The eye of nuclear medicine

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The relationship between maternal-fetus attachment and perceived parental bonds in pregnant women: Considering a possible mediating role of psychological distress

Maternal-Fetal Attachment (MFA) delineates the emotional, cognitive, and behavioral aspects that mothers develop toward the unborn baby during pregnancy. The literature indicates that optimal attachment in pregnancy represents a protective factor for the mother-child attachment bond after birth and child development outcomes. To date, there are few studies that have investigated associated factors of MFA. This study sets out to explore the association between perceived parental bonds and maternal-fetal bonding in pregnant women, accounting for factors such as psychological distress, socio-demographic and obstetric characteristics.MethodsIn this cross-sectional study, 1,177 pregnant women answered the Parental Bonding Instrument, the Maternal-Fetal Attachment Scale, State-Trait Anxiety Inventory (STAI-Y), and Beck-Depression Inventory (BDI-II).ResultsWe found out that perceived maternal and paternal care had significant direct effects on maternal-infant bonding during the pregnancy period when controlling for some confounders, including gestational age and mother age among others. Such maternal and paternal perceived care effects were not mediated by levels of psychological distress, which in turn resulted to be a “borderline” significant predictor of prenatal attachment. Interestingly, the gestational age and the mother age emerged to have a significant and synergic nonlinear effect, suggesting the influence on the MFAS of the gestational age depends on the values of the mother age, and likewise, the effect of mother age on MFAS depends on the gestational week.ConclusionThis study expands our knowledge of the intergenerational transmission of attachment pointing out the effects of a woman’s perceived bond in relation to her parental figures during the development of the prenatal attachment process. Findings also suggests that parenting support interventions may have benefits that are realized across generations

MRI Asymmetry Index of Hippocampal Subfields Increases Through the Continuum From the Mild Cognitive Impairment to the Alzheimer's Disease

Objective: It is well-known that the hippocampus presents significant asymmetry in Alzheimer's disease (AD) and that difference in volumes between left and right exists and varies with disease progression. However, few works investigated whether the asymmetry degree of subfields of hippocampus changes through the continuum from Mild Cognitive Impairment (MCI) to AD. Thus, aim of the present work was to evaluate the Asymmetry Index (AI) of hippocampal substructures as possible MRI biomarkers of Dementia. Moreover, we aimed to assess whether the subfields presented peculiar differences between left and right hemispheres. We also investigated the relationship between the asymmetry magnitude in hippocampal subfields and the decline of verbal memory as assessed by Rey's auditory verbal learning test (RAVLT).Methods: Four-hundred subjects were selected from ADNI, equally divided into healthy controls (HC), AD, stable MCI (sMCI), and progressive MCI (pMCI). The structural baseline T1s were processed with FreeSurfer 6.0 and volumes of whole hippocampus (WH) and 12 subfields were extracted. The AI was calculated as: (|Left-Right|/(Left+Right))*100. ANCOVA was used for evaluating AI differences between diagnoses, while paired t-test was applied for assessing changes between left and right volumes, separately for each group. Partial correlation was performed for exploring relationship between RAVLT summary scores (Immediate, Learning, Forgetting, Percent Forgetting) and hippocampal substructures AI. The statistical threshold was Bonferroni corrected p < 0.05/13 = 0.0038.Results: We found a general trend of increased degree of asymmetry with increasing severity of diagnosis. Indeed, AD presented the higher magnitude of asymmetry compared with HC, sMCI and pMCI, in the WH (AI mean 5.13 ± 4.29 SD) and in each of its twelve subfields. Moreover, we found in AD a significant negative correlation (r = −0.33, p = 0.00065) between the AI of parasubiculum (mean 12.70 ± 9.59 SD) and the RAVLT Learning score (mean 1.70 ± 1.62 SD).Conclusions: Our findings showed that hippocampal subfields AI varies differently among the four groups HC, sMCI, pMCI, and AD. Moreover, we found—for the first time—that hippocampal substructures had different sub-patterns of lateralization compared with the whole hippocampus. Importantly, the severity in learning rate was correlated with pathological high degree of asymmetry in parasubiculum of AD patients

