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In the last decade the simplification in clinical medicine and the unethiological, general approach is very frequent (the “medicine of consequences”, N. Ilankovic). Because the syndromological diagnoses and (relative) effective symptomatic therapy, in clinical psychiatry in most cases the targets recently are only the phenomenology of behavioral disorders and the hypothezid neurochemical consequences, without precise etiopathogenetic or/and psychodynamic approach.
Researchers have found that both medical and psychiatric comorbidity is common in patients with psychiatric disorders, particularly in children, adolescents and in old age. This nonethiological approach push the mental illnesses and the mental ill patients back in the darkness of unscientific era of middle century.
To schow how many psychotic disorders has real and detectable etiology.
Clinical and etiological analysis of 100 patients with psychotic disorders with data of all clinical laboratory and neuroimaging investigationes.
In most of patient (29%)t he cause of psychotic episode was the substance abuse, in 25% focal and systemic infection (inflammation), in 16% endocrin-metabolic disorders, in 11% brain damage, in 7% cerebrovascular disorders, and in 7% neurodevelopmental disorders.
The real etiological approach in clinical psychiatry open the door to most targeted etiological therapy of psychotic and other mental disorders.
In the last decades the unethiological approach in clinical medicine is very frequent (the ‘medicine of consequences’, N. Ilankovic). Because the syndromological diagnoses and (relative) effective symptomatic therapy in clinical psychiatry, in most cases the therapeutic targets are only the phenomenology of behavioral disorders and the neurochemical, imunological and morphological consequences, without precise etiopathogenetic approach.
Researchers have found that both medical and psychiatric comorbidity is common in patients with psychiatric disorders, particularly in children, adolescents and in old age.
To schow how many psychotic disorders have real and detectable etiology, very frequently extracerebral origin.
Clinical and etiological analysis of data of all clinical laboratory and neuroimaging investigationes by 100 patients with psychotic disorders with schizophrenic and schizophreniform clinical pictures.
In most of patient (29%) the cause of psychotic episode was the substance abuse, in 25% extracerebral focal and systemic infection (inflammation), in 16% endocrin-metabolic disorders (extracerebral origin), in 11% brain damage, in 7% cerebrovascular disorders, and in 7% neurodevelopmental disorders.
In about 70% of our patients with schizophrenic and schizophreniform psychotic disorders the primary causes of illness were extracerabral. The real etiological approach and diagnosis in clinical psychiatry open the door to most targeted etiological therapy of psychotic and other mental disorders.
One year affective symptom status rating with BP-II for 40 patients were based on interviews conducted at one month interval in prospective follow up. The clinical pictures, the number of shifts and the polarity were examined.
In these 1 years follow up the patients with BP-II were szmptomatic 56.1 % of months: depressive 61.2 % of months, manic 9.5 % of months and mixed 6.3 %. Subsyndromal (minor) depressive states and hzpomanic syndromes were evaluated in 33 % of months.
According to this results, our older results and many other clinical observations in literature, it is obligatory to introduce the new clinical/nosological category in current clinical praxis and in new classifications with name TRIPOLAR AFFECTIVE DISORDERS.
Numerous studies have applied novel multivariate statistical approaches to the analysis of brain alterations in patients with schizophrenia. However the diagnostic accuracy of the reported predictive models differs largely, making it difficult to evaluate the overall potential of these studies to inform clinical diagnosis.
We conducted a comprehensive literature search to identify all studies reporting performance of neuroimaging-based multivariate predictive models for the differentiation of patients with schizophrenia from healthy control subjects. The robustness of the results as well as the effect of potentially confounding continous variables (e.g. age, gender ratio, year of publication) was investigated.
