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BackgroundPeople in late adulthood die by suicide at the highest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age. Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHARE) prospective dataset to train and test a machine learning model to identify predictors for suicide in late life.MethodsOf more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ± 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on demographic data, physical health, depression, and cognitive functioning to extract essential variables for predicting death from suicide and then tested on the test set.ResultsThe random forest algorithm had an accuracy of 79% (95% CI 0.60-0.92, p = 0.002), a sensitivity of.80, and a specificity of.78. Among the variables contributing to the model performance, the three most important factors were how long the participant was ill before death, the frequency of contact with the next of kin and the number of offspring still alive.ConclusionsProspective clinical and social information can predict death from suicide with good accuracy in late adulthood. Most of the variables that surfaced as risk factors can be attributed to the construct of social connectedness, which has been shown to play a decisive role in suicide in late life.

ObjectiveBipolar Disorder (BD) is a severe mental illness associated with high rates of general medical comorbidity, reduced life expectancy, and premature mortality. Although BD has been associated with high medical hospitalization, the factors that contribute to this risk remain largely unexplored. We used baseline medical and psychiatric records to develop a supervised machine learning model to predict general medical admissions after discharge from psychiatric hospitalization.MethodsIn this retrospective three-year cohort study of 71 patients diagnosed with BD (mean age=52.19 years, females=56.33%), lasso regression models combining medical and psychiatric records, as well as those using them separately, were fitted and their predictive power was estimated using a leave-one-out cross-validation procedure.ResultsThe proportion of medical admissions in patients with BD was higher compared with age- and sex-matched hospitalizations in the same region (25.4% vs. 8.48%). The lasso model fairly accurately predicted the outcome (area under the curve [AUC]=69.5%, 95%C.I.=55–84.1; sensitivity=61.1%, specificity=75.5%, balanced accuracy=68.3%). Notably, pre-existing cardiovascular, neurological, or osteomuscular diseases collectively accounted for more than 90% of the influence on the model. The accuracy of the model based on medical records was slightly inferior (AUC=68.7%, 95%C.I. = 54.6-82.9), while that of the model based on psychiatric records only was below chance (AUC=61.8%, 95%C.I.=46.2–77.4).ConclusionOur findings support the need to monitor medical comorbidities during clinical decision-making to tailor and implement effective preventive measures in people with BD. Further research with larger sample sizes and prospective cohorts is warranted to replicate these findings and validate the predictive model.

Abstract Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.

BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.

Here, Prakasam and colleagues show that polyQ-expanded androgen receptor toxicity can be attenuated using artificial miRNAs targeting Lsd1 and Prmt6, two AR-co-activators overexpressed in an androgen-dependent manner specifically in skeletal muscle, thus ameliorating spinal-bulbar muscular atrophy phenotypes in flies and mice.

Abstract Background and objectives Excessive smartphone use, also referred to as “smartphone addiction” (SPA), has increasingly attracted neuroscientific interest due to its similarities with other behavioral addictions, particularly internet gaming disorder. Little is known about the neural mechanisms underlying smartphone addiction. We explored interrelationships between brain structure and function to specify neurobiological correlates of SPA on a neural system level. Methods Gray matter volume (GMV) and intrinsic neural activity (INA) were investigated in individuals with SPA (n = 20) and controls (n = 24), using multimodal magnetic resonance imaging and multivariate data fusion techniques, that is, parallel independent component analysis. Results The joint analysis of both data modalities explored shared information between GMV and INA. In particular, two amplitudes of low frequency fluctuations‐based independent neural systems significantly differed between individuals with SPA and controls. A medial/dorsolateral prefrontal system exhibited lower functional network strength in individuals with SPA versus controls, whereas the opposite pattern was detected in a parietal cortical/cerebellar system. Neural network strength was significantly related to duration of smartphone use and sleep difficulties. Discussion and conclusions We show modality‐specific associations of the brain's resting‐state activity with distinct and shared SPA symptom dimensions. In particular, the data suggest contributions of aberrant prefrontal and parietal neural network strength as a possible signature of deficient executive control in SPA. Scientific significance This study suggests distinct neural mechanisms underlying specific biological and behavioral dimensions of excessive smartphone use.

