Search

Neuroimaging studies have helped to illuminate the effects of opioids on the central nervous system (CNS) under various conditions, including acute opioid administration; treatment of acute, subacute, and chronic pain; and opioid use disorder (OUD). Exogenous opioids impart their effects via brain systems involved in pain, affect, cognition, and reward/value processes. Opioid effects on these brain processes may be similar across chronic pain and OUD treatment, although neuroimaging evidence on opioid effects in chronic pain remains relatively limited. Opioid-imparted CNS alterations, as assessed via neuroimaging, appear to relate to the dose and duration of opioid use, and these alterations may persist for several months after cessation of opioid use. Novel treatments for OUD and opioid cessation/tapering should focus on ameliorating opioid-induced CNS consequences. Evidence of central nervous system (CNS) exogenous opioid effects in humans has been primarily gained through neuroimaging of three participant populations: individuals after acute opioid administration, those with opioid use disorder (OUD), and those with chronic pain receiving opioid therapy. In both the brain and spinal cord, opioids alter processes of pain, cognition, and reward. Opioid-related CNS effects may persist and accumulate with longer opioid use duration. Meanwhile, opioid-induced benefits versus risks to brain health remain unclear. This review article highlights recent accumulating evidence for how exogenous opioids impact the CNS in humans. While investigation of CNS opioid effects has remained largely disparate across contexts of opioid acute administration, OUD, and chronic pain opioid therapy, integration across these contexts may enable advancement toward effective interventions. [ABSTRACT FROM AUTHOR]

Introduction: Functional brain templates are often used in the analysis of clinical functional MRI (fMRI) studies. However, these templates are mostly built based on anatomy or fMRI of healthy subjects, which have not been fully vetted in clinical cohorts. Our aim was to evaluate language templates by comparing with primary language areas (PLAs) detected from presurgical fMRI of brain tumor patients. Methods: Four language templates (A–D) based on anatomy, task‐based fMRI, resting‐state fMRI, and meta‐analysis, respectively, were compared with PLAs detected by fMRI with word generation and sentence completion paradigms. For each template, the fraction of PLA activations enclosed by the template (positive inclusion fraction, [PIF]), the fraction of activations within the template but that did not belong to PLAs (false inclusion fraction, [FIF]), and their Dice similarity coefficient (DSC) with PLA activations were calculated. Results: For anterior PLAs, Template A had the greatest PIF (median, 0.95), whereas Template D had both the lowest FIF (median, 0.074), and the highest DSC (median, 0.30), which were all significant compared to other templates. For posterior PLAs, Templates B and D had similar PIF (median, 0.91 and 0.90, respectively) and DSC (both medians, 0.059), which were all significantly higher than that of Template C. Templates B and C had significantly lower FIF (median, 0.061 and 0.054, respectively) compared to Template D. Conclusion: This study demonstrated significant differences between language templates in their inclusiveness of and spatial agreement with the PLAs detected in the presurgical fMRI of the patient cohort. These findings may help guide the selection of language templates tailored to their applications in clinical fMRI studies. [ABSTRACT FROM AUTHOR]

Background. Dyslexia is a neurological disorder that affects an individual’s language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multimodality-based DDMs. Methods. In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM. Results. The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction. [ABSTRACT FROM AUTHOR]

Altered postural control in the trunk/hip musculature is a characteristic of multiple neurological and musculoskeletal conditions. Previously it was not possible to determine if altered cortical and subcortical sensorimotor brain activation underlies impairments in postural control. This study used a novel fMRI-compatible paradigm to identify the brain activation associated with postural control in the trunk and hip musculature. BOLD fMRI imaging was conducted as participants performed two versions of a lower limb task involving lifting the left leg to touch the foot to a target. For the supported leg raise (SLR) the leg is raised from the knee while the thigh remains supported. For the unsupported leg raise (ULR) the leg is raised from the hip, requiring postural muscle activation in the abdominal/hip extensor musculature. Significant brain activation during the SLR task occurred predominantly in the right primary and secondary sensorimotor cortical regions. Brain activation during the ULR task occurred bilaterally in the primary and secondary sensorimotor cortical regions, as well as cerebellum and putamen. In comparison with the SLR, the ULR was associated with significantly greater activation in the right premotor/SMA, left primary motor and cingulate cortices, primary somatosensory cortex, supramarginal gyrus/parietal operculum, superior parietal lobule, cerebellar vermis, and cerebellar hemispheres. Cortical and subcortical regions activated during the ULR, but not during the SLR, were consistent with the planning, and execution of a task involving multisegmental, bilateral postural control. Future studies using this paradigm will determine mechanisms underlying impaired postural control in patients with neurological and musculoskeletal dysfunction.

Human adolescence marks a crucial phase of extensive brain development, highly susceptible to environmental influences. Employing brain age estimation to assess individual brain aging, we categorized individuals (N = 7,435, aged 9–10 years old) from the Adolescent Brain and Cognitive Development (ABCD) cohort into groups exhibiting either accelerated or delayed brain maturation, where the accelerated group also displayed increased cognitive performance compared to their delayed counterparts. A 4-way multi-set canonical correlation analysis integrating three modalities of brain metrics (gray matter density, brain morphological measures, and functional network connectivity) with nine environmental factors unveiled a significant 4-way canonical correlation between linked patterns of neural features, air pollution, area crime, and population density. Correlations among the three brain modalities were notably strong (ranging from 0.65 to 0.77), linking reduced gray matter density in the middle temporal gyrus and precuneus to decreased volumes in the left medial orbitofrontal cortex paired with increased cortical thickness in the right supramarginal and bilateral occipital regions, as well as increased functional connectivity in occipital sub-regions. These specific brain characteristics were significantly more pronounced in the accelerated brain aging group compared to the delayed group. Additionally, these brain regions exhibited significant associations with air pollution, area crime, and population density, where lower air pollution and higher area crime and population density were correlated to brain variations more prominently in the accelerated brain aging group.

Functional connectivity (FC) refers to the activation correlation between different brain regions. FC networks as typically represented as graphs with brain regions of interest (ROIs) as nodes and functional correlation as edges. Graph neural networks (GNNs) are machine learning architectures used to analyze FC graphs. However, traditional GNNs are limited in their ability to characterize FC edge attributes because they typically emphasize the importance of ROI node-based brain activation data. Line GNNs convert the edges of the original graph to nodes in the transformed graph, thereby emphasizing the FC between brain regions. We hypothesize that line GNNs will outperform traditional GNNs in FC applications. We investigated the performance of two common GNN architectures (GraphSAGE and GCN) trained on line and traditional graphs predicting task-associated FC changes across two datasets. The first dataset was from the Human Connectome Project (HCP) with 205 participants, the second was a dataset with 12 participants. The HCP dataset detailed FC changes in participants during a story-listening task, while the second dataset included the FC changes in a different auditory language task. Our findings from the HCP dataset indicated that line GNNs achieved lower mean squared error compared to traditional GNNs, with the line GraphSAGE model outperforming the traditional GraphSAGE by 18% (p