The final sample consisted of n=37 studies studies including n=1491 patients with schizophrenia and n=1488 healthy controls. Metaanalysis of the complete sample showed a sensitivity of 80.7% (95%-CI: 77.0 to 83.9%) and a specificity of 80.2% (95%-CI: 83.3 to 76.7%). Separate analysis for the different imaging modalities showed similar diagnostic accuracy for the structural MRI studies (sensitivity 77.3%, specificity 78.7%), the fMRI studies (sensitivity 81.4%, specificity 82.4%) and resting-state fMRI studies (sensitivity 86.9%, specificity 80.3%). Moderator analysis showed significant effects of age of patients on sensitivity (p=0.021) and of positive-tonegative symptom ratio on specificity (p=0.028) indicating better diagnostic accuracy in older patients and patients with positive symptoms.
Our analysis indicate an overall sensitivity and overall specificity of around 80 % of neuroimaging-based predictive models for differentiating schizophrenic patients from healthy controls. The results underline the potential applicability of neuroimaging-based predictive models for the diagnosis of schizophrenia.
Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with global functional connectivity (FC) alterations of resting-state (RS) networks. Only few studies tried to combine these data domains to outperform unimodal pattern classification approaches. We aimed at distinguishing SZ patients from healthy controls (HC) at the single-subject level by applying multivariate pattern recognition analysis to both gray matter (GM) volume and FC measures.
The RS functional and structural MRI data from 74 HC and 71 patients with SZ were obtained from the publicly available COBRE database. The machine learning pipeline wrapped into repeated nested cross-validation was used to train a multi-modal diagnostic system and evaluate its generalization capacity in new subjects.
Both functional and structural classifiers were able to distinguish between HC and SZ patients with similar accuracies. The RS classifier was showing a slightly higher accuracy (75%) comparing to GM volume classifier (74.4%). Ensemble-based data fusion outperformed pattern classification based on single MRI modalities by reaching 76.6% accuracy, as determined by cross-validation. Further analysis showed that RS classification was less sensitive to age-related effects across the life span than GM volume.
Our findings suggest that age plays an important role in discriminating SZ patients from HC, but that RS is more robust towards age-differences compared to GM volume. Single neuroimaging modalities provide useful insight into brain function or structure, while multimodal fusion emphasizes the strength of each and provides higher accuracy in discriminating SZ patients from HC.
Monthly affective symptom status ratings for 30 patients with BP-II were based on interviews conducted at 3- or 6-month intervals during 5 years of prospective follow-up. The clinical characteristics and number of shifts in symptom status and polarity were examined.
In these 5 years our patients with BP-II were symptomatic 61.2% of all follow-up months: depressive symptoms (55.4% of months) dominated over hypomanic (13.4% of months) and mixed (7.3% of months) symptoms. Subsyndromal (minor) depressive and hypomanic syndromes were evaluated in 23% of months. With mixed syndromes together it is 30.3% of months!
We propose the introducing of INTERMEDIATE STATE (subsyndromal depression, mixed and hypomanic states) in course of bipolar affective disorders, with special importance of this new clinical concept for etiological research and more targeted pharmacotherapy. We think that the new name/diagnosis TRIPOLAR affective disorders (depressive, manic and intermediate states - subsyndromal depression, hypomanic and mixed states) is more adequate for the course of this chronic affective illness.
Everyday clinical routine is frequently challenged by difficulty to choose among differential diagnostic options, since many psychiatric disorders share similar phenotypes. E.g., borderline personality disorder (BPD) and schizophrenia (SZ) can both be associated with psychotic syndromes.
Our objective was to evaluate the effectiveness of combining sMRI data and pattern classification methods to differentiate between BPD and SZ.
We aim to introduce objective diagnostic measures to improve the reliability of clinical evaluations.
sMRI data of 114 female patients were used to train a multivariate disease classifier.
MR images were processed using voxel-based morphometry and high-dimensional registration to the MNI template. Grey matter volume maps were fed into a machine learning pipeline consisting of adjustment for possible age effects, PCA for dimensionality reduction and linear ν-support vector classification. Diagnostic performance of the classifier was determined by repeated nested 10-fold cross-validation.
