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Book ; Online: Neurobiology, Clinical Course, and Therapeutic Approaches of Treatment Resistant Schizophrenia: Toward an Integrated View

Iasevoli, Felice / De Luca, Vincenzo / Charles Nucifora, Frederick

2020

Keywords	Medicine ; Psychiatry ; psychosis ; antipsychotic ; clozapine ; schizophrenia ; treatment refractory
Size	1 electronic resource (130 pages)
Publisher	Frontiers Media SA
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT021230248
ISBN	9782889633319 ; 2889633314
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article: Impact of Benzodiazepines and Illness Duration on Obsessive-Compulsive Disorder during COVID-19 in Italy: Exploring Symptoms' Evolutionary Benefits.

D'Urso, Giordano / Magliacano, Alfonso / Manzo, Marco / Pomes, Mattia Vittorio / Iuliano, Carla / Iasevoli, Felice / Dell'Osso, Bernardo / de Bartolomeis, Andrea

Brain sciences

2024 Volume 14, Issue 4

Abstract: Obsessive-compulsive disorder (OCD) is believed to follow a waxing and waning course, often according to environmental stressors. During the COVID-19 pandemic, pre-existing OCD symptoms were reported to increase and to change from checking to washing ... ...

Abstract	Obsessive-compulsive disorder (OCD) is believed to follow a waxing and waning course, often according to environmental stressors. During the COVID-19 pandemic, pre-existing OCD symptoms were reported to increase and to change from checking to washing behaviors, while new-onset symptoms were predominantly of the hoarding type. In the present study, we followed the evolution of OCD symptoms, anxiety, depression, and insights of illness in forty-six OCD patients throughout the pandemic. Clinical measures were collected at four different time points before and during the COVID-19 pandemic in Italy. Within-subject comparisons were used to compare clinical scale scores across time, and correlations were examined between patients' baseline characteristics and changes in clinical scores. We found that all clinical measures increased during the first Italian lockdown with respect to the pre-pandemic values. Anxiety decreased during the temporary elimination of restriction provisions, whereas the severity of OCD symptoms and insight returned to pre-pandemic values during the second mandatory lockdown. These results were observed only in two sub-groups of patients: those taking benzodiazepines and those with shorter illness duration. Our findings suggest the need for additional clinical attention to these specific sub-groups of OCD patients in case of particularly distressing circumstances while pointing to a possible adaptive role of their OCD symptoms when the environment requires a higher care of hygiene and an extraordinary supply of essential resources.
Language	English
Publishing date	2024-03-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651993-8
ISSN	2076-3425
ISSN	2076-3425
DOI	10.3390/brainsci14040338
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Editorial: Neurobiology, Clinical Course, and Therapeutic Approaches of Treatment-Resistant Schizophrenia: Toward an Integrated View.

Iasevoli, Felice / De Luca, Vincenzo / Nucifora, Frederick Charles

Frontiers in psychiatry

2019 Volume 10, Page(s) 870

Language	English
Publishing date	2019-11-21
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2564218-2
ISSN	1664-0640
ISSN	1664-0640
DOI	10.3389/fpsyt.2019.00870
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Article: Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning.

D'Urso, Giordano / Magliacano, Alfonso / Rotbei, Sayna / Iasevoli, Felice / de Bartolomeis, Andrea / Botta, Alessio

Diagnostics (Basel, Switzerland)

2022 Volume 12, Issue 4

Abstract: During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly ... ...

Abstract	During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such as pandemics and wars, especially when these implicate an extended lockdown. The early detection of higher risk clinical and non-clinical individuals would help prevent the new onset and/or deterioration of these symptoms. This in turn would lead to the implementation of public policies aimed at protecting vulnerable populations during these dramatic contingencies, therefore optimising the effectiveness of interventions and saving the resources of national healthcare systems. We used a supervised machine learning method to identify the predictors of the severity of psychiatric symptoms during the Italian lockdown due to the COVID-19 pandemic. Via a case study, we applied this methodology to a small sample of healthy individuals, obsessive-compulsive disorder patients, and adjustment disorder patients. Our preliminary results show that our models were able to predict depression, anxiety, and obsessive-compulsive symptoms during the lockdown with up to 92% accuracy based on demographic and clinical characteristics collected before the pandemic. The presented methodology may be used to predict the psychiatric prognosis of individuals under a large-scale lockdown and thus supporting the related clinical decisions.
Language	English
Publishing date	2022-04-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics12040957
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Article ; Online: Update on novel antipsychotics and pharmacological strategies for treatment-resistant schizophrenia.

de Bartolomeis, Andrea / Ciccarelli, Mariateresa / Vellucci, Licia / Fornaro, Michele / Iasevoli, Felice / Barone, Annarita

Expert opinion on pharmacotherapy

2022 Volume 23, Issue 18, Page(s) 2035–2052

Abstract: Introduction: Treatment resistant schizophrenia (TRS), the lack of response to at least two antipsychotics administered at adequate dose and duration, epitomizes in psychiatry one of the most difficult-to-treat pathologies, epidemiologically relevant ( ... ...

