Neuroanatomic and Functional Neuroimaging Findings

Diaz, Alexandre Paim; Bauer, Isabelle E.; Sanches, Marsal; Soares, Jair C.

doi:10.1007/7854_2020_174

Cited by 3 publications

(4 citation statements)

References 96 publications

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“…Genetic, biochemical, bioenergetic, inflammatory, and brain activity and connectivity dysfunction have been reported as associated with BD. 11,12 These dysfunctional biological processes can help identify effective treatment interventions. [11][12][13][14] However, the neurobiology could be even harder to disentangle when dealing with specific clinical characteristics and mood states, such as treatment-resistant depression.…”

Section: Neurobiological Variablesmentioning

confidence: 99%

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Treatment-resistant bipolar depression: concepts and challenges for novel interventions

Diaz

Fernandes

Quevedo

et al. 2022

Braz. J. Psychiatry

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Treatment-resistant bipolar depression (TRBD) has been reported in about one-quarter of patients with bipolar disorders, and few interventions have shown clear and established effectiveness. We conducted a narrative review of the published medical literature to identify papers discussing treatment-resistant depression concepts and novel interventions for bipolar depression that focus on TRBD. We searched for potentially relevant English-language articles published in the last decade. Selected articles (based on the title and abstract) were retrieved for a more detailed evaluation. A number of promising new interventions, both pharmacological and non-pharmacological, are being investigated for TRBD treatment, including ketamine, lurasidone, D-cycloserine, pioglitazone, N-acetylcysteine, angiotensin-converting enzyme inhibitors, angiotensin II type 1 receptor blockers, cyclooxygenase 2 inhibitors, magnetic seizure therapy, intermittent theta-burst stimulation, deep transcranial magnetic stimulation, vagus nerve stimulation therapy, and deep brain stimulation. Although there is no consensus about the concept of TRBD, better clarification of the neurobiology associated with treatment non-response could help identify novel strategies. More research is warranted, mainly focusing on personalizing current treatments to optimize response and remission rates.

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Section: Neurobiological Variablesmentioning

confidence: 99%

“…11,12 These dysfunctional biological processes can help identify effective treatment interventions. [11][12][13][14] However, the neurobiology could be even harder to disentangle when dealing with specific clinical characteristics and mood states, such as treatment-resistant depression.…”

Section: Neurobiological Variablesmentioning

confidence: 99%

Treatment-resistant bipolar depression: concepts and challenges for novel interventions

Diaz

Fernandes

Quevedo

et al. 2022

Braz. J. Psychiatry

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“…Genetic differences between patients with BD I and BD II have been suggested [96], [97], [98], [99], [100], using family databases, but neuroimaging differences have not been confirmed [101]. The bipolar spectrum is highly heterogeneous, with many different biotypes and their probable neurobiological causes [4], [102], [103], [104], [105]; different biotypes can be fully represented only in large samples [4]. The BD group of 37 subjects includes 7 patients who had committed suicide, a number close to known prevalence of death by suicide in BD.…”

Section: Limitations Of the Studymentioning

confidence: 99%

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Thomaidis

Michos

Papadimitriou³

et al. 2022

Preprint

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Objective: Complex machine learning classification algorithms using transcriptome data from post-mortem cerebellar tissue of bipolar patients and unaffected controls, have been recently included in pipelines for patient control classification and identification of characteristic biomarkers. Transcriptomic profile differences between patients and controls, can provide useful information about the role of the cerebellum in the pathogenesis of bipolar disorder and mood deregulation and in normal mood regulation and physiology. User-friendly, fully automated machine learning algorithms, using data extracted from established repositories, could achieve extremely high classification scores and disease- related predictive biomarker identification, in very short time frames and scaled down to small datasets, thus facilitating research on mood disorders. Method: An application of a fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper- parameter values, for classification between patients and controls and the production of models for biosignature selection, is presented. Transcriptome data used for the analysis were downloaded from the BioDataome preprocessed datasets database. The Dataome dataset, derived from the parent Gene Expression Omnibus GSE35974 (2013) and GSE35978 datasets, which have been originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and unaffected controls, (from the Stanley Medical Research Institute Neuropathology Consortium and Array collections), using Affymetrix Human Gene 1.0 ST Array. Patient and control groups were closely matched for age and sex . Results: Bipolar patients have been identified from controls based on the cerebellar transcriptomic profile with AUC 0.929 and Average Precision 0.955. Patients and Controls have been classified in two separated groups with no close to the boundary cases. Using 6 of the characteristic features discovered during the selection process, 99,6% classification accuracy was achieved. The three biomarkers contributing most to the predictive power of the model (92,7%), are also deregulated in temporal lobe epilepsy. Conclusion: The cerebellar transcriptome of bipolar patients has a discrete profile and can be used for further exploration of the role of this area in health and disease. 93% AUC and 96% Precision were achieved during classification between unaffected controls and patients with Bipolar Disorder.

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“…However, the genetic architecture of the disorder is still far from being elucidated [ 14 , 15 ]. Hypomanic and manic episodes have been reported in up to 10% of HD patients [ 16 , 17 ], and the pathogenesis of HD and bipolar disorder is connected to the same brain structures [ 18 , 19 , 20 , 21 , 22 , 23 ]. This finding could suggest the presence of common mechanisms underlying the development of HD and bipolar disease.…”

Section: Introductionmentioning

confidence: 99%

The Huntington’s Disease Gene in an Italian Cohort of Patients with Bipolar Disorder

Ferrari,

Capacci,

Bagnoli

et al. 2023

Genes

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Background and objectives: Huntington’s disease (HD) is characterized by motor, cognitive and psychiatric manifestations and caused by an expansion of CAG repeats over 35 triplets on the huntingtin (HTT) gene. However, expansions in the range 27–35 repeats (intermediate allele) can be associated with pathological phenotypes. The onset of HD is conventionally defined by the onset of motor symptoms, but psychiatric disturbances can precede the motor phase by up to twenty years. The aims of the present study are to identify HD patients in the pre-motor phase of the disease among patients diagnosed with bipolar disorders and evaluate any differences between bipolar patients carrying the normal HTT allele and patients with the expanded HTT gene. Methods: We assessed the HTT genotype in an Italian cohort of 69 patients who were affected by either type 1 or type 2 bipolar disorder. Results: No patient was found to be a carrier of the pathological HTT allele, but 10% of bipolar subjects carried an intermediate allele. Carriers of the intermediate allele were older at the onset of psychiatric symptoms than non-carriers. Conclusion: The pathological HTT gene was not associated with bipolar disorder, while we found a higher frequency of the intermediate allele among the bipolar population with respect to healthy controls. The identification of this subset of bipolar subjects has implications for the clinical management of patients and their family members and promotes further investigation into possible pathological mechanisms common to both HD and bipolar disorder.

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Neuroanatomic and Functional Neuroimaging Findings

Cited by 3 publications

References 96 publications

Treatment-resistant bipolar depression: concepts and challenges for novel interventions

Treatment-resistant bipolar depression: concepts and challenges for novel interventions

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

The Huntington’s Disease Gene in an Italian Cohort of Patients with Bipolar Disorder

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