Resting-state functional MRI (fMRI) has revealed functional changes at the cortical level in degenerative cervical myelopathy (DCM) patients. The aim of this study was to systematically integrate static and dynamic functional connectivity (FC) to unveil abnormalities of functional networks of DCM patients and to analyze the prognostic value of these abnormalities for patients using resting-state fMRI. In this study, we collected clinical data and fMRI data from 44 DCM patients and 39 healthy controls (HC). Independent component analysis (ICA) was performed to investigate the group differences of intra-network FC. Subsequently, both static and dynamic FC were calculated to investigate the inter-network FC alterations in DCM patients. k-means clustering was conducted to assess temporal properties for comparison between groups. Finally, the support vector machine (SVM) approach was performed to predict the prognosis of DCM patients based on static FC, dynamic FC, and fusion of these two metrics. Relative to HC, DCM patients exhibited lower intra-network FC and higher inter-network FC. DCM patients spent more time than HC in the state in which both patients and HC were characterized by strong inter-network FC. Both static and dynamic FC could successfully classify DCM patients with different surgical outcomes. The classification accuracy further improved after fusing the dynamic and static FC for model training. In conclusion, our findings provide valuable insights into the brain mechanisms underlying DCM neuropathology on the network level.

BackgroundThere are increasing reports of the misuse of prescription psychostimulants for cognitive enhancement together with recreational cannabis. This raises a concern that chronic use of cannabis high in Δ9-THC may alter the sensitivity to amphetamines. In this exploratory study we hypothesized chronic exposure to Δ9-THC through vaporized cannabis would diminish the central nervous system (CNS) activity of Adderall.MethodsTo address this issue we exposed male and female mice to inhaled vaporized cannabis (10.3% Δ9-THC) or placebo for 30 min each day for ten consecutive days. After 24 h, mice were imaged fully awake for changes in BOLD signal following an IP injection of Adderall (60 µg) during the scanning session. After a 2-week washout, without any cannabis or placebo exposure, mice were again imaged and challenged with Adderall during the scanning session. The data were registered to a mouse 3D MRI atlas with 134 brain regions providing site-specific increases and decreases in global brain activity.ResultsMice exposed to cannabis when compared to placebo showed a decrease in brain activation to Adderall. The blunted Adderall response was characterized by a decrease in positive BOLD signal and increase in negative BOLD. The prefrontal cortex, accumbens, ventral pallidum, caudate/putamen, and thalamus were most affected. After a 2-week wash out there were no significant differences between the cannabis and placebo groups when challenged with Adderall.SummaryThis exploratory study shows that short, daily exposures to inhaled cannabis, something equivalent to recreational use, affects the sensitivity to the psychostimulant Adderall. The reduced Adderall effect on brain activity, particularly circuitry associated with dopaminergic signaling raises concerns about escalation in psychostimulant use.

IntroductionSchizophrenia is characterized by a loss of network features between cognition and reward sub-circuits (notably involving the mesolimbic system), and this loss may explain deficits in learning and cognition. Learning in schizophrenia has typically been studied with tasks that include reward related contingencies, but recent theoretical models have argued that a loss of network features should be seen even when learning without reward. We tested this model using a learning paradigm that required participants to learn without reward or feedback. We used a novel method for capturing higher order network features, to demonstrate that the mesolimbic system is heavily implicated in the loss of network features in schizophrenia, even when learning without reward.MethodsfMRI data (Siemens Verio 3T) were acquired in a group of schizophrenia patients and controls (n=78; 46 SCZ, 18 ≤ Age ≤ 50) while participants engaged in associative learning without reward-related contingencies. The task was divided into task-active conditions for encoding (of associations) and cued-retrieval (where the cue was to be used to retrieve the associated memoranda). No feedback was provided during retrieval. From the fMRI time series data, network features were defined as follows: First, for each condition of the task, we estimated 2nd order undirected functional connectivity for each participant (uFC, based on zero lag correlations between all pairs of regions). These conventional 2nd order features represent the task/condition evoked synchronization of activity between pairs of brain regions. Next, in each of the patient and control groups, the statistical relationship between all possible pairs of 2nd order features were computed. These higher order features represent the consistency between all possible pairs of 2nd order features in that group and embed within them the contributions of individual regions to such group structure.ResultsFrom the identified inter-group differences (SCZ ≠ HC) in higher order features, we quantified the respective contributions of individual brain regions. Two principal effects emerged: 1) SCZ were characterized by a massive loss of higher order features during multiple task conditions (encoding and retrieval of associations). 2) Nodes in the mesolimbic system were over-represented in the loss of higher order features in SCZ, and notably so during retrieval.DiscussionOur analytical goals were linked to a recent circuit-based integrative model which argued that synergy between learning and reward circuits is lost in schizophrenia. The model’s notable prediction was that such a loss would be observed even when patients learned without reward. Our results provide substantial support for these predictions where we observed a loss of network features between the brain’s sub-circuits for a) learning (including the hippocampus and prefrontal cortex) and b) reward processing (specifically constituents of the mesolimbic system that included the ventral tegmental area and the nucleus accumbens. Our findings motivate a renewed appraisal of the relationship between reward and cognition in schizophrenia and we discuss their relevance for putative behavioral interventions.

Abstract Background Recent developments suggest that neurosteroids may achieve rapid antidepressant effects. As such, neurosteroidogenesis mediated by the translocator protein 18 kDa (TSPO) might constitute a promising option for the treatment of depression. Therefore, the current clinical trial aims to get the first evidence of whether TPSO ligands promote rapid antidepressant effects. Furthermore, we study which mechanisms of action, e.g., modulation of distinct neuronal networks, neurosteroidogenesis, endocrinological mechanisms, TSPO expression or microbiome composition, contribute to their putative antidepressant effects. Methods This is a randomized, placebo-controlled, double-blind single-center trial of 2-week treatment with the TSPO ligand etifoxine versus placebo in depressive patients. Main eligibility criteria: male or female individuals aged 18 to 65 years with unipolar/bipolar depressive disorder with no other psychiatric main diagnosis or acute neurological/somatic disorder or drug/alcohol dependence during their lifetime. The primary endpoint is the time point at which 50% of the maximal effect has occurred (ET50) estimated by the scores of the Hamilton Depression Scale (HAMD-21). A total of 20 patients per group are needed to detect changes of therapeutic efficacy about 5% and changes of ET50 about 10% with a power of 70%. Assuming a drop-out rate of 10–20%, 50 patients will be randomized in total. The study will be conducted at the Department of Psychiatry and Psychotherapy of the University of Regensburg. Discussion This study will provide a first proof-of-concept on the potential of the TSPO ligand etifoxine in the treatment of depressive disorders. Trial registration Clinical Trials Register (EudraCT number: 2021-006773-38 , registration date: 14 September 2022) and German Register of Clinical Studies (DRKS number: DRKS00031099 , registration date: 23 January 2023).