Abstract	Introduction: Treatment resistant schizophrenia (TRS), the lack of response to at least two antipsychotics administered at adequate dose and duration, epitomizes in psychiatry one of the most difficult-to-treat pathologies, epidemiologically relevant (affecting one-third of schizophrenia patients) and with severe consequences for the patients in terms of overall functioning. After 50 years, only one drug is approved for TRS: clozapine. Furthermore, a few patients do not respond even to clozapine and are indicated as clozapine-resistant patients. Areas covered: In this review and expert opinion, we have critically appraised the current literature, discussing the role of old and new agents in treating resistant schizophrenia. Expert opinion: The search for therapy against TRS, beyond clozapine or in addition to clozapine, has emerged over time, capturing mainly three types of strategies: 1. Add-on of a second-generation antipsychotic (i.e. amisulpride); 2. Add-on of a second antipsychotic with significantly different receptor profile compared to the older ones (e.g. aripiprazole and cariprazine); 3. Novel strategies beyond dopamine D2/D3 receptor occupancy (e.g. xanomeline + trospium, TAAR1-agonists, sodium benzoate, and D-amino acids). More high-quality clinical trials applying the current operationalized criteria for TRS and clozapine-resistance are required to evaluate the efficacy of alternative and add-on treatments.
MeSH term(s)	Humans ; Antipsychotic Agents/therapeutic use ; Clozapine/therapeutic use ; Schizophrenia/drug therapy ; Schizophrenia, Treatment-Resistant ; Amisulpride/therapeutic use
Chemical Substances	Antipsychotic Agents ; Clozapine (J60AR2IKIC) ; Amisulpride (8110R61I4U)
Language	English
Publishing date	2022-11-17
Publishing country	England
Document type	Review ; Journal Article
ZDB-ID	2001535-5
ISSN	1744-7666 ; 1465-6566
ISSN (online)	1744-7666
ISSN	1465-6566
DOI	10.1080/14656566.2022.2145884
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Article ; Online: The Homer1 family of proteins at the crossroad of dopamine-glutamate signaling: An emerging molecular "Lego" in the pathophysiology of psychiatric disorders. A systematic review and translational insight.

de Bartolomeis, Andrea / Barone, Annarita / Buonaguro, Elisabetta Filomena / Tomasetti, Carmine / Vellucci, Licia / Iasevoli, Felice

Neuroscience and biobehavioral reviews

2022 Volume 136, Page(s) 104596

Abstract: Once considered only scaffolding proteins at glutamatergic postsynaptic density (PSD), Homer1 proteins are increasingly emerging as multimodal adaptors that integrate different signal transduction pathways within PSD, involved in motor and cognitive ... ...

Abstract	Once considered only scaffolding proteins at glutamatergic postsynaptic density (PSD), Homer1 proteins are increasingly emerging as multimodal adaptors that integrate different signal transduction pathways within PSD, involved in motor and cognitive functions, with putative implications in psychiatric disorders. Regulation of type I metabotropic glutamate receptor trafficking, modulation of calcium signaling, tuning of long-term potentiation, organization of dendritic spines' growth, as well as meta- and homeostatic plasticity control are only a few of the multiple endocellular and synaptic functions that have been linked to Homer1. Findings from preclinical studies, as well as genetic studies conducted in humans, suggest that both constitutive (Homer1b/c) and inducible (Homer1a) isoforms of Homer1 play a role in the neurobiology of several psychiatric disorders, including psychosis, mood disorders, neurodevelopmental disorders, and addiction. On this background, Homer1 has been proposed as a putative novel target in psychopharmacological treatments. The aim of this review is to summarize and systematize the growing body of evidence on Homer proteins, highlighting the role of Homer1 in the pathophysiology and therapy of mental diseases.
MeSH term(s)	Carrier Proteins/metabolism ; Dopamine/metabolism ; Glutamates/therapeutic use ; Homer Scaffolding Proteins/metabolism ; Humans ; Mental Disorders/metabolism ; Signal Transduction
Chemical Substances	Carrier Proteins ; Glutamates ; HOMER1 protein, human ; Homer Scaffolding Proteins ; Dopamine (VTD58H1Z2X)
Language	English
Publishing date	2022-03-04
Publishing country	United States
Document type	Journal Article ; Review ; Systematic Review
ZDB-ID	282464-4
ISSN	1873-7528 ; 0149-7634
ISSN (online)	1873-7528
ISSN	0149-7634
DOI	10.1016/j.neubiorev.2022.104596
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Article ; Online: Neural substrates of verbal memory impairment in schizophrenia: A multimodal connectomics study.