Abstract Postpartum psychosis (PP) is a severe mental disorder that affects women in the first few weeks after delivery. To date there are no biomarkers that distinguish which women at risk (AR) develop a significant psychiatric relapse postpartum. While altered brain connectivity may contribute to the risk for psychoses unrelated to the puerperium, this remains unexplored in PP. We followed up 32 AR and 27 healthy (HC) women from pregnancy to 8-week postpartum. At this point, we classified women as AR-unwell (n = 15) if they had developed a psychiatric relapse meeting DSM-IV diagnostic criteria, or impacting on daily functioning and requiring treatment, or AR-well (n = 17) if they remained asymptomatic. Women also underwent an fMRI scan at rest and during an emotional-processing task, to study within- and between-networks functional connectivity. Women AR, and specifically those in the AR-well group, showed increased resting connectivity within an executive network compared to HC. During the execution of the emotional task, women AR also showed decreased connectivity in the executive network, and altered emotional load-dependent connectivity between executive, salience, and default-mode networks. AR-unwell women particularly showed increased salience network-dependent modulation of the default-mode and executive network relative to AR-well, who showed greater executive network-dependent modulation of the salience network. Our finding that the executive network and its interplay with other brain networks implicated in goal-directed behavior are intrinsically altered suggest that they could be considered neural phenotypes for postpartum psychosis and help advance our understanding of the pathophysiology of this disorder.

Transcranial magnetic stimulation (TMS) has become a promising strategy for bipolar disorder (BD). This study reviews neuroimaging findings, indicating functional, structural, and metabolic brain changes associated with TMS in BD. Web of Science, Embase, Medline, and Google Scholar were searched without any restrictions for studies investigating neuroimaging biomarkers, through structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and single photon emission computed tomography (SPECT), in association with response to TMS in patients with BD. Eleven studies were included (fMRI = 4, MRI = 1, PET = 3, SPECT = 2, and MRS = 1). Important fMRI predictors of response to repetitive TMS (rTMS) included higher connectivity of emotion regulation and executive control regions. Prominent MRI predictors included lower ventromedial prefrontal cortex connectivity and lower superior frontal and caudal middle frontal volumes. SPECT studies found hypoconnectivity of the uncus/parahippocampal cortex and right thalamus in non-responders. The post-rTMS changes using fMRI mostly showed increased connectivity among the areas neighboring the coil. Increased blood perfusion was reported post-rTMS in PET and SPECT studies. Treatment response comparison between unipolar depression and BD revealed almost equal responses. Neuroimaging evidence suggests various correlates of response to rTMS in BD, which needs to be further replicated in future studies.

Antisocial behavior involves actions that disregard the basic rights of others and may represent a threat to the social system. The neural processes associated with being subject to antisocial behavior, including social victimization, are still unknown. In this study, we used a social interaction task during functional magnetic resonance imaging to investigate the neural bases of social victimization. Brain activation and functional connectivity (FC) were estimated and correlated with the Big 5 Questionnaire, Temperament Evaluation in Memphis, Pisa and San Diego (TEMPS-M), and a Questionnaire of Daily Frustration scores. During social victimization, the right occipital and temporal cortex showed increased activation. The temporal cortex also had reduced FC with homotopic areas. Compared to the prosocial interaction, social victimization showed hyperactivation of the dorsomedial and lateral prefrontal cortex, putamen, and thalamus and increased FC of the medial-frontal–striatal–thalamic areas with the ventrolateral prefrontal cortex, insula, dorsal cingulate, and postcentral gyrus. Lastly, neuroticism, irritable temperament, and frustration scores were correlated with the magnitude of neural responses to social victimization. Our findings suggest that social victimization engages a set of regions associated with salience, emotional processing, and regulation, and these responses can be modulated by temperamental and personality traits.

Abstract Studies conducted in primary care as well as in psychiatric settings show that more than half of patients suffering from major depressive disorder (MDD) have poor adherence to antidepressants. Patients prematurely discontinue antidepressant therapy for various reasons, including patient-related (e.g., misperceptions about antidepressants, side-effects, and lack of tolerability), clinician-related (e.g., insufficient instruction received by clinicians about the medication, lack of shared decision-making, and follow-up care), as well as structural factors (e.g., access, cost, and stigma). The high rate of poor adherence to antidepressant treatments provides the impetus for identifying factors that are contributing to noncompliance in an individual patient, to implement a careful education about this phenomenon. As adherence to antidepressants is one of the major unmet needs in MDD treatment, being associated with negative outcomes, we sought to identify a series of priorities to be discussed with persons with MDD with the larger aim to improve treatment adherence. To do so, we analyzed a series of epidemiological findings and clinical reasons for this phenomenon, and then proceeded to define through a multi-step consensus a set of recommendations to be provided by psychiatrists and other practitioners at the time of the first (prescription) visit with patients. Herein, we report the results of this initiative.

