RATIONALE:Cognitive control is crucial for optimal daily functioning and for emotional well-being. Cognitive control has been shown to be modified by experimental manipulations under widely differing experimental conditions, including cognitive training, and pharmacological intervention mainly probing catecholaminergic systems with little focus on the serotonergic system. OBJECTIVES:To explore the role of serotonin on cognitive control in emotional and non-emotional settings. METHODS:Behavioral, electrodermal and prefrontal activity measures were evaluated to compare the effects of single-session task repetition and single-dose serotonergic intervention with escitalopram on cognitive control in healthy participants, using cognitive and emotional Stroop tasks. RESULTS:For cognitive Stroop, task repetition improved performance both 'on-line' within an ongoing task and 'off-line' after a four-hour delay, and escitalopram had no additional effects on this. In emotional Stroop, escitalopram enhanced the practice-related performance gain, starting from the second stimulus of each block. Compared to placebo, escitalopram also significantly reduced overall rate of premature responses. Regarding brain activation, escitalopram significantly reduced prefrontal activity during cognitive and even more so during emotional Stroop task. Lastly, electrodermal response showed significant habituation during cognitive but not emotional Stroop, in an effect that was not significantly modified by escitalopram. CONCLUSIONS:Cognitive control in emotional and non-emotional settings may respond differently to behavioral and pharmacological manipulations. Escitalopram may selectively improve cognitive control in an emotional setting compared to cognitive control in non-emotional settings.

Gender dysphoria and autism spectrum disorder (ASD) co-occur at high rates. Yet, it is unknown whether gender dysphoria and ASD are associated with common or distinct neurobiological correlates or how they relate to experiences of gender-related body incongruence. Using the Social Responsiveness Scale, we assessed autistic traits in 99 transgender and 99 cisgender individuals and investigated their associations with gender-related body incongruence, measured via a visually based “Body Morph” test, and with cortical thickness in the brain. Autistic traits were significantly higher among transgender individuals, and those with higher autistic traits had higher body incongruence scoring. Among transgender individuals, higher autistic traits were linked with a thinner cortex bilaterally in the temporal pole and the superior and inferior temporal gyri. Autistic traits were only partly associated with cortical morphology patterns previously reported in transgender individuals; instead, they were primarily linked to temporal lobe areas mediating social cognition. While replicating the previous literature on the increased prevalence of autistic traits among transgender individuals, this study reports specific regions in the brains of transgender individuals where cortical thickness is associated with autistic traits.

Background Hard plastics are typically invisible in MRI images, while fluids are not. Objects manufactured using traditional Fuse Deposition Modelling (FDM) 3D-printing techniques are prone to leakage. 3D-printers which use UV-hardened resin to form objects possess a trait that is considered a technical inconvenience: Objects created using these techniques are inherently liquid-tight, and hollow parts require addition of drainage holes for uncured liquid resin to escape. If this is not done liquid resin will remain inside the object, which in some cases is the desired outcome. Digital Light Processing (DLP) is an affordable 3D-printing technique using UV-hardened resin. The technique also possesses superior dimensional accuracy and the ability to recreate fine details. Purpose We devised a method to produce an inherently MR-visible accessory using DLP technology with low dimensional tolerance to facilitate MR-guided breast biopsies. Methods By hollowing out the object without adding drainage holes and tuning printing parameters such as z-lift distance to retain as much uncured liquid resin inside as possible, objects that are inherently visible in MRI scans can be created without further treatment. Results Objects created through our method are simple and cheap to recreate, have minimal manufacturing steps, and are shown to be dimensionally exact and inherently MRI visible to be directly used in various applications without further treatment. Conclusion While this method may not always be suitable, we demonstrate that this process is viable in creating highly accurate objects that are inherently visible in MRI scans using equipment that is easy to obtain and trivial in cost.