Tranfa, Mario / Iasevoli, Felice / Cocozza, Sirio / Ciccarelli, Mariateresa / Barone, Annarita / Brunetti, Arturo / de Bartolomeis, Andrea / Pontillo, Giuseppe

Human brain mapping

2023 Volume 44, Issue 7, Page(s) 2829–2840

Abstract: While verbal memory is among the most compromised cognitive domains in schizophrenia (SZ), its neural substrates remain elusive. Here, we explored the structural and functional brain network correlates of verbal memory impairment in SZ. We acquired ... ...

Abstract	While verbal memory is among the most compromised cognitive domains in schizophrenia (SZ), its neural substrates remain elusive. Here, we explored the structural and functional brain network correlates of verbal memory impairment in SZ. We acquired diffusion and resting-state functional MRI data of 49 SZ patients, classified as having preserved (VMP, n = 22) or impaired (VMI, n = 26) verbal memory based on the List Learning task, and 55 healthy controls (HC). Structural and functional connectivity matrices were obtained and analyzed to assess associations with disease status (SZ vs. HC) and verbal memory impairment (VMI vs. VMP) using two complementary data-driven approaches: threshold-free network-based statistics (TFNBS) and hybrid connectivity independent component analysis (connICA). TFNBS showed altered connectivity in SZ patients compared with HC (p < .05, FWER-corrected), with distributed structural changes and functional reorganization centered around sensorimotor areas. Specifically, functional connectivity was reduced within the visual and somatomotor networks and increased between visual areas and associative and subcortical regions. Only a tiny cluster of increased functional connectivity between visual and bilateral parietal attention-related areas correlated with verbal memory dysfunction. Hybrid connICA identified four robust traits, representing fundamental patterns of joint structural-functional connectivity. One of these, mainly capturing the functional connectivity profile of the visual network, was significantly associated with SZ (HC vs. SZ: Cohen's d = .828, p < .0001) and verbal memory impairment (VMP vs. VMI: Cohen's d = -.805, p = .01). We suggest that aberrant connectivity of sensorimotor networks may be a key connectomic signature of SZ and a putative biomarker of SZ-related verbal memory impairment, in consistency with bottom-up models of cognitive disruption.
MeSH term(s)	Humans ; Schizophrenia ; Connectome ; Magnetic Resonance Imaging ; Memory ; Brain ; Memory Disorders
Language	English
Publishing date	2023-02-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	1197207-5
ISSN	1097-0193 ; 1065-9471
ISSN (online)	1097-0193
ISSN	1065-9471
DOI	10.1002/hbm.26248
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Article ; Online: Residual effects of medications for sleep disorders on driving performance: A systematic review and network meta-analysis of randomized controlled trials: NMA driving and hypnotics.

Fornaro, Michele / Caiazza, Claudio / Rossano, Flavia / Cilmi, Flavia / De Prisco, Michele / Vieta, Eduard / Thompson, Trevor / Solmi, Marco / Carvalho, Andre Ferrer / Iasevoli, Felice / de Bartolomeis, Andrea

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology

2024 Volume 81, Page(s) 53–63

Abstract: Sleep medications often carry residual effects potentially affecting driving safety, warranting network meta-analysis (NMA). PubMed/EMBASE/TRID/Clinicaltrials.gov/WHO-ICTRP/WebOfScience were inquired for randomized controlled trials of hypnotic driving ... ...