Abstract Background Identification of cortical loci for lower limb movements for stroke rehabilitation is crucial for better rehabilitation outcomes via noninvasive brain stimulation by targeting the fine-grained cortical loci of the movements. However, identification of the cortical loci for lower limb movements using functional MRI (fMRI) is challenging due to head motion and difficulty in isolating different types of movement. Therefore, we developed a custom-made MR-compatible footplate and leg cushion to identify the cortical loci for lower limb movements and conducted multivariate analysis on the fMRI data. We evaluated the validity of the identified loci using both fMRI and behavioral data, obtained from healthy participants as well as individuals after stroke. Methods We recruited 33 healthy participants who performed four different lower limb movements (ankle dorsiflexion, ankle rotation, knee extension, and toe flexion) using our custom-built equipment while fMRI data were acquired. A subgroup of these participants (Dataset 1; n = 21) was used to identify the cortical loci associated with each lower limb movement in the paracentral lobule (PCL) using multivoxel pattern analysis and representational similarity analysis. The identified cortical loci were then evaluated using the remaining healthy participants (Dataset 2; n = 11), for whom the laterality index (LI) was calculated for each lower limb movement using the cortical loci identified for the left and right lower limbs. In addition, we acquired a dataset from 15 individuals with chronic stroke for regression analysis using the LI and the Fugl–Meyer Assessment (FMA) scale. Results The cortical loci associated with the lower limb movements were hierarchically organized in the medial wall of the PCL following the cortical homunculus. The LI was clearer using the identified cortical loci than using the PCL. The healthy participants (mean ± standard deviation: 0.12 ± 0.30; range: – 0.63 to 0.91) exhibited a higher contralateral LI than the individuals after stroke (0.07 ± 0.47; – 0.83 to 0.97). The corresponding LI scores for individuals after stroke showed a significant positive correlation with the FMA scale for paretic side movement in ankle dorsiflexion (R2 = 0.33, p = 0.025) and toe flexion (R2 = 0.37, p = 0.016). Conclusions The cortical loci associated with lower limb movements in the PCL identified in healthy participants were validated using independent groups of healthy participants and individuals after stroke. Our findings suggest that these cortical loci may be beneficial for the neurorehabilitation of lower limb movement in individuals after stroke, such as in developing effective rehabilitation interventions guided by the LI scores obtained for neuronal activations calculated from the identified cortical loci across the paretic and non-paretic sides of the brain.

In-Seon Lee,1 Da-Eun Yoon,1 Seoyoung Lee,2 Jae-Hwan Kang,3,4 Younbyoung Chae,1 Hi-Joon Park,1 Junsuk Kim5 1College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea; 2Department of Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway; 3Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea; 4Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea; 5School of Information Convergence, Kwangwoon University, Seoul, Republic of KoreaCorrespondence: Junsuk Kim, School of Information Convergence, Kwangwoon University, 20 Kwangwoon-ro, Seoul, 01897, Republic of Korea, Tel +82-2-940-8477, Email junsuk.kim@kw.ac.kr Hi-Joon Park, College of Korean Medicine, Kyung Hee University, 26 Kyunghee-daero, Seoul, 02447, Republic of Korea, Tel +82-2-961-9435, Email acufind@khu.ac.krPurpose: Only a few studies have focused on the brain mechanisms underlying the itch processing in AD patients, and a neural biomarker has never been studied in AD patients. We aimed to develop a deep learning model-based neural signature which can extract the relevant temporal dynamics, discriminate between AD and healthy control (HC), and between AD patients who responded well to acupuncture treatment and those who did not.Patients and Methods: We recruited 41 AD patients (22 male, age mean ± SD: 24.34 ± 5.29) and 40 HCs (20 male, age mean ± SD: 26.4 ± 5.32), and measured resting-state functional MRI signals. After preprocessing, 38 functional regions of interest were applied to the functional MRI signals. A long short-term memory (LSTM) was used to extract the relevant temporal dynamics for classification and train the prediction model. Bootstrapping and 4-fold cross-validation were used to examine the significance of the models.Results: For the identification of AD patients and HC, we found that the supplementary motor area (SMA), posterior cingulate cortex (PCC), temporal pole, precuneus, and dorsolateral prefrontal cortex showed significantly greater prediction accuracy than the chance level. For the identification of high and low responder to acupuncture treatment, we found that the lingual-parahippocampal-fusiform gyrus, SMA, frontal gyrus, PCC and precuneus, paracentral lobule, and primary motor and somatosensory cortex showed significantly greater prediction accuracy than the chance level.Conclusion: We developed and evaluated a deep learning model-based neural biomarker that can distinguish between AD and HC as well as between AD patients who respond well and those who respond less to acupuncture. Using the intrinsic neurological abnormalities, it is possible to diagnose AD patients and provide personalized treatment regimens.Keywords: Atopic Dermatitis, deep learning, functional MRI, biomarkers, personalized medicine

Peter A Pahapill,1 Elsa V Arocho-Quinones,1 Guangyu Chen,2 Brad Swearingen,3 Carissa W Tomas,3 Kevin M Koch,3 Andrew Scott Nencka3 1Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA; 2Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA; 3Center for Neuroimaging, Medical College of Wisconsin, Milwaukee, WI, USACorrespondence: Peter A Pahapill, Tel +1-414-805-5400, Fax +1-414-955-0115, Email ppahapill@mcw.eduBackground: Chronic low back pain (cLBP) has been associated with alterations in brain functional connectivity (FC) but based upon heterogeneous populations and single network analyses. Our goal is to study a more homogeneous cLBP population and focus on multiple cross-network (CN) connectivity analysis. We hypothesize that within this population: 1) altered CN FC, involving emotion and reward/aversion functions are related to their pain levels and 2) altered relationships are dependent upon pain phenotype (constant neuropathic vs intermittent pain).Methods: In this case series, resting state fcMRI scans were obtained over a study duration of 60 months from 23 patients (13 constant neuropathic and 10 intermittent pain) with Persistent Spinal Pain Syndrome (PSPS Type 2) being considered for spinal cord stimulation (SCS) therapy at a single academic center. Images were acquired using a Discovery MR750 GE scanner. During the resting state acquisitions, they were asked to close their eyes and relax. The CN analysis was performed on 7 brain networks and compared to age-matched controls. Linear regression was used to test the correlation between CN connectivity and pain scores.Results: CN FC involving emotion networks (STM: striatum network index) was significantly lower than controls in all patients, regardless of pain phenotype (P < 0.003). Pain levels were positively correlated with emotional FC for intermittent pain but negatively correlated for constant pain.Conclusion: This is the first report of 1) altered CN FC involving emotion/reward brain circuitry in 2) a homogeneous population of cLBP patients with 3) two different pain phenotypes (constant vs intermittent) in PSPS Type 2 patients being considered for SCS. FC patterns were altered in cLBP patients as compared to controls and were characteristic for each pain phenotype. These data support fcMRI as a potential and objective tool in assessing pain levels in cLBP patients with different pain phenotypes.Keywords: spinal cord stimulation, persistent spinal pain syndrome, functional MRI, chronic pain