Background3D printers have gained prominence in rapid prototyping and viable in creating dimensionally accurate objects that are both safe within a Magnetic Resonance Imaging (MRI) environment and visible in MRI scans. A challenge when making MRI-visible objects using 3D printing is that hard plastics are invisible in standard MRI scans, while fluids are not. So typically, a hollow object will be printed and filled with a liquid that will be visible in MRI scans. This poses an engineering challenge however since objects created using traditional Fused Deposition Modeling (FDM) 3D-printing techniques are prone to leakage. Digital Light Processing (DLP) is a relatively modern and affordable 3D-printing technique using UV-hardened resin, capable of creating objects that are inherently liquid-tight. When printing hollow parts using DLP printers, one typically requires adding drainage holes for uncured liquid resin to escape during the printing process. If this is not done liquid resin will remain inside the object, which in our application is the desired outcome.PurposeWe devised a method to produce an inherently MRI-visible accessory using DLP technology with low dimensional tolerance to facilitate MRI-guided breast biopsies.MethodsBy hollowing out the object without adding drainage holes and tuning printing parameters such as z-lift distance to retain as much uncured liquid resin inside as possible through surface tension, objects that are inherently visible in MRI scans can be created without further post-processing treatment.ResultsObjects created through our method are simple and inexpensive to recreate, have minimal manufacturing steps, and are shown to be dimensionally exact and inherently MRI visible to be directly used in various applications without further treatment.ConclusionOur proposed method of manufacturing objects that are inherently both MRI safe, and MRI visible. The proposed process is simple and does not require additional materials and tools beyond a DLP 3D-printer. With only an inexpensive DLP 3D-printer kit and basic cleaning and sanitation materials found in the hospital, we have demonstrated the viability of our process by successfully creating an object containing fine structures with low spatial tolerances used for MRI-guided breast biopsies.

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594. In an interim analysis, an artificial intelligence model was nearly four times more efficient in terms of cancers detected per number of magnetic resonance imaging tests, compared to traditional breast density measures used in a previous clinical trial.

BACKGROUND:Emotional dysregulation affects up to two-thirds of adult patients with attention-deficit/hyperactivity disorder (ADHD) and is increasingly seen as a core ADHD symptom that is clinically associated with greater functional impairment and psychiatric comorbidity. We sought to investigate emotional dysregulation in ADHD and explored its neural underpinnings. METHODS:We studied emotion induction and regulation in a clinical cohort of adult patients with ADHD before and after a stimulant challenge. We compared patients with age- and gender-matched healthy controls using behavioural, structural, and functional measures. We hypothesized that patients would demonstrate aberrant emotion processing compared with healthy controls, and sought to find whether this could be normalized by stimulant medication. RESULTS:Behaviourally, the ADHD group showed reduced emotion induction and regulation capacity. Brain imaging revealed abberant activation and deactivation patterns during emotion regulation, lower grey-matter volume in limbic and paralimbic areas, and greater grey-matter volume in visual and cerebellar areas, compared with healthy controls. The behavioural and functional deficits seen in emotion induction and regulation in the ADHD group were not normalized by stimulant medication. CONCLUSION:Patients with ADHD may have impaired emotion induction and emotion regulation capacity, but these deficits are not reversed by stimulant medication. These results have important clinical implications when assessing which aspects of emotional dysregulation are relevant for patients and if and how traditional ADHD pharmacotherapy affects emotion induction and emotion regulation.

Background: Functional seizures (FS) are paroxysmal episodes, resembling epileptic seizures, but without underlying epileptic abnormality. The aetiology and neuroanatomic associations are incompletely understood. Recent brain imaging data indicate cerebral changes, however, without clarifying possible pathophysiology. In the present study, we specifically investigated the neuroanatomic changes in subregions of the amygdala and hippocampus in FS.Methods: T1 MRI scans of 37 female patients with FS and 37 age-matched female seizure naive controls (SNC) were analyzed retrospectively in FreeSurfer version 7.1. Seizure naive controls included patients with depression and anxiety disorders. The analysis included whole-brain cortical thickness, subcortical volumes, and subfields of the amygdala and hippocampus. Group comparisons were carried out using multivariable linear models. Results: The FS and SNC groups did not differ in the whole hippocampus and amygdala volumes. However, patients had a significant reduction of the right lateral amygdala volume (p = 0.00041), an increase of the right central amygdala, (p = 0.037), and thinning of the left superior frontal gyrus (p = 0.024). Additional findings in patients were increased volumes of the right medial amygdala (p = 0.031), left anterior amygdala (p = 0.017), and left dentate gyrus of the hippocampus (p = 0.035).Conclusions: The observations from the amygdala and hippocampus segmentation affirm that there are neuroanatomic associations of FS. The pattern of these changes aligned with some of the cerebral changes described in chronic stress conditions and depression. The pattern of detected changes further study, and may, after validation, provide biomarkers for diagnosis and treatment.& COPY; 2023 Elsevier Inc. All rights reserved.