Diagnostic Developments in Differentiating Unresponsive Wakefulness Syndrome and the Minimally Conscious State

When treating patients with a disorder of consciousness (DOC), it is essential to obtain an accurate diagnosis as soon as possible to generate individualized treatment programs. However, accurately diagnosing patients with DOCs is challenging and prone to errors when differentiating patients in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) from those in a Minimally Conscious State (MCS). Upwards of ~40% of patients with a DOC can be misdiagnosed when specifically designed behavioral scales are not employed or improperly administered. To improve diagnostic accuracy for these patients, several important neuroimaging and electrophysiological technologies have been proposed. These include Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Transcranial Magnetic Stimulation (TMS). Here, we review the different ways in which these techniques can improve diagnostic differentiation between VS/UWS and MCS patients. We do so by referring to studies that were conducted within the last 10 years, which were extracted from the PubMed database. In total, 55 studies met our criteria (clinical diagnoses of VS/UWS from MCS as made by PET, fMRI, EEG and TMS- EEG tools) and were included in this review. By summarizing the promising results achieved in understanding and diagnosing these conditions, we aim to emphasize the need for more such tools to be incorporated in standard clinical practice, as well as the importance of data sharing to incentivize the community to meet these goals

The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures

Abstract Psychogenic nonepileptic seizures (PNES) are episodes of paroxysmal impairment associated with a range of motor, sensory, and mental manifestations, which perfectly mimic epileptic seizures. Several patterns of neural abnormalities have been described without identifying a definite neurobiological substrate. In this multicenter cross-sectional study, we applied a multivariate classification algorithm on morphological brain imaging metrics to extract reliable biomarkers useful to distinguish patients from controls at an individual level. Twenty-three patients with PNES and 21 demographically matched healthy controls (HC) underwent an extensive neuropsychiatric/neuropsychological and neuroimaging assessment. One hundred and fifty morphological brain metrics were used for training a random forest (RF) machine-learning (ML) algorithm. A typical complex psychopathological construct was observed in PNES. Similarly, univariate neuroimaging analysis revealed widespread neuroanatomical changes affecting patients with PNES. Machine-learning approach, after feature selection, was able to perform an individual classification of PNES from controls with a mean accuracy of 74.5%, revealing that brain regions influencing classification accuracy were mainly localized within the limbic (posterior cingulate and insula) and motor inhibition systems (the right inferior frontal cortex (IFC)). This study provides Class II evidence that the considerable clinical and neurobiological heterogeneity observed in individuals with PNES might be overcome by ML algorithms trained on surface-based magnetic resonance imaging (MRI) data

Exoskeleton-Robot Assisted Therapy in Stroke Patients: A Lesion Mapping Study

Background: Technology-supported rehabilitation is emerging as a solution to support therapists in providing a high-intensity, repetitive and task-specific treatment, aimed at improving stroke recovery. End-effector robotic devices are known to positively affect the recovery of arm functions, however there is a lack of evidence regarding exoskeletons. This paper evaluates the impact of cerebral lesion load on the response to a validated robotic-assisted rehabilitation protocol.Methods: Fourteen hemiparetic patients were assessed in a within-subject design (age 66.9 ± 11.3 years; 10 men and 4 women). Patients, in post-acute phase, underwent 7 weeks of bilateral arm training assisted by an exoskeleton robot combined with a conventional treatment (consisting of simple physical activity together with occupational therapy). Clinical and neuroimaging evaluations were performed immediately before and after rehabilitation treatments. Fugl-Meyer (FM) and Motricity Index (MI) were selected to measure primary outcomes, i.e., motor function and strength. Functional independance measure (FIM) and Barthel Index were selected to measure secondary outcomes, i.e., daily living activities. Voxel-based lesion symptom mapping (VLSM) was used to determine the degree of cerebral lesions associated with motor recovery.Results: Robot-assisted rehabilitation was effective in improving upper limb motor function recovery, considering both primary and secondary outcomes. VLSM detected that lesion load in the superior region of the corona radiata, internal capsule and putamen were significantly associated with recovery of the upper limb as defined by the FM scores (p-level < 0.01).Conclusions: The probability of functional recovery from stroke by means of exoskeleton robotic rehabilitation relies on the integrity of specific subcortical regions involved in the primary motor pathway. This is consistent with previous evidence obtained with conventional neurorehabilitation approaches