BackgroundWhile identifying risk factors for adolescent depression is critical for early prevention and intervention, most studies have sought to understand the role of isolated factors rather than across a broad set of factors. Here, we sought to examine multi-level factors that maximize the prediction of depression symptoms in US children participating in the Adolescent Brain and Cognitive Development (ABCD) study.MethodsA total of 7,995 participants from ABCD (version 3.0 release) provided complete data at baseline and 1-year follow-up data. Depression symptoms were measured with the Child Behavior Checklist. Predictive features included child demographic, environmental, and structural and resting-state fMRI variables, parental depression history and demographic characteristics. We used linear (elastic net regression, EN) and non-linear (gradient-boosted trees, GBT) predictive models to identify which set of features maximized prediction of depression symptoms at baseline and, separately, at 1-year follow-up.ResultsBoth linear and non-linear models achieved comparable results for predicting baseline (EN: MAE = 3.757; R2  = 0.156; GBT: MAE = 3.761; R2  = 0.147) and 1-year follow-up (EN: MAE = 4.255; R2  = 0.103; GBT: MAE = 4.262; R2  = 0.089) depression. Parental history of depression, greater family conflict, and shorter child sleep duration were among the top predictors of concurrent and future child depression symptoms across both models. Although resting-state fMRI features were relatively weaker predictors, functional connectivity of the caudate was consistently the strongest neural feature associated with depression symptoms at both timepoints.ConclusionsConsistent with prior research, parental mental health, family environment, and child sleep quality are important risk factors for youth depression. Functional connectivity of the caudate is a relatively weaker predictor of depression symptoms but may represent a biomarker for depression risk.

Over the past decade, a growing body of research in adults has emphasized the role of the cerebellum in social and emotional cognition. This has been further supported by findings of delayed social and emotional development in toddlers with cerebellar injury during the fetal and newborn periods. However, the contributions of the cerebellum to social-emotional development in typically developing newborns are unclear. To bridge this gap in knowledge, we used multimodal MRI to investigate associations between cerebellar structure and function in 88 healthy neonates (mean ± sd of postmenstrual age, = 42.00 ± 1.91 weeks) and social-emotional development at 18-months assessed using the Infant-Toddler Social-Emotional Assessment (ITSEA) (mean age on ITSEA: 18.32 ± 1.19 months old). We found that cerebellar volume was not associated with ITSEA domain scores at 18 months. We further demonstrated cerebellar functional gradient (FGR) defined using principal component analysis (PCA) was associated with Externalizing domain (linear regression model, falsediscovery-rate-adjusted p = 0.013). This cluster (FGR7) included the left dentate, right VI, left Vermis VIIIb, and right V lobules. Finally, we demonstrated that either structural or functional features of the cerebellum reliably predicted scores on the Externalizing and Internalizing domains (correlation between actual and predicted scores: for structural, Fisher's z = 0.48 ± 0.01 for Internalizing, p = 0.01; for functional, Fisher's z = 0.45 ± 0.01 for Externalizing, p = 0.02; with permutation test). Collectively, our findings suggest that the cerebellum plays an important role in social-emotional development during the critical early stages of life. [ABSTRACT FROM AUTHOR]

Introduction: Cerebrospinal fluid (CSF) flow is involved in brain waste clearance and may be impaired in neurodegenerative diseases such as Parkinson's disease. This study aims to investigate the relationship between the CSF pulsation and the development of dementia in Parkinson's disease (PD) patients using EPI-based fMRI. Methods: We measured CSF pulsation in the 4th ventricle of 17 healthy controls and 35 PD patients using a novel CSF pulsation index termed "CSFpulse" based on echo-planar imaging (EPI)-based fMRI. The PD patients were classified into a PD with dementia high-risk group (PDD-H, n = 19) and a low risk group (PDD-L, n = 16), depending on their development of dementia within 5 years after initial brain imaging. The size of the 4th ventricle was measured using intensity-based thresholding. Results: We found that CSF pulsation was significantly higher in PD patients than in healthy controls, and that PD patients with high risk of dementia (PDD-H) had the highest CSF pulsation. We also observed an enlargement of the 4th ventricle in PD patients compared to healthy controls. Conclusion: Our results suggest that CSF pulsation may be a potential biomarker for PD progression and cognitive decline, and that EPI-based fMRI can be a useful tool for studying CSF flow and brain function in PD. [ABSTRACT FROM AUTHOR]

Optimizing the treatment of hydrocephalus remains a major challenge in adult and pediatric neurosurgery. Currently, clinical treatment relies heavily on anatomic imaging of ventricular size and clinical presentation. The emergence of functional and structural brain connectivity imaging has provided the basis for a new paradigm in the management of hydrocephalus. Here we review the pertinent advances in this field. Following PRISMA-ScR guidelines for scoping reviews, we searched PubMed for relevant literature from 1994 to April 2023 using hydrocephalus and MRI-related terms. Included articles reported original MRI data on human subjects with hydrocephalus, while excluding non-English or pre-1994 publications that didn't match the study framework. The review identified 44 studies that investigated functional and/or structural connectivity using various MRI techniques across different hydrocephalus populations. While there is significant heterogeneity in imaging technology and connectivity analysis, there is broad consensus in the literature that 1) hydrocephalus is associated with disruption of functional and structural connectivity, 2) this disruption in cerebral connectivity can be further associated with neurologic compromise 3) timely treatment of hydrocephalus restores both cerebral connectivity and neurologic compromise. The robustness and consistency of these findings vary as a function of patient age, hydrocephalus etiology, and the connectivity region of interest studied. Functional and structural brain connectivity imaging shows potential as an imaging biomarker that may facilitate optimization of hydrocephalus treatment. Future research should focus on standardizing regions of interest as well as identifying connectivity analysis most pertinent to clinical outcome. [ABSTRACT FROM AUTHOR]

Mental Time Travel (MTT) allows us to remember past events and imagine future ones. According to previous literature, the Temporal Distance of events affects MTT: our ability to order events worsens for close, compared to far, events. However, those studies established distances a-priori, albeit the way we perceive events' temporal distance may subjectively differ from their objective distance. Thus, in the current study, we aimed to investigate the effects of Perceived Temporal Distance (PTD) on the MTT ability and the brain areas mediating this process. Thirty-three healthy volunteers took part in an fMRI MTT task. Participants were asked to project themselves into the past, present, or future, and to judge a series of events as relative-past or relative-future, in relation to the adopted time location. Outside the scanner, participants provided PTD estimates for each stimulus of the MTT task. Participants' performance and functional activity were analyzed as a function of these estimations. At the behavioural level, PTD predicts the modulation of the performance for relative-past and relative-future. Bilateral angular gyrus, retrosplenial cortex, temporo-parietal region and medial, middle and superior frontal gyri mediate the PTD effect. In addition to these areas, the closer the relative-future events are perceived, the higher the involvement of left parahippocampal and lingual gyri and right cerebellum. Thus, perceived proximity of events activates frontal and posterior parietal areas, which therefore might mediate the processing of PTD in the cognitive spatial representation of time. Future proximity also activates cerebellum and medial temporal areas, known to be involved in imaginative and constructive cognitive functions. [ABSTRACT FROM AUTHOR]