IntroductionAdaptive and successful emotion regulation, the ability to flexibly exert voluntary control over emotional experience and the ensuing behavior, is vital for optimal daily functioning and good mental health. In clinical settings, pharmacological and psychological interventions are widely employed to modify pathological emotion processing and ameliorate its deleterious consequences.MethodsIn this study, we investigated the acute effects of single-dose escitalopram on the induction and regulation of fear and disgust in healthy subjects. Furthermore, we compared these pharmacological effects with psychological emotion regulation that utilized a cognitive strategy with reappraisal. Emotion induction and regulation tasks were performed before and 4 h after ingestion of placebo or 10 mg escitalopram in a randomized, double-blind design. The International Affective Picture System (IAPS) was used as a source of images, with threat-related pictures selected for fear and disease and contamination-related pictures for disgust. Behavioral data, electrodermal activity (EDA), and functional near-infrared spectroscopy (fNIRS) recordings were collected.ResultsEscitalopram significantly reduced emotion intensity for both fear and disgust during emotion induction, albeit with differing electrodermal and hemodynamic activity patterns for the two negative emotions. At rest, i.e., in the absence of emotive stimuli, escitalopram increased sympathetic activity during the fear but not during the disgust experiments. For both fear and disgust, emotion regulation with reappraisal was more effective in reducing emotion intensity compared to pharmacological intervention with escitalopram or placebo.DiscussionWe concluded that emotion regulation with reappraisal and acute administration of escitalopram, but not placebo, reduce emotion intensity for both fear and disgust, with cognitive regulation being significantly more efficient compared to pharmacological regulation under the conditions of this study. Results from the fNIRS and EDA recordings support the concept of differential mechanisms of emotion regulation that could be emotion-specific.

Affective processing, including induction and regulation of emotion, activates neural networks, induces physiological responses, and generates subjective experience. Dysregulation of these processes can lead to maladaptive behavior and even psychiatric morbidity. Multimodal studies of emotion thus not only help elucidate the nature of emotion, but also contribute to important clinical insights. In the present study, we compared the induction (EI) and effortful regulation (ER) with reappraisal of fear and disgust in healthy subjects using functional near infrared spectroscopy (fNIRS) in conjunction with electrodermal activity (EDA). During EI, there was significant activation in medial prefrontal cortex (PFC) for fear and more widespread activation for disgust, with right lateral PFC significantly more active during disgust compared to fear. ER was equally effective for fear and disgust reducing subjective emotion rating by roughly 45%. Compared to baseline, there was no increased PFC activity for fear during ER, while for disgust lateral PFC was significantly more active. Significant differences between the two negative emotions were also observed in sympathetic nerve activity as reflected in EDA during EI, but not during ER. Lastly, compared to men, women had higher emotion rating for both fear and disgust without corresponding differences in EDA. In conclusion, in the present study we show that emotion induction was associated with differential activation in both PFC and sympathetic nerve activity for fear and disgust. These differences were however less prominent during emotion regulation. We discuss the potential interpretation of our results and their implications regarding our understanding of negative emotion processing.

Gender incongruence (GI) is characterized by a feeling of estrangement from the own body in the context of self. GI is often described in people who identify as transgender. The underlying mechanisms are unknown. Data from MRI measurements and tests of own body perception triggered us to pose a model that GI in transgender persons (TGI) could be associated with a disconnection within the brain circuits mediating the perception of own body as self. This is a departure from a previous model of sex atypical cerebral dimorphism, introducing a concept that better accords with a core feature of TGI. The present MRI study of 54 hormone naive transmen (TrM), 38 transwomen (TrW), 44 cismen and 41 ciswomen show that cortical gyrification, a metric that reflects early maturation of cerebral cortex, is significantly lower in transgender compared with cisgender participants. This reduction is limited to the occipito-parietal cortex and the sensory motor cortex, regions encoding own body image and body ownership. Moreover, the cortical gyrification correlated inversely with own body-self incongruence in these regions. These novel data suggest that GI in TGI may originate in the neurodevelopment of body image encoding regions. The results add potentially to understanding neurobiological contributors to gender identity.

Long-term fatigue and cognitive dysfunction affects 35% of allogeneic haematopoietic stem cell transplantation (aHSCT) survivors, suggesting a dysfunctional prefrontal cortex. In this study, we assessed prefrontal cortex and sympathetic nervous system activity in aHSCT patients with fatigue (n = 12), non-fatigued patients (n = 12) and healthy controls (n = 27). Measurement of near-infrared spectroscopy and electrodermal activity was carried out at rest and during cognitive performance (Stroop, verbal fluency and emotion regulation tasks). Prefrontal cortex and sympathetic nervous system activity were also analyzed in response to dopamine and noradrenaline increase after a single dose of methylphenidate. Baseline cognitive performance was similar in the two patient groups. However, after methylphenidate, only non-fatigued patients improved in Stroop accuracy and had better verbal fluency task performance compared to the fatigued group. Task-related activation of prefrontal cortex in fatigued patients was lower compared to non-fatigued patients during all cognitive tests, both before and after methylphenidate administration. During the Stroop task, reaction time, prefrontal cortex activation, and sympathetic nervous system activity were all lower in fatigued patients compared to healthy controls, but similar in non-fatigued patients and healthy controls. Reduced prefrontal cortex activity and sympathetic arousal suggests novel treatment targets to improve fatigue after aHSCT.