We were able to correctly classify unseen test subjects’ diagnosis with 74% accuracy. Classification sensitivity and specificity was 74%. Volume reductions in SZ vs. BPD were predominantly located in the left peri- and intrasylvian regions, orbitofrontal regions, the nucleus caudatus and the right cerebellum. Volume reductions in BPD compared to SZ were found predominantly in the left cerebellum, in limbic areas and the left inferior occipital gyrus.
Our results suggest that SZ can be differentiated from BPD at the single-subject level using sMRI and pattern classification methods. In future, this method might enhance clinical evaluations and improve accuracy and reliability of differential diagnosis.
The clinical differentiation of schizophrenic and mood disorders is frequently challenged by co-occurring affective and psychotic symptoms. Thus, it has long been discussed whether these disease groups are subserved by common or distinct neurobiological surrogates.
The detection of diagnostic biomarkers for schizophrenic and mood disorders could facilitate clinical decision making in ambiguous cases.
To evaluated whether multivariate pattern classification of structural MRI enables the differential diagnostic classification of 158 patients with schizophrenia (SZ) and 104 patients with major depression (MD).
T1-weighted patient scans were processed using voxel-based morphometry. Diagnostic features were extracted from the age- and sex-adjusted GM maps using PCA and linear SVMs. Repeated nested cross-validation was emplyoed to assess the generalizability of diagnostic performance.
Cross-validated classification accuracy was 76% based on a discriminative pattern involving perisylvian, limbic, medial prefrontal and precuneal GM volume reductions in SZ vs. MD. GM volume reductions in MD vs. SZ were detected in the premotor, sensorimotor, parietal, cerebellar and brainstem structures. The 'SZ-likehood' of MD was correlated with the age of disease onset, leading to a significantly higher misclassification rate among MD patients with an age of onset between 15 and 30 yrs.
The findings suggest that SZ and MD can be identified at the single subject level using neuroanatomical pattern recognition. The decreased diagnostic separability of MD patients with an early disease onset may challenge the traditional nosological boundaries and may relate to higher levels of chronicity and unfavorable disease outcomes in this patient population.
Many of so called schizophrenias are symptomatic, secondary cerebral dysfunction (disorders). The unethiological approach and the symptomatic pharmacotherapy, push this patient automatically in group of chronic mental illneses and in big stigmatisation of patients and their family.
Clinical, neuropsychological, laboratoric, neuroimaging and ethiological analysis of sample of 100 patient with schizophreniform clinical pictures.
In 29 % of patients the cause was the substance abuse, in 25 % extracerebral infection/inflammtion, in 16 % endocrine/metabolic disorders, in 11 % brain damage, in 7 % cardiovascular disorders and in 7 % neurodevelopmental disorders.
In about 70 % of ur patients with schizophrenical and schizophreniform psycotic clinical pictures the primary cause were extracerebral. The real ethiological approach and diagnosis in clinical psychiatry is the only way to targeted ethiological therapy and reduction of chronification and stigmatisation of psychiatric patients
The enlarged cava septi pellucidi (CSP = 6 mm in length) have been reported as a reliable marker of an underlying neuropsychiatric disease or disorder. Differences in the dimensions of cava longer than 6 mm associated with a neuropsychiatric impairment could be of possible clinical and forensic significance.
We obtained 479 brains from autopsied persons (310 males and 169 females, aged 22–89 years) and observed that 110 brains (75 males and 35 females) had CSP, of which the length of CSP was equal to or longer than 6 mm on 69 (49 males and 20 females) of them. These cava were classified into four groups depending on the past medical histories of the autopsied person: five without neuropsychiatric history (asymptomatic CSP), 25 schizophrenic patients, 22 alcoholics, and 17 with a past head trauma (symptomatic CSP).
The linear parameters of CSP (i.e. length, width) of the symptomatic and asymptomatic groups were measured and were statistically analyzed. Analysis revealed that the cava in the group of schizophrenic patients were significantly longer and wider.
Discriminant function analysis was used to derive a mathematical formula to classify CSP into one of the groups obtained based on width measurements of the cavum.
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