ABSTRACT
Diffusion neuroimaging has emerged as an essential non-invasive technique to explore in vivo microstructural characteristics of white matter (WM), whose integrity allows complex behaviors and cognitive abilities. Studying the factors contributing to inter-individual variability in WM microstructure can provide valuable insight into structural and functional differences of brain among individuals. Genetic influence on this variation has been largely investigated in twin studies employing different measures derived from diffusion neuroimaging. In this context, we performed a comprehensive literature search across PubMed, Scopus and Web of Science of original twin studies focused on the heritability of WM. Overall, our results highlighted a consistent heritability of diffusion indices (i.e., fractional anisotropy, mean, axial and radial diffusivity), and network topology among twins. The genetic influence resulted prominent in frontal and occipital regions, in the limbic system, and in commissural fibers. To enhance the understanding of genetic influence on WM microstructure further studies in less heterogeneous experimental settings, encompassing all diffusion indices, are warranted.
ABSTRACT
Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.
Subject(s)
Atlases as Topic , Brain , Magnetic Resonance Imaging , Neuroimaging , Female , Humans , Pregnancy , Brain/diagnostic imaging , Brain/embryology , Datasets as Topic , Fetal Development/physiology , Fetus/diagnostic imaging , Magnetic Resonance Imaging/methods , Neuroimaging/methodsABSTRACT
BACKGROUND: Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non-motor functioning remain to be elucidated. OBJECTIVE: To quantify cross-sectional regional cerebellar lobule volumes using three dimensional T1-weighted anatomical brain magnetic resonance imaging from the global ENIGMA-PD working group. METHODS: Cerebellar parcellation was performed using a deep learning-based approach from 2487 people with PD and 1212 age and sex-matched controls across 22 sites. Linear mixed effects models compared total and regional cerebellar volume in people with PD at each Hoehn and Yahr (HY) disease stage, to an age- and sex- matched control group. Associations with motor symptom severity and Montreal Cognitive Assessment scores were investigated. RESULTS: Overall, people with PD had a regionally smaller posterior lobe (dmax = -0.15). HY stage-specific analyses revealed a larger anterior lobule V bilaterally (dmax = 0.28) in people with PD in HY stage 1 compared to controls. In contrast, smaller bilateral lobule VII volume in the posterior lobe was observed in HY stages 3, 4, and 5 (dmax = -0.76), which was incrementally lower with higher disease stage. Within PD, cognitively impaired individuals had lower total cerebellar volume compared to cognitively normal individuals (d = -0.17). CONCLUSIONS: We provide evidence of a dissociation between anterior "motor" lobe and posterior "non-motor" lobe cerebellar regions in PD. Whereas less severe stages of the disease are associated with larger motor lobe regions, more severe stages of the disease are marked by smaller non-motor regions. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Subject(s)
Parkinson Disease , Humans , Parkinson Disease/complications , Cross-Sectional Studies , Magnetic Resonance Imaging , Cerebellum , BrainABSTRACT
Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images.
Subject(s)
Deep Learning , Female , Male , Humans , Fetus/diagnostic imaging , Magnetic Resonance Imaging , Algorithms , Brain/diagnostic imagingABSTRACT
Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.
Subject(s)
Imaging, Three-Dimensional , Magnetic Resonance Imaging , Female , Humans , Pregnancy , Pregnancy Trimester, Second , Imaging, Three-Dimensional/methods , Reproducibility of Results , Magnetic Resonance Imaging/methods , Brain/diagnostic imagingABSTRACT
BACKGROUND: The recent widespread use of diffusion tensor imaging (DTI) tractography allowed researchers to investigate the diffusivity modifications and neuroanatomical changes of white matter (WM) fascicles in major psychiatric disorders, including bipolar disorder (BD). In BD, corpus callosum (CC) seems to have a crucial role in explaining the pathophysiology and cognitive impairment of this psychiatric disorder. This review aims to provide an overview on the latest results emerging from studies that investigated neuroanatomical changes of CC in BD using DTI tractography. METHODS: Bibliographic research was conducted on PubMed, Scopus and Web of Science datasets until March 2022. Ten studies fulfilled our inclusion criteria. RESULTS: From the reviewed DTI tractography studies a significant decrease of fractional anisotropy emerged in the genu, body and splenium of CC of BD patients compared to controls. This finding is coupled with reduction of fiber density and modification in fiber tract length. Finally, an increase of radial and mean diffusivity in forceps minor and in the entire CC was also reported. LIMITATIONS: Small sample size, heterogeneity in terms of methodological (diffusion gradient) and clinical (lifetime comorbidity, BD status, pharmacological treatments) characteristics. CONCLUSIONS: Overall, these findings suggest the presence of structural modifications in CC in BD patients, which may in turn explain the cognitive impairments often observed in this psychiatric disorder, especially in executive processing, motor control and visual memory. Finally, structural modifications may suggest an impairment in the amount of functional information and a morphological impact within those brain regions connected by CC.