Behavioral features of binge eating disorder (BED) suggest abnormalities in reward and inhibitory control. Studies of adult populations suggest functional abnormalities in reward and inhibitory control networks. Despite behavioral markers often developing in children, the neurobiology of pediatric BED remains unstudied. Fifty eight preadolescent children (aged 9–10 years) with BED and 68 age, body mass index and developmentally matched control children were extracted from the 3.0 baseline (Year 0) release of the Adolescent Brain Cognitive Development Study. We investigated task-evoked blood-oxygen-level-dependent response during functional magnetic resonance imaging, as participants completed the monetary incentive delay task and the stop signal task. We indexed group differences in regions of interest in the reward (orbitofrontal cortex, nucleus accumbens) and inhibitory control (dorsolateral prefrontal cortex, anterior cingulate cortex). No significant group differences emerged during tasks of inhibitory control among children with BED and children without BED. Similarly, no significant group differences emerged during the anticipation or receipt of reward among children with BED and children without BED. Preadolescent children with BED do not demonstrate abnormal neural activity in prominent nodes of reward or inhibitory control circuitry during tasks of inhibitory control, reward anticipation, and reward receipt, relative to children without BED who also had a similar body mass index. [ABSTRACT FROM AUTHOR]

Multiple brain imaging studies of negative emotional bias in major depressive disorder (MDD) have used images of fearful facial expressions and focused on the amygdala and the prefrontal cortex. The results have, however, been inconsistent, potentially due to small sample sizes (typically N < 50). It remains unclear if any alterations are a characteristic of current depression or of past experience of depression, and whether there are MDD-related changes in effective connectivity between the two brain regions. Activations and effective connectivity between the amygdala and dorsolateral prefrontal cortex (DLPFC) in response to fearful face stimuli were studied in a large population-based sample from Generation Scotland. Participants either had no history of MDD (N = 664 in activation analyses, N = 474 in connectivity analyses) or had a diagnosis of MDD during their lifetime (LMDD, N = 290 in activation analyses, N = 214 in connectivity analyses). The within-scanner task involved implicit facial emotion processing of neutral and fearful faces. Compared to controls, LMDD was associated with increased activations in left amygdala (P FWE = 0.031 , k E = 4) and left DLPFC (P FWE = 0.002 , k E = 33), increased mean bilateral amygdala activation (β = 0.0715 , P = 0.0314), and increased inhibition from left amygdala to left DLPFC, all in response to fearful faces contrasted to baseline. Results did not appear to be attributable to depressive illness severity or antidepressant medication status at scan time. Most studied participants had past rather than current depression, average severity of ongoing depression symptoms was low, and a substantial proportion of participants were receiving medication. The study was not longitudinal and the participants were only assessed a single time. LMDD is associated with hyperactivity of the amygdala and DLPFC, and with stronger amygdala to DLPFC inhibitory connectivity, all in response to fearful faces, unrelated to depression severity at scan time. These results help reduce inconsistency in past literature and suggest disruption of 'bottom-up' limbic-prefrontal effective connectivity in depression. • Lifetime MDD is related to higher amygdala and DLPFC activations to fearful faces. • There is increased inhibition from amygdala to DLPFC when viewing fearful faces in LMDD. • Changes are not attributed to acute MDD severity or antidepressant medication status. • Results help substantially reduce ambiguity in the previous depression literature. [ABSTRACT FROM AUTHOR]

Background: Identification of cortical loci for lower limb movements for stroke rehabilitation is crucial for better rehabilitation outcomes via noninvasive brain stimulation by targeting the fine-grained cortical loci of the movements. However, identification of the cortical loci for lower limb movements using functional MRI (fMRI) is challenging due to head motion and difficulty in isolating different types of movement. Therefore, we developed a custom-made MR-compatible footplate and leg cushion to identify the cortical loci for lower limb movements and conducted multivariate analysis on the fMRI data. We evaluated the validity of the identified loci using both fMRI and behavioral data, obtained from healthy participants as well as individuals after stroke. Methods: We recruited 33 healthy participants who performed four different lower limb movements (ankle dorsiflexion, ankle rotation, knee extension, and toe flexion) using our custom-built equipment while fMRI data were acquired. A subgroup of these participants (Dataset 1; n = 21) was used to identify the cortical loci associated with each lower limb movement in the paracentral lobule (PCL) using multivoxel pattern analysis and representational similarity analysis. The identified cortical loci were then evaluated using the remaining healthy participants (Dataset 2; n = 11), for whom the laterality index (LI) was calculated for each lower limb movement using the cortical loci identified for the left and right lower limbs. In addition, we acquired a dataset from 15 individuals with chronic stroke for regression analysis using the LI and the Fugl–Meyer Assessment (FMA) scale. Results: The cortical loci associated with the lower limb movements were hierarchically organized in the medial wall of the PCL following the cortical homunculus. The LI was clearer using the identified cortical loci than using the PCL. The healthy participants (mean ± standard deviation: 0.12 ± 0.30; range: – 0.63 to 0.91) exhibited a higher contralateral LI than the individuals after stroke (0.07 ± 0.47; – 0.83 to 0.97). The corresponding LI scores for individuals after stroke showed a significant positive correlation with the FMA scale for paretic side movement in ankle dorsiflexion (R2 = 0.33, p = 0.025) and toe flexion (R2 = 0.37, p = 0.016). Conclusions: The cortical loci associated with lower limb movements in the PCL identified in healthy participants were validated using independent groups of healthy participants and individuals after stroke. Our findings suggest that these cortical loci may be beneficial for the neurorehabilitation of lower limb movement in individuals after stroke, such as in developing effective rehabilitation interventions guided by the LI scores obtained for neuronal activations calculated from the identified cortical loci across the paretic and non-paretic sides of the brain. [ABSTRACT FROM AUTHOR]

Introduction: Approximately one in six people will experience an episode of major depressive disorder (MDD) in their lifetime. Effective treatment is hindered by subjective clinical decision-making and a lack of objective prognostic biomarkers. Functional MRI (fMRI) could provide such an objective measure but the majority of MDD studies has focused on static approaches, disregarding the rapidly changing nature of the brain. In this study, we aim to predict depression severity changes at 3 and 6 months using dynamic fMRI features. Methods: For our research, we acquired a longitudinal dataset of 32 MDD patients with fMRI scans acquired at baseline and clinical follow-ups 3 and 6 months later. Several measures were derived from an emotion face-matching fMRI dataset: activity in brain regions, static and dynamic functional connectivity between functional brain networks (FBNs) and two measures from a wavelet coherence analysis approach. All fMRI features were evaluated independently, with and without demographic and clinical parameters. Patients were divided into two classes based on changes in depression severity at both follow-ups. Results: The number of coherence clusters (nCC) between FBNs, reflecting the total number of interactions (either synchronous, anti-synchronous or causal), resulted in the highest predictive performance. The nCC-based classifier achieved 87.5% and 77.4% accuracy for the 3- and 6-months change in severity, respectively. Furthermore, regression analyses supported the potential of nCC for predicting depression severity on a continuous scale. The posterior default mode network (DMN), dorsal attention network (DAN) and two visual networks were the most important networks in the optimal nCC models. Reduced nCC was associated with a poorer depression course, suggesting deficits in sustained attention to and coping with emotion-related faces. An ensemble of classifiers with demographic, clinical and lead coherence features, a measure of dynamic causality, resulted in a 3-months clinical outcome prediction accuracy of 81.2%. Discussion: The dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts. [ABSTRACT FROM AUTHOR]