Abstract	Sleep medications often carry residual effects potentially affecting driving safety, warranting network meta-analysis (NMA). PubMed/EMBASE/TRID/Clinicaltrials.gov/WHO-ICTRP/WebOfScience were inquired for randomized controlled trials of hypnotic driving studies in persons with insomnia and healthy subjects up to 05/28/2023, considering the vehicle's standard deviation of lateral position - SDLP (Standardized Mean Difference/SMD) and driving impairment rates on the first morning (co-primary outcomes) and endpoint. Risk-of-bias, global/local inconsistencies were measured, and CINeMA was used to assess the confidence in the evidence. Of 4,805 identified records, 26 cross-over RCTs were included in the systematic review, of which 22 entered the NMA, focusing on healthy subjects only. After a single administration, most molecules paralleled the placebo, outperforming zopiclone regarding SDLP. In contrast, ramelteon 8 mg, daridorexant 100 mg, zolpidem 10 mg bedtime, zolpidem middle-of-the-night 10 mg and 20 mg, mirtazapine 15-30 mg, and triazolam 0.5 mg performed significantly worse than placebo. Lemborexant 2.5-5 mg, suvorexant 15-20 mg, and zolpidem 3.5 mg middle-of-the-night associated with lower impairment than zopiclone. Repeated administration (maximum follow-up time of ten days) caused fewer residual effects than acute ones, except for flurazepam. Heterogeneity and inconsistency were negligible. Confidence in the evidence was low/very low. Sensitivity analyses confirmed the main analyses. Most FDA-approved hypnotics overlapped placebo at in-label doses, outperforming zopiclone. Repeated administration for 15 days or less reduced residual effects, warranting further research on the matter.
MeSH term(s)	Humans ; Hypnotics and Sedatives/adverse effects ; Zolpidem/adverse effects ; Network Meta-Analysis ; Automobile Driving ; Psychomotor Performance ; Randomized Controlled Trials as Topic ; Sleep Initiation and Maintenance Disorders/drug therapy ; Piperazines ; Azabicyclo Compounds
Chemical Substances	Hypnotics and Sedatives ; Zolpidem (7K383OQI23) ; zopiclone (03A5ORL08Q) ; Piperazines ; Azabicyclo Compounds
Language	English
Publishing date	2024-02-23
Publishing country	Netherlands
Document type	Meta-Analysis ; Systematic Review ; Journal Article
ZDB-ID	1082947-7
ISSN	1873-7862 ; 0924-977X
ISSN (online)	1873-7862
ISSN	0924-977X
DOI	10.1016/j.euroneuro.2024.01.011
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Article ; Online: Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning

Giordano D’Urso / Alfonso Magliacano / Sayna Rotbei / Felice Iasevoli / Andrea de Bartolomeis / Alessio Botta

Diagnostics, Vol 12, Iss 957, p

2022 Volume 957

Abstract	During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such as pandemics and wars, especially when these implicate an extended lockdown. The early detection of higher risk clinical and non-clinical individuals would help prevent the new onset and/or deterioration of these symptoms. This in turn would lead to the implementation of public policies aimed at protecting vulnerable populations during these dramatic contingencies, therefore optimising the effectiveness of interventions and saving the resources of national healthcare systems. We used a supervised machine learning method to identify the predictors of the severity of psychiatric symptoms during the Italian lockdown due to the COVID-19 pandemic. Via a case study, we applied this methodology to a small sample of healthy individuals, obsessive-compulsive disorder patients, and adjustment disorder patients. Our preliminary results show that our models were able to predict depression, anxiety, and obsessive-compulsive symptoms during the lockdown with up to 92% accuracy based on demographic and clinical characteristics collected before the pandemic. The presented methodology may be used to predict the psychiatric prognosis of individuals under a large-scale lockdown and thus supporting the related clinical decisions.
Keywords	machine learning ; COVID-19 ; prediction ; obsessive-compulsive disorder ; depression ; anxiety ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Pharmacotherapy to prevent the onset of depression following traumatic brain injury.

Fornaro, Michele / Trinchillo, Assunta / Saccà, Francesco / Iasevoli, Felice / Nolano, Maria / de Bartolomeis, Andrea

Expert opinion on pharmacotherapy

2021 Volume 23, Issue 2, Page(s) 255–262

Abstract: Introduction: Depressive symptoms may follow traumatic brain injury (TBI), affecting cognition, apathy, and overall general functioning. Pharmacotherapy to prevent the onset of depression following TBI is, therefore, crucial.: Areas covered: The ... ...

Abstract	Introduction: Depressive symptoms may follow traumatic brain injury (TBI), affecting cognition, apathy, and overall general functioning. Pharmacotherapy to prevent the onset of depression following TBI is, therefore, crucial. Areas covered: The present report critically appraises current pharmacotherapy to prevent the onset of depression following TBI as well as novel potential pharmacological avenues on the matter. Both efficacy and safety issues are considered, emphasizing an evidence-based approach whenever feasible. The authors further provide the reader with their expert opinion and future perspectives on the subject. Expert opinion: Despite its clinical burden and relatively frequent occurrence, the prophylaxis of post-TBI depression warrants further research. The current clinical guidelines of depression do not account for people with a primary diagnosis of TBI. Prospective cohort studies supported by proof-of-concept trials are nonetheless urged toward more effective, patient-tailored pharmacotherapy to prevent the onset of depression and treatment-resistance phenomena following TBI.
MeSH term(s)	Brain Injuries, Traumatic/complications ; Brain Injuries, Traumatic/drug therapy ; Cognition ; Depression/drug therapy ; Depression/etiology ; Depression/prevention & control ; Expert Testimony ; Humans ; Prospective Studies
Language	English
Publishing date	2021-09-22
Publishing country	England
Document type	Journal Article
ZDB-ID	2001535-5
ISSN	1744-7666 ; 1465-6566
ISSN (online)	1744-7666
ISSN	1465-6566
DOI	10.1080/14656566.2021.1980537
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