 2000. Peled A., Multiple Constraint Organization in the Brain: A Theory for Serious Mental Disorders. Brain Research Bulletin1999, 49: 245-250.

Tononi G., Sporns O., Edelman G.M., A Measure for Brain Complexity: Relating Functional Segregation and Integration in the Nervous System. Proc Natl Acad Sci 1994; 91(May): 5033-5037.

clinical brain profiling

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