Schizophrenia is a chronic psychiatric disorder with characteristic symptoms of delusions, hallucinations, lack of motivation, and paucity of thought. Recent evidence suggests that the symptoms of schizophrenia, negative symptoms in particular, vary widely between the sexes and that symptom onset is earlier in males. A better understanding of sex‐based differences in functional magnetic resonance imaging (fMRI) studies of schizophrenia may provide a key to understanding sex‐based symptom differences. This study aimed to summarize sex‐based functional magnetic resonance imaging (fMRI) differences in brain activity of patients with schizophrenia. We searched PubMed and Scopus to find fMRI studies that assessed sex‐based differences in the brain activity of patients with schizophrenia. We excluded studies that did not evaluate brain activity using fMRI, did not evaluate sex differences, and were nonhuman or in vitro studies. We found 12 studies that met the inclusion criteria for the current systematic review. Compared to females with schizophrenia, males with schizophrenia showed more blood oxygen level‐dependent (BOLD) activation in the cerebellum, the temporal gyrus, and the right precuneus cortex. Male patients also had greater occurrence of low‐frequency fluctuations in cerebral blood flow in frontal and parietal lobes and the insular cortex, while female patients had greater occurrence of low‐frequency fluctuations in the hippocampus, parahippocampus, and lentiform nucleus. The current study summarizes fMRI studies that evaluated sex‐based fMRI brain differences in schizophrenia that may help to shed light on the underlying pathophysiology and further understanding of sex‐based differences in the clinical presentation and course of the disorder. [ABSTRACT FROM AUTHOR]

Blood‐level oxygenation‐dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher‐order connectivity. It is not yet clear how higher‐order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second‐order) and higher‐order connectivity. We show that genuine higher‐order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher‐order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher‐order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting‐state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher‐order networks that are distinct from second‐order resting‐state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher‐order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments. [ABSTRACT FROM AUTHOR]

Converging evidence has revealed disturbances in the corticostriatolimic system are associated with suicidal behaviors in adults with major depressive disorder. However, the neurobiological mechanism that confers suicidal vulnerability in depressed adolescents is largely unknown. A total of 86 depressed adolescents with and without prior suicide attempts (SA) and 47 healthy controls underwent resting-state functional imaging (R-fMRI) scans. The dynamic amplitude of low-frequency fluctuations (dALFF) was measured using sliding window approach. We identified SA-related alterations in dALFF variability primarily in the left middle temporal gyrus, inferior frontal gyrus, middle frontal gyrus (MFG), superior frontal gyrus (SFG), right SFG, supplementary motor area (SMA) and insula in depressed adolescents. Notably, dALFF variability in the left MFG and SMA was higher in depressed adolescents with recurrent suicide attempts than in those with a single suicide attempt. Moreover, dALFF variability was capable of generating better diagnostic and prediction models for suicidality than static ALFF. Our findings suggest that alterations in brain dynamics in regions involved in emotional processing, decision-making and response inhibition are associated with an increased risk of suicidal behaviors in depressed adolescents. Furthermore, dALFF variability could serve as a sensitive biomarker for revealing the neurobiological mechanisms underlying suicidal vulnerability. [ABSTRACT FROM AUTHOR]

Background: Ornithine transcarbamylase deficiency (OTCD) due to an X‐linked OTC mutation, is responsible for moderate to severe hyperammonemia (HA) with substantial morbidity and mortality. About 80% of females with OTCD remain apparently "asymptomatic" with limited studies of their clinical characteristics and long‐term health vulnerabilities. Multimodal neuroimaging studies and executive function testing have shown that asymptomatic females exhibit limitations when stressed to perform at higher cognitive load and had reduced activation of the prefrontal cortex. This retrospective study aims to improve understanding of factors that might predict development of defined complications and serious illness in apparent asymptomatic females. A proband and her daughter are presented to highlight the utility of multimodal neuroimaging studies and to underscore that asymptomatic females with OTCD are not always asymptomatic. Methods: We review data from 302 heterozygote females with OTCD enrolled in the Urea Cycle Disorders Consortium (UCDC) longitudinal natural history database. We apply multiple neuroimaging modalities in the workup of a proband and her daughter. Results: Among the females in the database, 143 were noted as symptomatic at baseline (Sym). We focused on females who were asymptomatic (Asx, n = 111) and those who were asymptomatic initially upon enrollment in study but who later became symptomatic sometime during follow‐up (Asx/Sym, n = 22). The majority of Asx (86%) and Asx/Sym (75%) subjects did not restrict protein at baseline, and ~38% of Asx and 33% of Asx/Sym subjects suffered from mild to severe neuropsychiatric conditions such as mood disorder and sleep problems. The risk of mild to severe HA sometime later in life for the Asx and Asx/Sym subjects as a combined group was ~4% (5/133), with ammonia ranging from 77 to 470 μM and at least half (2/4) of subjects requiring hospital admission and nitrogen scavenger therapy. For this combined group, the median age of first HA crisis was 50 years, whereas the median age of first symptom which included neuropsychiatric and/or behavioral symptoms was 17 years. The multimodal neuroimaging studies in female heterozygotes with OTCD also underscore that asymptomatic female heterozygotes with OTCD (e.g., proband) are not always asymptomatic. Conclusions: Analysis of Asx and Asx/Sym females with OTCD in this study suggests that future evidence‐based management guidelines and/or a clinical risk score calculator for this cohort could be useful management tools to reduce morbidity and improve long‐term quality of life. [ABSTRACT FROM AUTHOR]