Subject(s)
Bipolar Disorder , White Matter , Humans , Bipolar Disorder/diagnostic imaging , Diffusion Tensor Imaging/methods , White Matter/diagnostic imaging , Corpus Callosum/diagnostic imaging , Brain , AnisotropyABSTRACT
Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.
Subject(s)
Brain , Psychotic Disorders , Humans , Brain/metabolism , Inflammation , Psychotic Disorders/pathology , Biomarkers , Machine LearningABSTRACT
Major Depressive Disorder (MDD) is a severe psychiatric disorder characterized by selective impairments in mood regulation, cognition and behavior. Although it is well-known that antidepressants can effectively treat moderate to severe depression, the biochemical effects of these medications on white matter (WM) integrity are still unclear. Therefore, the aim of the study is to review the main scientific evidence on the differences in WM integrity in responders and non-responders to antidepressant medications. A record search was performed on three datasets (PubMed, Scopus and Web of Science) and ten records matched our inclusion criteria. Overall, the reviewed studies highlighted a good efficacy of antidepressants in MDD treatment. Furthermore, there were differences in WM integrity between responders and non-responders, mainly localized in cingulate cortices, hippocampus and corpus callosum, where the former group showed higher fractional anisotropy and lower axial diffusivity values. Modifications in WM integrity might be partially explained by branching and proliferation as well as neurogenesis of axonal fibers mediated by antidepressants, which in turn may have positively affected brain metabolism and increase the quantity of the serotonergic neurotransmitter within synaptic clefts. However, the reviewed studies suffer from some limitations, including the heterogeneity in treatment duration, antidepressant administration, medical posology, and psychiatric comorbidities. Therefore, future studies are needed to reduce confounding effects of antidepressant medications and to adopt longitudinal and multimodal approaches in order to better characterize the differences in WM integrity between responders and non-responders.
ABSTRACT
Treatment-resistant depression (TRD) is a severe disorder characterized by high relapse rates and decreased quality of life. An effective strategy in the management of TRD is deep brain stimulation (DBS), a technique consisting of the implantation of electrodes that receive a stimulation via a pacemaker-like stimulator into specific brain areas, detected through neuroimaging investigations, which include the subgenual cingulate cortex (sgCC), basal ganglia, and forebrain bundles. In this context, to improve our understanding of the mechanism underlying the antidepressant effects of DBS in TRD, we collected the results of diffusion tensor imaging (DTI) studies exploring how WM microstructure is associated with the therapeutic effects of DBS in TRD. A search on PubMed, Web of Science, and Scopus identified 11 investigations assessing WM microstructure in responders and non-responders to DBS. Altered WM microstructure, particularly in the sgCC, medial forebrain bundle, cingulum bundle, forceps minor, and uncinate fasciculus, was associated with the antidepressant effect of DBS in TRD. Overall, the results show that DBS targeting selective brain regions, including the sgCC, forebrain bundle, cingulum bundle, rectus gyrus, anterior limb of the internal capsule, forceps minor, and uncinate fasciculus, seem to be effective for the treatment of TRD.
Subject(s)
Deep Brain Stimulation , Depressive Disorder, Treatment-Resistant , White Matter , Humans , Diffusion Tensor Imaging/methods , White Matter/diagnostic imaging , Deep Brain Stimulation/methods , Quality of Life , Depressive Disorder, Treatment-Resistant/diagnostic imaging , Depressive Disorder, Treatment-Resistant/therapy , Antidepressive Agents/therapeutic useABSTRACT
Major Depressive Disorder (MDD) and Bipolar Disorder Depression (BDD) are common psychiatric illnesses characterized by structural and functional brain alterations and signs of neuroinflammation. In line with the neuroinflammatory pathogenesis of depressive syndromes, recent studies have demonstrated how white matter (WM) microstructural impairments detected by Diffusion Tensor Imaging, are correlated to peripheral immunomarkers in depressed patients. In this context, we performed a comprehensive systematic search on PubMed, Medline and Scopus of the original studies published till June 2022, exploring the association between immunomarkers and WM alteration patterns in patients affected by MDD or BDD. Overall, the studies included in this review showed a consistent association between blood proinflammatory and counter-regulatory immunomarkers, including regulatory T cells and natural killer cells markers, as well as measures of demyelination and dysmyelination in both MDD and BDD patients. These pathogenetic insights could outline an integrated clinical perspective to affective disorders, helping psychiatrists to develop novel biotype-to-phenotype models of depression and opening the way to tailored approaches in treatments.