Suicide ideation and behaviors are major health issues in the field of mental health. Several psychological and psychosocial factors have been taken into account as possible predictors of suicidality. Only recently affective temperaments have been considered as possible factors linked to suicide. This study aims to investigate the relationship between affective temperaments and suicidality, including the lifetime onset of suicide ideation, lifetime presence of suicide attempts and the total number of lifetime suicide attempts. This is a naturalistic multicentric observational study, involving outpatient units of seven University sites in Italy. Patients were administered with the short version of TEMPS-M and the Columbia Suicide Severity Rating Scale. A total of 653 participants were recruited, with a diagnosis of bipolar (55.7%), unipolar (35.8%) and cyclothymic disorder (8.4%). Regression models showed that the presence of lifetime suicide behaviors was increased in patients presenting trait related impulsivity (p < 0.0001), poor free-interval functioning (p < 0.05), higher number of affective episodes (p < 0.01), higher number of hospitalizations (p < 0.0001), cyclothymic and irritable affective temperaments (p < 0.05 and p < 0.05, respectively). Conversely, the presence of hyperthymic affective disposition reduced the likelihood of having suicidal behaviors (p < 0.01). Lifetime suicidal ideation was associated with trait-related impulsivity (p < 0.001), poor free-interval functioning (p < 0.05), higher number of affective episodes (p < 0.001) and of hospitalizations (p < 0.001). Depressive temperaments increased the likelihood of presenting suicidal ideation (p < 0.05), along with irritable temperaments (p < 0.01), contrary to hyperthymic affective (p < 0.05). Results of the present study confirm that affective disposition has a significant impact on the onset of suicidal ideation and behaviors, and that affective dispositions should be assessed in clinical settings to identify people at risk of suicide. Moreover, a wider clinical evaluation, including different clinical psychopathological dimensions, should be taken into consideration to develop effective preventive interventions.

The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of effective connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal.The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task.

Trait-related impulsiveness is highly prevalent in patients with mood disorders, being associated with negative outcomes. The predictive role of affective temperaments on trait-related impulsivity is still understudied. The aim of the present study is to investigate the relationship between impulsivity and affective temperaments in a sample of euthymic patients with mood disorders. This is a real-world multicentric observational study, carried out at the outpatient units of seven university sites in Italy. All patients filled in the short version of Munster Temperament Evaluation of the Memphis, Pisa, Paris and San Diego and the Barratt Impulsiveness Scale. The study sample included 653 participants, mainly female (58.2%), with a mean age of 46.9 (±14.1). Regression analyses showed that higher levels of trait-related impulsivity were associated to suicide attempts (p < 0.000), the presence of psychotic symptoms during acute phases (p < 0.05), a seasonal pattern (p < 0.05), a lower age at onset of the disorder (p < 0.05), cyclothymic (p < 0.01) and irritable temperaments (p < 0.01). The results of our study highlight the importance to screen patients with mood disorders for impulsivity and affective temperaments in order to identify patients who are more likely to present a worse outcome and to develop personalized and integrated early pharmacological and psychosocial treatment plans. Novelties of the present paper include the recruitment of patients in a stable phase, which reduced possible bias in patients’ self-reports, and the multicentric nature of the study, resulting in the recruitment of a large sample of patients with mood disorders, geographically distributed across Italy, thus improving the generalizability of study results.