Apathy is a common and disabling complication of Parkinson's disease characterized by reduced goal-directed behaviour. Several studies have reported dysfunction within prefrontal cortical regions and projections from brainstem nuclei whose neuromodulators include dopamine, serotonin and noradrenaline. Work in animal and human neuroscience have confirmed contributions of these neuromodulators on aspects of motivated decision-making. Specifically, these neuromodulators have overlapping contributions to encoding the value of decisions, and influence whether to explore alternative courses of action or persist in an existing strategy to achieve a rewarding goal. Building upon this work, we hypothesized that apathy in Parkinson's disease should be associated with an impairment in value-based learning. Using a four-armed restless bandit reinforcement learning task, we studied decision-making in 75 volunteers; 53 patients with Parkinson's disease, with and without clinical apathy, and 22 age-matched healthy control subjects. Patients with apathy exhibited impaired ability to choose the highest value bandit. Task performance predicted an individual patient's apathy severity measured using the Lille Apathy Rating Scale (R = −0.46, P < 0.001). Computational modelling of the patient's choices confirmed the apathy group made decisions that were indifferent to the learnt value of the options, consistent with previous reports of reward insensitivity. Further analysis demonstrated a shift away from exploiting the highest value option and a reduction in perseveration, which also correlated with apathy scores (R = −0.5, P < 0.001). We went on to acquire functional MRI in 59 volunteers; a group of 19 patients with and 20 without apathy and 20 age-matched controls performing the Restless Bandit Task. Analysis of the functional MRI signal at the point of reward feedback confirmed diminished signal within ventromedial prefrontal cortex in Parkinson's disease, which was more marked in apathy, but not predictive of their individual apathy severity. Using a model-based categorization of choice type, decisions to explore lower value bandits in the apathy group activated prefrontal cortex to a similar degree to the age-matched controls. In contrast, Parkinson's patients without apathy demonstrated significantly increased activation across a distributed thalamo-cortical network. Enhanced activity in the thalamus predicted individual apathy severity across both patient groups and exhibited functional connectivity with dorsal anterior cingulate cortex and anterior insula. Given that task performance in patients without apathy was no different to the age-matched control subjects, we interpret the recruitment of this network as a possible compensatory mechanism, which compensates against symptomatic manifestation of apathy in Parkinson's disease. [ABSTRACT FROM AUTHOR]

Simple Summary: The expense associated with training detection and service dogs is significant. By employing resting-state functional resonance imaging technique, a non-invasive method capable of probing brain function, we can identify the critical brain regions linked to selecting dogs inclined towards successful training. These biomarkers identified before commencement of training predict successful trainability and hence reduce training costs by obviating the need to invest in dogs that are unlikely to be successful. Furthermore, our research extends to elucidating the identified brain regions in dogs that exhibit homologous functions to those found in the human brain, offering valuable insights into the evolutionary parallels between humans and our closest animal companions. Functional brain connectivity based on resting-state functional magnetic resonance imaging (fMRI) has been shown to be correlated with human personality and behavior. In this study, we sought to know whether capabilities and traits in dogs can be predicted from their resting-state connectivity, as in humans. We trained awake dogs to keep their head still inside a 3T MRI scanner while resting-state fMRI data was acquired. Canine behavior was characterized by an integrated behavioral score capturing their hunting, retrieving, and environmental soundness. Functional scans and behavioral measures were acquired at three different time points across detector dog training. The first time point (TP1) was prior to the dogs entering formal working detector dog training. The second time point (TP2) was soon after formal detector dog training. The third time point (TP3) was three months' post detector dog training while the dogs were engaged in a program of maintenance training for detection work. We hypothesized that the correlation between resting-state FC in the dog brain and behavior measures would significantly change during their detection training process (from TP1 to TP2) and would maintain for the subsequent several months of detection work (from TP2 to TP3). To further study the resting-state FC features that can predict the success of training, dogs at TP1 were divided into a successful group and a non-successful group. We observed a core brain network which showed relatively stable (with respect to time) patterns of interaction that were significantly stronger in successful detector dogs compared to failures and whose connectivity strength at the first time point predicted whether a given dog was eventually successful in becoming a detector dog. A second ontologically based flexible peripheral network was observed whose changes in connectivity strength with detection training tracked corresponding changes in behavior over the training program. Comparing dog and human brains, the functional connectivity between the brain stem and the frontal cortex in dogs corresponded to that between the locus coeruleus and left middle frontal gyrus in humans, suggestive of a shared mechanism for learning and retrieval of odors. Overall, the findings point toward the influence of phylogeny and ontogeny in dogs producing two dissociable functional neural networks. [ABSTRACT FROM AUTHOR]

Background: Varenicline is considered one of the most effective treatment options for smoking cessation. Nonetheless, it is only modestly effective. A deeper comprehension of the effects of varenicline by means of the in-depth review of relevant fMRI studies may assist in paving the development of more targeted and effective treatments. Methodology: A search of PubMed and Google Scholar databases was conducted with the keywords "functional magnetic resonance imaging" or "fMRI", and "varenicline". All peer-reviewed articles regarding the assessment of smokers with fMRI while undergoing treatment with varenicline and meeting the predefined criteria were included. Results: Several studies utilizing different methodologies and targeting different aspects of brain function were identified. During nicotine withdrawal, decreased mesocorticolimbic activity and increased amygdala activity, as well as elevated amygdala-insula and insula-default-mode-network functional connectivity are alleviated by varenicline under specific testing conditions. However, other nicotine withdrawal-induced changes, including the decreased reward responsivity of the ventral striatum, the bilateral dorsal striatum and the anterior cingulate cortex are not influenced by varenicline suggesting a task-dependent divergence in neurocircuitry activation. Under satiety, varenicline treatment is associated with diminished cue-induced activation of the ventral striatum and medial orbitofrontal cortex concomitant with reduced cravings; during the resting state, varenicline induces activation of the lateral orbitofrontal cortex and suppression of the right amygdala. Conclusions: The current review provides important clues with regard to the neurobiological mechanism of action of varenicline and highlights promising research opportunities regarding the development of more selective and effective treatments and predictive biomarkers for treatment efficacy. [ABSTRACT FROM AUTHOR]

Introduction: Reminiscence therapy (RT), which engages individuals to evoke positive memories, has been shown to be effective in improving psychological well-being in older adults suffering from PTSD, depression, and anxiety. However, its impact on brain function has yet to be determined. This paper presents functional magnetic resonance imaging (fMRI) data to describe changes in autobiographical memory networks (AMN) in community-dwelling older adults. Methods: This pilot study used a within-subject design to measure changes in AMN activation in 11 older adults who underwent 6 weeks of RT. In the scanner, participants retrieved autobiographical memories which were either recent or remote, rehearsed or unrehearsed. Participants also underwent a clinical interview to assess changes in memory, quality of life, mental health, and affect. Findings: Compared to pretreatment, anxiety decreased (z = −2.014, p =.040) and activated significant areas within the AMN, including bilateral medial prefrontal cortex, left precuneus, right occipital cortex, and left anterior hippocampus. Conclusion: Although RT had subtle effects on psychological function in this sample with no evidence of impairments, including depression at baseline, the fMRI data support current thinking of the effect RT has on the AMN. Increased activation of right posterior hippocampus following RT is compatible with the Multiple Trace Theory Theory (Nadel & Moscovitch, 1997). [ABSTRACT FROM AUTHOR]