Objective: The supply territories of main cerebral arteries are predominantly identified based on distribution of infarct lesions in patients with large arterial occlusion; whereas, there is no consensus atlas regarding the supply territories of smaller end-arteries. In this study, we applied a data-driven approach to construct a stroke atlas of the brain using hierarchical density clustering in large number of infarct lesions, assuming that voxels/regions supplied by a common end-artery tend to infarct together. Methods: A total of 793 infarct lesions on MRI scans of 458 patients were segmented and coregistered to MNI-152 standard brain space. Applying a voxel-wise data-driven hierarchical density clustering algorithm, we identified those voxels that were most likely to be part of same infarct lesions in our dataset. A step-wise clustering scheme was applied, where the clustering threshold was gradually decreased to form the first 20 mother ( > 50 cm(3)) or main (1-50 cm(3)) clusters in addition to any possible number of tiny clusters ( < 1 cm(3)); and then, any resultant mother clusters were iteratively subdivided using the same scheme. Also, in a randomly selected 2/3 subset of our cohort, a bootstrapping cluster analysis with 100 permutations was performed to assess the statistical robustness of proposed clusters. Results: Approximately 91% of the MNI-152 brain mask was covered by 793 infarct lesions across patients. The covered area of brain was parcellated into 4 mother, 16 main, and 123 tiny clusters at the first hierarchy level. Upon iterative clustering subdivision of mother clusters, the brain tissue was eventually parcellated into 1 mother cluster (62.6 cm(3)), 181 main clusters (total volume 1107.3 cm(3)), and 917 tiny clusters (total volume of 264.8 cm(3)). In bootstrap analysis, only 0.12% of voxels, were labelled as "unstable" - with a greater reachability distance in cluster scheme compared to their corresponding mean bootstrapped reachability distance. On visual assessment, the mother/main clusters were formed along supply territories of main cerebral arteries at initial hierarchical levels, and then tiny clusters emerged in deep white matter and gray matter nuclei prone to small vessel ischemic infarcts. Conclusions: Applying voxel-wise data-driven hierarchical density clustering on a large number of infarct lesions, we have parcellated the brain tissue into clusters of voxels that tend to be part of same infarct lesion, and presumably representing end-arterial supply territories. This hierarchical stroke atlas of the brain is shared publicly, and can potentially be applied for future infarct location-outcome analysis.

Purpose: Characterize the static and dynamic functional connectivity for subjects with juvenile myoclonic epilepsy (JME) using a quantitative data-driven analysis approach. Methods: Whole-brain resting-state functional MRI data were acquired on a 3 T whole-body clinical MRI scanner from 18 subjects clinically diagnosed with JME and 25 healthy control subjects. 2-min sliding-window approach was incorporated in the quantitative data-driven data analysis framework to assess both the dynamic and static functional connectivity in the resting brains. Two-sample t-tests were performed voxel-wise to detect the differences in functional connectivity metrics based on connectivity strength and density. Results: The static functional connectivity metrics based on quantitative data-driven analysis of the entire 10-min acquisition window of resting-state functional MRI data revealed significantly enhanced functional connectivity in JME patients in bilateral dorsolateral prefrontal cortex, dorsal striatum, precentral and middle temporal gyri. The dynamic functional connectivity metrics derived by incorporating a 2-min sliding window into quantitative data-driven analysis demonstrated significant hyper dynamic functional connectivity in the dorsolateral prefrontal cortex, middle temporal gyrus and dorsal striatum. Connectivity strength metrics (both static and dynamic) can detect more extensive functional connectivity abnormalities in the resting-state functional networks (RFNs) and depict also larger overlap between static and dynamic functional connectivity results. Conclusion: Incorporating a 2-min sliding window into quantitative data-driven analysis of resting-state functional MRI data can reveal additional information on the temporally fluctuating RFNs of the human brain, which indicate that RFNs involving dorsolateral prefrontal cortex have temporal varying hyper dynamic characteristics in JME patients. Assessing dynamic along with static functional connectivity may provide further insights into the abnormal function connectivity underlying the pathological brain functioning in JME.

