Biochemical markers in major depression as interface between neuronal network and Artificial Neural Network (ANN)

Cocchi, Massimo; Tonello, Lucio; Cappello, G.; Nabacino, L.; Passi, S.; Baldini, N.; Soreca, I.; Paffetti, I.; Castrogiovanni, Paolo; Tarozzi, G

doi:10.4081/jbr.2006.8085

Cited by 4 publications

(3 citation statements)

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“…Platelet fatty acids (PA, LA, AA) results obtained from apparently normal subjects and from subjects generically classified with Mood Disorders (psychiatric diagnosis) were analysed through the use of a Self Organizing Map (SOM), as described in previous reports (Cocchi, Tonello, & Cappello, 2006; Three main areas have been obtained: 1) Normal subjects area (a-only green dots); 2) Depressive and Bipolar subjects area (b-only red dots); 3) Mixture of both (c-red and green dots). The third area was split into two other areas (yellow and orange) according to the major density of normal (yellow) and pathological (orange) subjects.…”

Section: Evidence Of the Som In Classifying Depressive And Bipolar Sumentioning

confidence: 99%

“…Cocchi and Tonello have studied platelet membranes of depressed subjects, enlisting profiles of FAs as a possible measure of the membrane status and to determine whether fatty acids could provide indications of diagnostic help between normal subjects and subjects affected by mood disorders. In the first experimental phase, two mathematical tools were identified, a complex one (Self Organizing Map (SOM)) and a simple one (the B2 Index), which will prove valuable not only to define the condition of the Major Depression and Bipolar Disorder, but also to provide the possibility of reasonable inferences about the biological significance of the two molecular mood disorders Cocchi, Tonello, & Cappello, 2006;Cocchi & Tonello, 2007;Cocchi, Tonello, Tsaluchidu et al, 2008;Cocchi, Tonello, De Lucia et al, 2009a;Cocchi, Tonello, De Lucia et al, 2009b).…”

Section: Introductionmentioning

confidence: 99%

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Platelet’s Fatty Acids and Differential Diagnosis of Major Depression and Bipolar Disorder through the Use of an Unsupervised Competitive-Learning Network Algorithm (SOM)

Benedetti¹,

Bucciarelli²,

Canestrari³

et al. 2014

OJD

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Aim of the study: The main purpose of this work was to verify, through the study of three fatty acids of platelets (namely: Palmitic Acid-PA, Linoleic Acid-LA, Arachidonic Acid-AA) the ability to distinguish adult subjects with Major Depression (MD) from those with Bipolar Disorder (BD), using an artificial neural network (Self Organizing Map-SOM) and an indirect index of the viscosity of the membrane (B2), in agreement with previous results; secondly, the ability to understand any similarities between children and adults in the molecular characterization of mood disorders, both in general and in relation to a subset of individuals with suicidal ideation, indicated by the survey instruments used (SOM). Design: The study design, in order to achieve the objectives, has forecast the recruitment of three groups of subjects without regard to sex, age, food intake patterns, or pharmacological therapies: 1) Controls; 2) Subjects with Major Depression; 3) Subjects with Bipolar Disorder. They were provided for the following investigations: 1) Platelet Fatty Acids analysis; 2) Plasma and platelet serotonin levels; 3) Oxidative stress and inflammation markers. * Corresponding authors. S. Benedetti et al. 53Moreover, the data of the fatty acids of platelets, previously obtained by a group of children were used, for comparison, in the SOM, with adults surveyed. Subjects participating in the study: All participants, volunteer, were recruited in the judgment of psychiatrists as they presented themselves to the office visit of the "Dipartimento di Salute Mentale", ASUR 4, Fano, Italy. All subjects were submitted to a semi-structured interview based on DSM-IV-TR criteria. 21-item Hamilton Depression Rating Scale (HDRS), Hamilton Anxiety Rating Scale (HARS), Clinical Global Impressions Scale (CGI), Symptom Checklist-90 (SCL-90), and Hypomania/Mania Symptom Checklist (HCL-32) were also applied to each patient. Adult subjects who did not give consent, patients with a diagnosis other than an affective disorder and subjects in their first clinical episode were excluded. The study was double blind. Outcomes: The results obtained have confirmed the main objective of the study. It was possible to obtain, in fact, the recognition of individuals with Major Depression and Bipolar Disorder, using the SOM and the index B2. Value of the study: The methodology used in this study may be of utility, such as quantitative diagnostic support to the psychiatrist, in order to reduce the high error that occurs in the first diagnosis, with regard to mood disorders. Limitations of this study: The limitations of this study are mainly related to the number of controls. They should have been more numerous such as the number of suicidal ideations. About the comparison with children, the main limitation seems to be the lack of psychiatric diagnosis of children, so that any results just assume the value of hypotheses. Future Research: Because of the strong classificatory properties of the SOM, it would require a RCT in a larger sample of subjects with mood dis...

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Section: Evidence Of the Som In Classifying Depressive And Bipolar Sumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Platelet’s Fatty Acids and Differential Diagnosis of Major Depression and Bipolar Disorder through the Use of an Unsupervised Competitive-Learning Network Algorithm (SOM)

Benedetti¹,

Bucciarelli²,

Canestrari³

et al. 2014

OJD

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“…All these observations indicate that the gut microbiome and some behavioral patterns may be linked in mammals. In 2006, Cocchi [9] carried out a study on the similarity between the fatty acid profiles of platelets and neurons. Based on this similarity, they were able to identify different mood disorders (Major Depression and Bipolar Disorder) by creating an artificial neural network, whose crucial factors were three fatty acids (i.e., Palmitic Acid, Linoleic Acid, Arachidonic Acid) [10,11].…”

Section: Introductionmentioning

confidence: 99%

Depression and Microbiome—Study on the Relation and Contiguity between Dogs and Humans

et al. 2020

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Behavioral studies demonstrate that not only humans, but all other animals including dogs, can suffer from depression. A quantitative molecular evaluation of fatty acids in human and animal platelets has already evidenced similarities between people suffering from depression and German Shepherds, suggesting that domestication has led dogs to be similar to humans. In order to verify whether humans and dogs suffering from similar pathologies also share similar microorganisms at the intestinal level, in this study the gut-microbiota composition of 12 German Shepherds was compared to that of 15 dogs belonging to mixed breeds which do not suffer from depression. Moreover, the relation between the microbiota of the German Shepherd’s group and that of patients with depression has been investigated. The results indicate that the German Shepherd’s gut-microbiota has a different composition compared to other dog breeds and is characterized by microbial groups identified in humans with depression, highlighting the existence of a “core” microbiota associated with depression.

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Stream of consciousness: Quantum and biochemical assumptions regarding psychopathology

Tonello¹,

Cocchi

Gabrielli³

et al. 2017

Medical Hypotheses

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Biochemical markers in major depression as interface between neuronal network and Artificial Neural Network (ANN)

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Cited by 4 publications

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Platelet’s Fatty Acids and Differential Diagnosis of Major Depression and Bipolar Disorder through the Use of an Unsupervised Competitive-Learning Network Algorithm (SOM)

Platelet’s Fatty Acids and Differential Diagnosis of Major Depression and Bipolar Disorder through the Use of an Unsupervised Competitive-Learning Network Algorithm (SOM)

Depression and Microbiome—Study on the Relation and Contiguity between Dogs and Humans

Stream of consciousness: Quantum and biochemical assumptions regarding psychopathology

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