Subject(s)
Bipolar Disorder , Depressive Disorder, Major , White Matter , Humans , Bipolar Disorder/pathology , Depressive Disorder, Major/diagnostic imaging , Depressive Disorder, Major/pathology , Diffusion Tensor Imaging/methods , Inflammation/pathology , White Matter/pathologyABSTRACT
BACKGROUND: Bipolar Disorder (BD) is a severe chronic psychiatric disorder whose aetiology is still largely unknown. However, increasing literature reported the involvement of neurovascular factors in the pathophysiology of BD, suggesting that a measure of Cerebral Blood Flow (CBF) could be an important biomarker of the illness. Therefore, since, to date, Magnetic Resonance Perfusion Weighted Imaging (PWI) techniques, such as Dynamic Susceptibility Contrast (DSC) and Arterial Spin Labelling (ASL), are the most common approaches that allow non-invasive in-vivo perfusion measurements,this review aims to summarize the results from all PWI studies that evaluated the CBF in BD. METHODS: A bibliographic search in PubMed up until May 2021 was performed. 16 PWI studies that used DSC or ASL sequences met our inclusion criteria. RESULTS: Overall, the results supported the presence of hyper-perfusion in the cingulate cortex and fronto-temporal regions, as well as the presence of hypo-perfusion in the cerebellum in BD, compared with both healthy controls and patients with unipolar depression. CBF changes after cognitive and aerobic training, as well as in relation with other physiological, clinical, and neurocognitive variables were also reported. LIMITATIONS: The heterogeneity across the studies, in terms of experimental designs, sample selection, and methodological approach employed, limited the studies' comparison. CONCLUSIONS: These findings showed CBF alterations in the cingulate cortex, fronto-temporal regions, and cerebellum in BD, suggesting that CBF may be an important pathophysiological marker of BD that merits further investigation to clarify the extent of neurovascular alterations.
Subject(s)
Bipolar Disorder , Bipolar Disorder/diagnostic imaging , Cerebrovascular Circulation/physiology , Humans , Magnetic Resonance Angiography/methods , Magnetic Resonance Imaging/methods , Perfusion , Perfusion Imaging , Spin LabelsABSTRACT
OBJECTIVE: Psychotic disorders are frequently associated with decline in functioning and cognitive difficulties are observed in subjects at clinical high risk (CHR) for psychosis. In this work, we applied automatic approaches to neurocognitive and functioning measures, with the aim of investigating the link between global, social and occupational functioning, and cognition. METHODS: 102 CHR subjects and 110 patients with recent onset depression (ROD) were recruited. Global assessment of functioning (GAF) related to symptoms (GAF-S) and disability (GAF-D). and global functioning social (GF-S) and role (GF-R), at baseline and of the previous month and year, and a set of neurocognitive measures, were used for classification and regression. RESULTS: Neurocognitive measures related to GF-R at baseline (r = 0.20, p = 0.004), GF-S at present (r = 0.14, p = 0.042) and of the past year (r = 0.19, p = 0.005), for GAF-F of the past month (r = 0.24, p < 0.001) and GAF-D of the past year (r = 0.28, p = 0.002). Classification reached values of balanced accuracy of 61% for GF-R and GAF-D. CONCLUSION: We found that neurocognition was related to psychosocial functioning. More specifically, a deficit in executive functions was associated to poor social and occupational functioning.