The complex structure of the brain supports high-order cognition, which is crucial for mastering chess. Surface-based measures, including the fractional dimension (FD) and gyrification index (GI), may be more sensitive in detecting cortical changes relative to volumetric indexes. For this reason, structural magnetic resonance imaging data from 29 chess experts and 29 novice participants were analyzed using the CAT12 toolbox. FD and GI for each brain region were compared between the groups. A multivariate model was used to identify surface-based brain measures that can predict chess expertise. In chess experts, FD is increased in the left frontal operculum (p < 0.01), and this change correlates with the starting age of chess practice (ρ = −0.54, p < 0.01). FD is decreased in the right superior parietal lobule (p < 0.01). Chess expertise is predicted by the FD in a network of fronto-parieto-temporal regions and is associated with GI changes in the middle cingulate gyrus (p < 0.01) and the superior temporal sulcus (p < 0.01). Our findings add to the evidence that chess expertise is based on the complex properties of the brain surface of a network of transmodal association areas important for flexible high-level cognitive functions. Interestingly, these changes are associated with long-lasting practice, suggesting that neuroplastic effects develop over time.

Prosocial behavior is critical for the natural development of an individual as well as for promoting social relationships. Although this complex behavior results from gratuitous acts occurring between an agent and a recipient and a wealth of literature on prosocial behavior has investigated these actions, little is known about the effects on the recipient and the neurobiology underlying them. In this study, we used functional magnetic resonance imaging to identify neural correlates of receiving prosocial behavior in the context of real-world experiences, with different types of action provided by the agent, including practical help and effort appreciation. Practical help was associated with increased activation in a network of regions spanning across bilateral superior temporal sulcus, temporoparietal junction, temporal pole, and medial prefrontal cortex. Effort appreciation was associated with activation and increased task-modulated connectivity of the occipital cortex. Prosocial-dependent brain responses were associated with positive affect. Our results support the role of the theory of mind network and the visual cortices in mediating the positive effects of receiving gratuitous help. Moreover, they indicate that specific types of prosocial behavior are mediated by distinct brain networks, which further demonstrates the uniqueness of the psychological processes underlying prosocial actions.

The development of awake intraoperative brain-mapping procedures for resection of brain tumors is of growing interest in neuroscience, because it enables direct testing of brain tissue, previously only possible in non-human primates. In a recent study performed in this setting specific effects can be induced by direct electrical stimulation on different sectors of premotor cortex during the execution of a hand manipulation task. Specifically, direct electrical stimulation applied on a dorsal sector of precentral cortex led to an increase in motor unit recruitment in hand muscles during execution of a hand manipulation task (Recruitment sector). The opposite effect was elicited when electrical stimulation was delivered more ventrally on the precentral cortex (Suppression sector). We studied whether the different effects on motor behavior induced by direct electrical stimulation applied on the two sites of the precentral cortex underlie differences in their functional connectivity with other brain areas, measured using resting state fMRI. Using healthy adults scanned as part of the Human Connectome Project, we computed the functional connectivity of each sector used as seeds. The functional connectivity patterns of the two intraoperative seeds was similar but cross-comparison revealed that the left and right Recruitment sectors had stronger functional connections with the hand region of the sensorimotor cortex, while the right Suppression region displayed stronger functional connectivity with a bilateral set of parieto-frontal areas crucial for the integration of perceptual and cognitive hand-related sensorimotor processes required for goal-related hand actions. Our results suggest that analyzing data obtained in the intraoperative setting with resting state functional magnetic resonance imaging in healthy brains can yield useful insight into the roles of different premotor sectors directly involved in hand-object interaction.

There is an increasing interest in identifying non-invasive biomarkers of disease severity and prognosis in idiopathic Parkinson’s disease (PD). Dopamine-transporter SPECT (DAT-SPECT), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI) provide unique information about the brain’s neurotransmitter and microstructural properties. In this study, we evaluate the relative and combined capability of these imaging modalities to predict symptom severity and clinical progression in de novo PD patients. To this end, we used MRI, SPECT, and clinical data of de novo drug-naïve PD patients (n = 205, mean age 61 ± 10) and age-, sex-matched healthy controls (n = 105, mean age 58 ± 12) acquired at baseline. Moreover, we employed clinical data acquired at 1 year follow-up for PD patients with or without L-Dopa treatment in order to predict the progression symptoms severity. Voxel-based group comparisons and covariance analyses were applied to characterize baseline disease-related alterations for DAT-SPECT, DTI, and sMRI. Cortical and subcortical alterations in de novo PD patients were found in all evaluated imaging modalities, in line with previously reported midbrain-striato-cortical network alterations. The combination of these imaging alterations was reliably linked to clinical severity and disease progression at 1 year follow-up in this patient population, providing evidence for the potential use of these modalities as imaging biomarkers for disease severity and prognosis that can be integrated into clinical trials.