Objectives: Blood‐oxygen‐level‐dependent (BOLD) magnetic resonance imaging (MRI) facilitates the non‐invasive in‐vivo evaluation of placental oxygenation. The aims of this study were to identify and quantify a relative BOLD effect in response to hyperoxia in the human placenta and to compare it between pregnancies with and those without fetal growth restriction (FGR). Methods: This was a prospective multicenter study (NCT02238301) of 19 pregnancies with FGR (estimated fetal weight (EFW) on ultrasound < 5th centile) and 75 non‐FGR pregnancies (controls) recruited at two centers in Paris, France. Using a 1.5‐Tesla MRI system, the same multi‐echo gradient‐recalled echo (GRE) sequences were performed at both centers to obtain placental T2* values at baseline and in hyperoxic conditions. The relative BOLD effect was calculated according to the equation 100 × (hyperoxic T2* − baseline T2*)/baseline T2*. Baseline T2* values and relative BOLD effect were compared according to EFW (FGR vs non‐FGR), presence/absence of Doppler anomalies and birth weight (small‐for‐gestational age (SGA) vs non‐SGA). Results: We observed a relative BOLD effect in response to hyperoxia in the human placenta (median, 33.8% (interquartile range (IQR), 22.5–48.0%)). The relative BOLD effect did not differ significantly between pregnancies with and those without FGR (median, 34.4% (IQR, 24.1–48.5%) vs 33.7% (22.7–47.4%); P = 0.95). Baseline T2* Z‐score adjusted for gestational age at MRI was significantly lower in FGR pregnancies compared with non‐FGR pregnancies (median, −1.27 (IQR, −4.87 to −0.10) vs 0.33 (IQR, −0.81 to 1.02); P = 0.001). Baseline T2* Z‐score was also significantly lower in those pregnancies that subsequently delivered a SGA neonate (n = 23) compared with those that delivered a non‐SGA neonate (n = 62) (median, −0.75 (IQR, −3.48 to 0.29) vs 0.35 (IQR, −0.79 to 1.05); P = 0.01). Conclusions: Our study confirms a BOLD effect in the human placenta and that baseline T2* values are significantly lower in pregnancies with FGR. Further studies are needed to evaluate whether such parameters may detect placental insufficiency before it has a clinical impact on fetal growth. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Objective: A third of patients with epilepsy continue to have seizures despite receiving adequate antiseizure medication. Transcranial direct current stimulation (tDCS) might be a viable adjunct treatment option, having been shown to reduce epileptic seizures in patients with focal epilepsy. Evidence for the use of tDCS in genetic generalized epilepsy (GGE) is scarce. We aimed to establish the feasibility of applying tDCS during fMRI in patients with GGE to study the acute neuromodulatory effects of tDCS, particularly on sensorimotor network activity. Methods: Seven healthy controls and three patients with GGE received tDCS with simultaneous fMRI acquisition while watching a movie. Three tDCS conditions were applied: anodal, cathodal and sham. Periods of 60 s without stimulation were applied between each stimulation condition. Changes in sensorimotor cortex connectivity were evaluated by calculating the mean degree centrality across eight nodes of the sensorimotor cortex defined by the Automated Anatomical Labeling atlas (primary motor cortex (precentral left and right), supplementary motor area (left and right), mid-cingulum (left and right), postcentral gyrus (left and right)), across each of the conditions, for each participant. Results: Simultaneous tDCS-fMRI was well tolerated in both healthy controls and patients without adverse effects. Anodal and cathodal stimulation reduced mean degree centrality of the sensorimotor network (Friedman's ANOVA with Dunn's multiple comparisons test; adjusted p = 0.02 and p = 0.03 respectively). Mean degree connectivity of the sensorimotor network during the sham condition was not different to the rest condition (adjusted p = 0.94). Conclusion: Applying tDCS during fMRI was shown to be feasible and safe in a small group of patients with GGE. Anodal and cathodal stimulation caused a significant reduction in network connectivity of the sensorimotor cortex across participants. This initial research supports the feasibility of using fMRI to guide and understand network modulation by tDCS that might facilitate its clinical application in GGE in the future. [ABSTRACT FROM AUTHOR]

The N-back task is widely used to investigate working memory. Previous functional magnetic resonance imaging (fMRI) studies have shown that local brain activation depends on the difficulty of the N-back task. Recently, changes in functional connectivity and local activation during a task, such as a singlehand movement task, have been reported to give the distinct information. However, previous studies have not investigated functional connectivity changes in the entire brain during N-back tasks. In this study, we compared alterations in functional connectivity and local activation related to the difficulty of the N-back task. Because structural connectivity has been reported to be associated with local activation, we also investigated the relationship between structural connectivity and accuracy in a N-back task using diffusion tensor imaging (DTI). Changes in functional connectivity depend on the difficulty of the N-back task in a manner different from local activation, and the 2-back task is the best method for investigating working memory. This indicates that local activation and functional connectivity reflect different neuronal events during the N-back task. The top 10 structural connectivities associated with accuracy in the 2-back task were locally activated during the 2-back task. Therefore, structural connectivity as well as fMRI will be useful for predicting the accuracy of the 2-back task. [ABSTRACT FROM AUTHOR]

Background: In temporal lobe epilepsy (TLE), estimating the potential risk of language dysfunction before surgery is a necessary procedure. Functional MRI (fMRI) is considered the most useful to determine language lateralization noninvasively. However, there are no standardized language fMRI protocols, and several issues remain unresolved. In particular, the language tasks normally used are predominantly active paradigms that require the overt participation of patients, making assessment difficult for pediatric patients or patients with intellectual disabilities. In this study, task-based fMRI with passive narrative listening was applied to evaluate speech comprehension to estimate language function in Japanese-speaking patients with drug-resistant TLE. Methods: Twenty-one patients (six with intellectual disabilities) participated. Patients listened to passive auditory stimuli with combinations of forward and silent playback, and forward and backward playback. The activation results were extracted using a block design, and lateralization indices were calculated. The obtained fMRI results were compared to the results of the Wada test. Results: The concordance rate between fMRI and the Wada test was 95.2%. Meaningful responses were successfully obtained even from participants with intellectual disabilities. Conclusions: This passive fMRI paradigm can provide safe and easy presurgical language evaluation, particularly for individuals who may not readily engage in active paradigms. [ABSTRACT FROM AUTHOR]

In this paper, we discuss how the clustering analysis technique can be applied to analyze functional magnetic resonance imaging (fMRI) time-series data in the context of glioblastoma (GBM), a highly heterogeneous brain tumor. The precise characterization of GBM is challenging and requires advanced analytical approaches. We have synthesized the existing literature to provide an overview of how clustering algorithms can help identify unique patterns within the dynamics of GBM. Our review shows that the clustering of fMRI time series has great potential for improving the differentiation between various subtypes of GBM, which is pivotal for developing personalized therapeutic strategies. Moreover, this method proves to be effective in capturing temporal changes occurring in GBM, enhancing the monitoring of disease progression and response to treatment. By thoroughly examining and consolidating the current research, this paper contributes to the understanding of how clustering techniques applied to fMRI data can refine the characterization of GBM. This article emphasizes the importance of incorporating cutting-edge data analysis techniques into neuroimaging and neuro-oncology research. By providing a detailed perspective, this approach may guide future investigations and boost the development of tailored therapeutic strategies for GBM. [ABSTRACT FROM AUTHOR]