Subject(s)
Cognition Disorders , Psychotic Disorders , Humans , Psychiatric Status Rating Scales , Depression , Neuropsychological Tests , Psychotic Disorders/diagnosis , Cognition Disorders/psychologyABSTRACT
BACKGROUND: Brain structure abnormalities throughout the course of Parkinson's disease have yet to be fully elucidated. OBJECTIVE: Using a multicenter approach and harmonized analysis methods, we aimed to shed light on Parkinson's disease stage-specific profiles of pathology, as suggested by in vivo neuroimaging. METHODS: Individual brain MRI and clinical data from 2357 Parkinson's disease patients and 1182 healthy controls were collected from 19 sources. We analyzed regional cortical thickness, cortical surface area, and subcortical volume using mixed-effects models. Patients grouped according to Hoehn and Yahr stage were compared with age- and sex-matched controls. Within the patient sample, we investigated associations with Montreal Cognitive Assessment score. RESULTS: Overall, patients showed a thinner cortex in 38 of 68 regions compared with controls (dmax = -0.20, dmin = -0.09). The bilateral putamen (dleft = -0.14, dright = -0.14) and left amygdala (d = -0.13) were smaller in patients, whereas the left thalamus was larger (d = 0.13). Analysis of staging demonstrated an initial presentation of thinner occipital, parietal, and temporal cortices, extending toward rostrally located cortical regions with increased disease severity. From stage 2 and onward, the bilateral putamen and amygdala were consistently smaller with larger differences denoting each increment. Poorer cognition was associated with widespread cortical thinning and lower volumes of core limbic structures. CONCLUSIONS: Our findings offer robust and novel imaging signatures that are generally incremental across but in certain regions specific to disease stages. Our findings highlight the importance of adequately powered multicenter collaborations. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Subject(s)
Parkinson Disease , Brain/diagnostic imaging , Brain/pathology , Humans , Magnetic Resonance Imaging , Neuroimaging , Parkinson Disease/complications , Thalamus/pathologyABSTRACT
OBJECTIVE: Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects. METHODS: A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1-MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a "learning by example" procedure; the features with best performance was then selected by "greedy forward-feature selection." Finally, this model underwent a leave-one-out cross-validation approach. RESULTS: From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process. CONCLUSION: We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required.
Subject(s)
Autism Spectrum Disorder , Support Vector Machine , Autism Spectrum Disorder/diagnostic imaging , Brain , Brain Mapping , Child , Humans , Magnetic Resonance ImagingABSTRACT
BACKGROUND: Lithium is one of the most effective medications for bipolar disorder episode prevention, but its mechanism of action is still largely unknown. The hippocampus is a subcortical cerebral structure involved in the formation of emotional responses, cognition and various primitive functions, altered during affective episodes. Deviations in the anatomy or physiology of the hippocampus would partially explain the symptomatology of bipolar subjects, and restoration may reflect treatment response. METHODS: In this mini review, we summarize the studies which have investigated the effect of lithium intake on the volume of hippocampus, measured using magnetic resonance imaging (MRI). We performed a bibliographic search on PubMed, using the terms terms "hippocampus", "lithium", "bipolar disorder", "volume" and "MRI". Only original studies were considered. RESULTS: Thirteen studies met the inclusion criteria. Nine studies demonstrated increased total hippocampal volume or hippocampal subfield volumes in BD patients on lithium treatment (Li BD) compared to those not taking lithium (non-Li BD), while four failed to show significant differences between groups. When healthy controls were compared to either the Li subjects or the non-Li ones, the findings were more heterogeneous. LIMITATIONS: Heterogeneity in the methodology and definition of groups limits the comparison of study results. CONCLUSIONS: Lithium may be associated with increased hippocampal volume in BD, potentially due to its putative neurotrophic action, but further research is needed better define the morphological alterations of hippocampus in BD and the longitudinal effects of lithium in the short and long-term.
Subject(s)
Bipolar Disorder , Antimanic Agents/therapeutic use , Bipolar Disorder/diagnostic imaging , Bipolar Disorder/drug therapy , Hippocampus/diagnostic imaging , Humans , Lithium/therapeutic use , Lithium Compounds/therapeutic use , Magnetic Resonance ImagingABSTRACT
BACKGROUND: Patterns of coordinated variations of gray matter (GM) morphology across individuals are promising indicators of disease. However, it remains unclear if they can help characterize first-episode psychosis (FEP) and symptoms' severity. METHODS: Sixty-seven FEP and 67 matched healthy controls (HC) were assessed with structural MRI to evaluate the existence of distributed GM structural covariance patterns associated to brain areas belonging to salience network. Voxel-based morphometry (VBM) and structural covariance differences, investigated with salience network seed-based Partial Least Square, were applied to explore differences between groups. GM density associations with Raven's intelligent quotient (IQ) and Positive and Negative Syndrome Scale (PANSS) scores were investigated. RESULTS: Univariate VBM results gave trend without significant GM differences across groups. GM and IQ correlated positively in both groups: in FEP, mostly in hippocampus, insula, and fronto-temporal structures, while in HC mostly in amygdala, thalamus and fronto-temporal regions. GM and PANSS scores correlated negatively in FEP, with widespread clusters located in limbic regions. Multivariate analysis showed strong and opposite structural GM covariance with salience network for FEP and HC. Moreover, structural covariance of the salience network in FEP correlated negatively with severity of clinical symptoms. CONCLUSION: Our study provides evidence supporting the insular dysfunction model of psychosis. Reduced structural GM covariance of the salience network, with its association to symptom's severity, appears a promising morphometry feature for FEP detection.