Hierarchical clustering is a useful data-driven approach to classify complex data and has been used to analyze resting-state functional magnetic resonance imaging (fMRI) data and derive functional networks of the human brain at very large scale, such as the entire visual or sensory-motor cortex. In this study, we developed a voxel-wise, whole-brain hierarchical clustering framework to perform multi-stage analysis of group-averaged resting-state fMRI data in different levels of detail. With the framework we analyzed particularly the somatosensory motor and visual systems in fine details and constructed the corresponding sub-dendrograms, which corroborate consistently with the known modular organizations from previous clinical and experimental studies. The framework provides a useful tool for data-driven analysis of resting-state fMRI data to gain insight into the hierarchical organization and degree of functional modulation among the sub-units.

Kinetic oscillatory stimulation (KOS) in the nasal cavity is a non-invasive cranial nerve stimulation method with promising efficacy for acute migraine and other inflammatory disorders. For a better understanding of the underlying neurophysiological mechanisms of KOS treatment, we conducted a resting-state functional magnetic resonance imaging (fMRI) study of 10 acute migraine patients and 10 normal control subjects during KOS treatment in a 3 T clinical MRI scanner. The fMRI data were first processed using a group independent component analysis (ICA) method and then further analyzed with a voxel-wise 3-way ANOVA modeling and region of interest (ROI) of functional connectivity metrics. All migraine participants were relieved from their acute migraine symptoms after 10–20 min KOS treatment and remained migraine free for 3–6 months. The resting-state fMRI result indicates that migraine patients have altered intrinsic functional activity in the anterior cingulate, inferior frontal gyrus and middle/superior temporal gyrus. KOS treatment gave rise to up-regulated intrinsic functional activity for migraine patients in a number of brain regions involving the limbic and primary sensory systems, while down regulating temporally the activity for normal controls in a few brain areas, such as the right dorsal posterior insula and inferior frontal gyrus. The result of this study confirms the efficacy of KOS treatment for relieving acute migraine symptoms and reducing attack frequency. Resting-state fMRI measurements demonstrate that migraine is associated with aberrant intrinsic functional activity in the limbic and primary sensory systems. KOS in the nasal cavity gives rise to the adjustment of the intrinsic functional activity in the limbic and primary sensory networks and restores the physiological homeostasis in the autonomic nervous system.

Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p < 0.01) features were identified by general linear modeling and used to generate a classification model for the framework. This feature-optimized classification of ICs with SVM (FOCIS) framework was used to classify both group and single subject ICA results. The classification results obtained using FOCIS and previously published FSL-FIX were compared against manually evaluated results. On average the false negative rate in identifying artifact contaminated ICs for FOCIS and FSL-FIX were 98.27 and 92.34%, respectively. The number of artifact and functional network components increased almost linearly with the input NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC < 33, whereas only a few limited ICs are affected by direct splitting when NIC is incremented beyond NIC > 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data.

BACKGROUND: Previous studies using hierarchical clustering approach to analyze resting-state fMRI data were limited to a few slices or regions-of-interest (ROIs) after substantial data reduction. PURPOSE: To develop a framework that can perform voxel-wise hierarchical clustering of whole-brain resting-state fMRI data from a group of subjects. MATERIALS AND METHODS: Resting-state fMRI measurements were conducted for 86 adult subjects using a single-shot echo-planar imaging (EPI) technique. After pre-processing and co-registration to a standard template, pair-wise cross-correlation coefficients (CC) were calculated for all voxels inside the brain and translated into absolute Pearson's distances after imposing a threshold CC≥0.3. The group averages of the Pearson's distances were then used to perform hierarchical clustering with the developed framework, which entails gray matter masking and an iterative scheme to analyze the dendrogram. RESULTS: With the hierarchical clustering framework, we identified most of the functional connectivity networks reported previously in the literature, such as the motor, sensory, visual, memory, and the default-mode functional networks (DMN). Furthermore, the DMN and visual system were split into their corresponding hierarchical sub-networks. CONCLUSION: It is feasible to use the proposed hierarchical clustering scheme for voxel-wise analysis of whole-brain resting-state fMRI data. The hierarchical clustering result not only confirmed generally the finding in functional connectivity networks identified previously using other data processing techniques, such as ICA, but also revealed directly the hierarchical structure within the functional connectivity networks.

PURPOSE: Hypothalamus is the control center of ANS which innervates cardiac and smooth muscles, various endocrine and exocrine glands; it influences the activity of most tissues and organ systems in the body. Therefore, Hypothalamus plays a critical role in maintaining homeostasis in the human body. ANS dysregulation is of significant clinical importance in terms of behavioral symptoms and designing therapeutic intervention. The purpose of this study is to investigate the dynamical functional response in hypothalamus to a novel therapeutic intervention which has been shown to be extraordinarily effective for treating migraine headache and understand the underlying neurological mechanisms for the intervention.