Information and communication technologies-based interventions for children with autism spectrum conditions: a systematic review of randomized control trials from a positive technology perspective

Information and communication technologies (ICTs) have become more widely used in the past years to help people with autism spectrum conditions (ASC). Serious games embedded into computers or tablets, as well as social robots, are the most employed ICT-related tools that are appealing to and appropriate for autistic children. The goal of ICT applications is to enhance behavioral abnormalities associated with ASC while also creating an interactive link between one person and one computer. Comparatively, to human-based therapy, ICT tools aid to inspire autistic children by providing predictability and regularity of tasks. Regaining social skills is the primary behavioral goal for which ICT tools have been designed and implemented. In the past several years, many studies have been created to show how effective it is at improving targeted behaviors. However, only a small number of researchers have used an RCT approach to evaluate its effectiveness. In this systematic review, we only included RCT studies where ICT technologies were used to help children with ASC in improving their social skills. Only 14 RCT studies satisfied the criteria and 12 described significant improvements, showing how the use of technology in educational contexts produced better improvement in developing several social skill facets with respect to the traditional face-to-face approach. Some studies used interventions and outcome measures focused on the core ASC symptoms, but many others addressed neurocognitive functions directly, like social cognition or emotional regulation, while other more general functions such as language or adaptive behaviors. We propose a classification based on processes and outcome measures to foster future research in this specific area of research. The behavioral intervention mediated by technological tools such as computer-based, tablet, and social robotics, undoubtedly provides a comfortable environment that promotes constant learning for people with ASC. Evidence provided in this review highlights the translational potential of this field of study in primary care practice and educational settings

Dataset of pahs determined in home-made honey samples collected in central Italy by means of dllme-gc-ms and cluster analysis for studying the source apportionment

This paper would like to show all the data related to an intensive field campaign focused on the characterization of the Polyaromatic Hydrocarbons (PAHs) composition profile in almost 60 honey samples collected in Central Italy. The analytical data here reported are the base for a study aimed to identify the pollution sources in a region. 22 PAHs were analyzed by means of ultrasound-vortex-assisted dispersive liquid-liquid micro-extraction (DLLME) procedure followed by a triple quadrupole gas chromatograph/mass spectrometer (GC-MS). A chemometrics approach has been carried out for evaluating all the data: in particular, principal component analysis and cluster analysis has been used both for the identification of the main natural/anthropogenic pollutants affecting a site and for evaluating the air quality

Design, synthesis and preliminary pharmacological evaluation of new imidazolinonesas L-DOPA prodrugs

L-DOPA, the immediate biological precursor of dopamine, is still considered the drug of choice in the treatment of Parkinson's disease. However, therapy with L-DOPA is associated with a number of acute problems. With the aim to increase the bioavailability after oral administration, we designed a multi-protected L-DOPA prodrugs able to release the drug by both spontaneous chemical or enzyme catalyzed hydrolysis. The new compounds have been synthesized and preliminarily evaluated for their water solubility, log P, chemical stability, and enzymatic stability. The results indicate that the incorporation of the amino acidic moiety of L-DOPA into an imidazoline-4-one ring provides prodrugs sufficiently stable to potentially cross unchanged the acidic environment of the stomach, and to be absorbed from the intestine. They also might be able to release L-DOPA in human plasma after enzymatic hydrolysis. The ability of prodrugs 6a-b to increase basal levels of striatal DA, and influence brain neurochemistry associated with dopaminergic activity following oral administration, as well as the radical-scavenging activity against DPPH for compounds 6a-b and 15a are also reported