Polyglutamine (polyQ) expansions in the androgen receptor (AR) gene cause spinal and bulbar muscular atrophy (SBMA), a neuromuscular disease characterized by lower motor neuron (MN) loss and skeletal muscle atrophy, with an unknown mechanism. We generated new mouse models of SBMA for constitutive and inducible expression of mutant AR and performed biochemical, histological and functional analyses of phenotype. We show that polyQ-expanded AR causes motor dysfunction, premature death, IIb-to-IIa/IIx fiber-type change, glycolytic-to-oxidative fiber-type switching, upregulation of atrogenes and autophagy genes and mitochondrial dysfunction in skeletal muscle, together with signs of muscle denervation at late stage of disease. PolyQ expansions in the AR resulted in nuclear enrichment. Within the nucleus, mutant AR formed 2% sodium dodecyl sulfate (SDS)-resistant aggregates and inclusion bodies in myofibers, but not spinal cord and brainstem, in a process exacerbated by age and sex. Finally, we found that two-week induction of expression of polyQ-expanded AR in adult mice was sufficient to cause premature death, body weight loss and muscle atrophy, but not aggregation, metabolic alterations, motor coordination and fiber-type switch, indicating that expression of the disease protein in the adulthood is sufficient to recapitulate several, but not all SBMA manifestations in mice. These results imply that chronic expression of polyQ-expanded AR, i.e. during development and prepuberty, is key to induce the full SBMA muscle pathology observed in patients. Our data support a model whereby chronic expression of polyQ-expanded AR triggers muscle atrophy through toxic (neomorphic) gain of function mechanisms distinct from normal (hypermorphic) gain of function mechanisms.

Background: Progressive neuronal loss in neurodegenerative diseases such as Parkinson’s disease (PD) is associated with progressive degeneration of associated white matter tracts as measured by diffusion tensor imaging (DTI). These findings may have diagnostic and functional implications but their value in de novo PD remains unknown. Here we analyzed longitudinal DTI data from Parkinson’s Progression Markers Initiative de novo PD patients for changes over time relative to healthy control (HC) participants.Methods: Baseline and 1-year follow-up DTI MRI data from 71 PD patients and 45 HC PPMI participants were included in the analyses. Whole-brain fractional anisotropy (FA) and mean diffusivity (MD) images were compared for baseline group differences and group–by–time interactions. Baseline and 1-year changes in DTI values were correlated with changes in DTI measures and symptom severity, respectively.Results: At baseline, PD patients showed significantly increased FA in brainstem, cerebellar, anterior corpus callosal, inferior frontal and inferior fronto-occipital white matter and increased MD in primary sensorimotor and supplementary motor regions. Over 1 year PD patients showed a significantly stronger decline in FA compared to HC in the optic radiation and corpus callosum and parietal, occipital, posterior temporal, posterior thalamic, and vermis gray matter. Significant increases in MD were observed in white matter of the midbrain, optic radiation and corpus callosum, while gray matter of prefrontal, insular and posterior thalamic regions. Baseline brainstem FA white matter (WM) values predicted 1-year changes in FA white matter and MD gray matter values. White but not gray matter changes in both FA and MD were significantly associated with changes in symptom severity.Conclusion: Significant gray and white matter DTI alterations are observable at the time of PD diagnosis and expand in the first year of de novo PD to other cortical and white matter regions. This pattern of DTI changes is in line with preclinical and neuroanatomical studies suggesting that the increased spatial spread of alpha-synuclein neuropathology is the key mechanism of PD progression. Taken together, these findings suggest that DTI may serve as a sensitive biomarker of disease progression in early-stage PD.

