A dimensional approach to affective disorder: The relations between Scl-90 subdimensions and QEEG parameters.

Objectives: QEEG reflects neuronal activity directly rather than using indirect parameters, such as blood deoxygenation and glucose utilization, as in fMRI and PET. The correlation between QEEG spectral power density and Symptom Check List-90-R may help identify biomarkers pertaining to brain function, associated with affective disorder symptoms. This study aims at determining whether there is a relation between QEEG spectral power density and Symptom Check List-90-R symptom scores in affective disorders. Methods: This study evaluates 363 patients who were referred for the initial application and diagnosed with affective disorders according to DSM-V, with QEEG and Scl-90-R. Spectral power density was calculated for the 18 electrodes representing brain regions. Results: Somatization scores were found to be correlated with Pz and O1 theta, O1 and O2 high beta. Whereas FP1 delta activities were correlated with anxiety, F3, F4, and Pz theta were correlated with obsession scores. Interpersonal sensitivity scores were found to be correlated with F4 delta, P3, T5, P4, T6 alpha and T5, and T6 theta activities. While depression scores were correlated with P3 and T4 delta, as well as T4 theta, there was a correlation between anger and F4, as well as T4 alpha and F8 high beta activities. Paranoia scores are correlated with FP1, F7, T6 and F8 theta, T5 and F8 delta, and O2 high beta activities. Conclusions: According to our results, anxiety, obsession, interpersonal sensitivity, depression, anger, and paranoia are related to some spectral powers of QEEG. Delta-beta coupling seems to be a neural biomarker for affective dysregulation.

QEEG - spectral power density of brain regions in predicting risk, resistance and resilience for bipolar disorder: A comparison of first degree relatives and unrelated healthy subjects.

BACKGROUND: Temperament stems from the brain circuitry. Genetic differences among people are attributable to differences in neurophysiological function. Affective temperament is proposed endophenotype for bipolar affective disorder. QEEG - spectral power density is thought to be an index of general affective and cognitive brain activity. The association of spectral power density with types of affective temperament may enlighten endophenotypes for bipolar affective disorder disposition. METHOD: TEMPS-A scale and rest QEEG were done on 25 euthymic patients, their healthy first degree relatives (n = 25) and 25 unrelated healthy control subjects. All patients were on lithium maintenance therapy. RESULTS: F4 and T4 delta wave activity were similar between patients and first degree relatives, while Pz alpha activity was similar in first degree relatives and unrelated healthy subjects (p = 0.025, p = 0.001, p = 0.010). Cyclothymic and hyperthymic temperament scores were similar between patients and first degree relatives but higher than unrelated healthy subjects (p = 0.015, p = 0.010). F7 beta and F7-O2 high beta power were correlated with hyperthymic and irritable temperaments respectively in bipolar subjects (r = 0.439, 0.387; 0.405, 0.364; 0.226, 0.351). T3-F4-T4 delta powers were correlated with cyclothymic temperament in patients and their first degree relatives (r = 0.443, 0.420, 505). Pz alpha power and hyperthymic temperament were inversely correlated in first degree relatives and unrelated healthy subjects (r = -0.256 and -0.311). CONCLUSION: Medial temporal network may be associated with bipolar affective disorder heritability. On the other hand, left dorsolateral prefrontal beta and high beta activities may be a neural marker for disorder resistance together with right occipital high beta power.

Bipolar Spectrum Disorder May Be Associated With Family History of Diseases.

BACKGROUND: This study aims at investigating into the presence of family history of diabetes, ischemic heart disease, thyroid disease, cancer, cerebrovascular disease, and epilepsy in bipolar patients. METHODS: Totally 1,148 patients admitted to our outpatient unit between January 2018 and January 2020, who were diagnosed with bipolar disorder according to Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V), from whom informed consent was obtained, were cross-sectionally and consecutively evaluated. Each patient was questioned regarding a family history of diabetes, ischemic heart disease, thyroid disease, cancer (gastrointestinal, breast and prostate cancer, leukemia, and lymphoma), cerebrovascular disease and epilepsy in first- and second-degree relatives. RESULTS: Diabetes, ischemic heart disease, cancer, cerebrovascular disease and epilepsy were more common in the family histories than in bipolar patients. A strong correlation was found between family history positive for epilepsy and bipolar disorder with psychotic symptoms. Also, a correlation was found between family history for diabetes and seasonal course and family history positive for thyroid disease and comorbid anxiety disorder. CONCLUSIONS: This study is the first to investigate into the frequency of physical diseases in the family histories of bipolar patients. Current therapies target the association between common leading pathways and symptoms whereas it is the association between stress and neural circuits that underlie the pathophysiology that should be targeted.

QEEG in affective disorder: about to be a biomarker, endophenotype and predictor of treatment response.

QEEG is a relatively easy to apply, cost effective method among many electrophysiologic and functional brain imaging techniques used to assess individuals for diagnosis and determination of the most suitable treatment. Its temporal resolution provides an important advantage. Many specific EEG indicators play a role in the differential diagnosis of neuropsychiatric disorders. QEEG has advantages over EEG in the dimensional approach to symptomatology of psychiatric disorders. The prognostic value of EEG has a long history. Slow wave EEG rhythm has been reported as a predictor and measure of clinical improvement under ECT. The induction level in delta band activity predicts the long term effect of ECT. Current studies focus on the predictive power of EEG on response to pharmacotherapy and somatic treatments other than ECT. This paper discusses either QEEG can be a biomarker and/or an endophenotype in affective disorders, if it has diagnostic and prognostic value and if it can contribute to personalized treatment design, through a review of relevant literature.