Subject(s)
Psychotic Disorders , Brain/diagnostic imaging , Cerebral Cortex/diagnostic imaging , Gray Matter/diagnostic imaging , Humans , Magnetic Resonance Imaging , Psychotic Disorders/diagnostic imagingABSTRACT
BACKGROUND: Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct therapy on the basis of patient-specific biomarker may be a considerable step towards personalized psychiatry. Machine learning methods are gaining increasing popularity in the medical field. Once trained, the possibility to consider single patients in the analyses instead of whole groups makes them particularly appealing to investigate treatment response. Deep learning, a branch of machine learning, lately gained attention, due to its effectiveness in dealing with large neuroimaging data and to integrate them with clinical, molecular or -omics biomarkers. METHODS: In this mini-review, we summarize studies that use deep learning methods to predict response to treatment in depression. We performed a bibliographic search on PUBMED, Google Scholar and Web of Science using the terms "psychiatry", "mood disorder", "depression", "treatment", "deep learning", "neural networks". Only studies considering patients' datasets are considered. RESULTS: Eight studies met the inclusion criteria. Accuracies in prediction of response to therapy were considerably high in all studies, but results may be not easy to interpret. LIMITATIONS: The major limitation for the current studies is the small sample size, which constitutes an issue for machine learning methods. CONCLUSIONS: Deep learning shows promising results in terms of prediction of treatment response, often outperforming regression methods and reaching accuracies of around 80%. This could be of great help towards personalized medicine. However, more efforts are needed in terms of increasing datasets size and improved interpretability of results.
Subject(s)
Deep Learning , Psychiatry , Depression , Humans , Machine Learning , Neural Networks, ComputerABSTRACT
Gender differences in mood and anxiety disorders are well-established. However, the neural basis of these differences is not clear yet, especially in terms of brain metabolism. Indeed, although several proton Magnetic Resonance Spectroscopy (¹H MRS) investigations reported different metabolic levels in both depression and anxiety disorders, which have been also linked to symptoms severity and response to treatment, the role of gender on these differences have not been explored yet. Therefore, this study aims at investigating the role of sex in neurometabolic alterations associated with both mood and anxiety disorders. A 3T single-voxel ¹H MRS acquisition of the dorsolateral prefrontal cortex was acquired from 14 Major Depressive Disorder, 10 Generalized Anxiety Disorder (GAD), 11 Panic Disorder (PD), patients and 16 healthy controls (HC). Among males, PD patients showed significantly lower GPC+PC (also observed in GAD+PD) and Glu levels compared to HC. Finally, a significant group x sex interaction effect was observed in the GPC+PC and Glu levels. We proved the presence of an association between sex and brain metabolites in anxiety spectrum.
Subject(s)
Depressive Disorder, Major , Mood Disorders , Anxiety , Anxiety Disorders/pathology , Brain/pathology , Depressive Disorder, Major/diagnostic imaging , Depressive Disorder, Major/metabolism , Female , Humans , Male , Mood Disorders/diagnostic imaging , Sex CharacteristicsABSTRACT
Objectives: To determine the effectiveness of an early intervention program in enhancing visual function in very preterm infants. Methods: We conducted a RCT. We included preterm infants born between 25+0 and 29+6 weeks of gestational age (GA), without severe morbidities, and their families. Infants were randomized to either receive Standard Care (SC) or Early Intervention (EI). SC, according to NICU protocols, included Kangaroo Mother Care and minimal handling. EI included, in addition to routine care, parental training according to the PremieStart program, and multisensory stimulation (infant massage and visual interaction) performed by parents. Visual function was assessed at term equivalent age (TEA) using a prevalidated battery evaluating ocular spontaneous motility, ability to fix and follow a target, reaction to color, stripes discrimination and visual attention at distance. Results: Seventy preterm (EI n = 34, SC n = 36) infants were enrolled. Thirteen were excluded according to protocol. Fifty-seven infants (EI = 27, SC = 30) were assessed at TEA. The two groups were comparable for parental and infant characteristics. In total, 59% of infants in the EI group achieved the highest score in all the nine assessed items compared to 17% in the SC group (p = 0.001): all infants in both groups showed complete maturation in four items, but EI infants showed more mature findings in the other five items (ocular motility both spontaneous and with target, tracking arc, stripes discrimination and attention at distance). Conclusions: Our results suggest that EI has a positive effect on visual function maturation in preterm infants at TEA. Trial Registration: clinicalTrial.gov (NCT02983513).