Despite their wide-spread use, only limited information is available on the comparative test-retest reliability of task-based functional and resting state magnetic resonance imaging measures of blood oxygen level dependence (tb-fMRI and rs-fMRI) and cerebral blood flow (CBF) using arterial spin labeling. This information is critical to designing properly powered longitudinal studies. Here we comprehensively quantified and compared the test-retest reliability and reproducibility performance of 8 commonly applied fMRI tasks, 6 rs-fMRI metrics and CBF in 30 healthy volunteers. We find large variability in test-retest reliability performance across the different tb-fMRI paradigms and rs-fMRI metrics, ranging from poor to excellent. A larger extent of activation in tb-fMRI is linked to higher between-subject reliability of the respective task suggesting that differences in the amount of activation may be used as a first reliability estimate of novel tb-fMRI paradigms. For rs-fMRI, a good reliability of local activity estimates is paralleled by poor performance of global connectivity metrics. Evaluated CBF measures provide in general a good to excellent test-reliability matching or surpassing the best performing tb-fMRI and rs-fMRI metrics. This comprehensive effort allows for direct comparisons of test-retest reliability between the evaluated MRI domains and measures to aid the design of future tb-fMRI, rs-fMRI and CBF studies.

Degeneration of dopaminergic neurons is a hallmark of Parkinson’s disease. However, its link to Parkinson’s disease symptoms remains unclear. Striatal resting state functional connectivity differentiates between Parkinson’s disease patients and healthy controls and might be a potential mediator of the effects of striatal dopaminergic degeneration onto Parkinson’s disease symptoms. Here, we evaluated the relationship between dopaminergic deficits, striatal functional connectivity (SFC) at rest and different Parkinson’s disease clinical symptoms in the largest currently established cohort of de novo Parkinson’s disease patients. We show that SFC is an independent predictor of symptom severity in Parkinson’s disease in addition to striatal dopaminergic deficits. Furthermore, we find that distinct SFC networks are associated with symptoms reflecting the ability to perform daily routine automatized motor tasks and clinician-rated Parkinson’s disease motor symptoms. We find that reduced SFC is a major and independent predictor of Parkinson’s disease symptoms going beyond the mere reflection of striatal dopaminergic input loss. These findings indicate the high value of SFC as a clinically relevant biomarker in Parkinson’s disease.

This research aims to explore the neural correlates involved in altruistic punishment, parochial altruism and anti-social punishment, using the Third-Party Punishment (TPP) game. In particular, this study considered these punishment behaviors in in-group vs. out-group game settings, to compare how people behave with members of their own national group and with members of another national group. The results showed that participants act altruistically to protect in-group members. This study indicates that norm violation in in-group (but not in out-group) settings results in increased activity in the medial prefrontal cortex and temporo-parietal junction, brain regions involved in the mentalizing network, as the third-party attempts to understand or justify in-group members' behavior. Finally, exploratory analysis during anti-social punishment behavior showed brain activation recruitment of the ventromedial prefrontal cortex, an area associated with altered regulation of emotions.

Variation of the gene coding for D2 receptors (DRD2) has been associated with risk for schizophrenia and with working memory deficits. A functional intronic SNP (rs1076560) predicts relative expression of the two D2 receptors isoforms, D2S (mainly pre-synaptic) and D2L (mainly post-synaptic). However, the effect of functional genetic variation of DRD2 on striatal dopamine D2 signaling and on its correlation with prefrontal activity during working memory in humans is not known.Thirty-seven healthy subjects were genotyped for rs1076560 (G>T) and underwent SPECT with [123I]IBZM (which binds primarily to post-synaptic D2 receptors) and with [123I]FP-CIT (which binds to pre-synaptic dopamine transporters, whose activity and density is also regulated by pre-synaptic D2 receptors), as well as BOLD fMRI during N-Back working memory.Subjects carrying the T allele (previously associated with reduced D2S expression) had striatal reductions of [123I]IBZM and of [123I]FP-CIT binding. DRD2 genotype also differentially predicted the correlation between striatal dopamine D2 signaling (as identified with factor analysis of the two radiotracers) and activity of the prefrontal cortex during working memory as measured with BOLD fMRI, which was positive in GG subjects and negative in GT.Our results demonstrate that this functional SNP within DRD2 predicts striatal binding of the two radiotracers to dopamine transporters and D2 receptors as well as the correlation between striatal D2 signaling with prefrontal cortex activity during performance of a working memory task. These data are consistent with the possibility that the balance of excitatory/inhibitory modulation of striatal neurons may also affect striatal outputs in relationship with prefrontal activity during working memory performance within the cortico-striatal-thalamic-cortical pathway.