Task-based fMRI Research Articles

A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis

Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitivity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, atlases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodology. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an impressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse datasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support.

Cerebral hyperactivation across the Alzheimer’s disease pathological cascade

Abstract Neuronal dysfunction in specific brain regions or across distributed brain networks is a known feature of Alzheimer’s disease. An often reported finding in the early stage of the disease is the presence of increased functional MRI (fMRI) BOLD signal under task conditions relative to cognitively normal controls, a phenomenon known as “hyperactivation”. However, research in the past decades yielded complex, sometimes conflicting results. The magnitude and topology of fMRI hyperactivation patterns have been found to vary across the preclinical and clinical spectrum of Alzheimer’s disease, including concomitant “hypoactivation” in some cases. These incongruences are likely due to a range of factors, including the disease stage at which the cohort is examined, the brain areas or networks studied, and the fMRI paradigm utilised to evoke these functional abnormalities. Additionally, a perennial question pertains to the nature of hyperactivation in the context of Alzheimer’s disease. Some propose it reflects compensatory mechanisms to sustain cognitive performance while others suggest it is linked to the pathological disruption of a highly regulated homeostatic cycle that contributes to, or even drives, disease progression. Providing a coherent narrative for these empirical and conceptual discrepancies is paramount to develop disease models, understand the synergy between hyperactivation and the Alzheimer’s disease pathological cascade, and tailor effective interventions. We first provide a comprehensive overview of functional brain changes spanning the course from normal ageing to the clinical spectrum of Alzheimer’s disease. We then highlight evidence supporting a close relationship between fMRI hyperactivation and in vivo markers of Alzheimer’s pathology. We primarily focus on task-based fMRI studies in humans, but also consider studies using different functional imaging techniques and animal models. We then discuss the potential mechanisms underlying hyperactivation in the context of Alzheimer’s disease, and provide a testable framework bridging hyperactivation, ageing, cognition, and the Alzheimer’s disease pathological cascade. We conclude with a discussion of future challenges and opportunities to advance our understanding of the fundamental disease mechanisms of Alzheimer’s disease, and the promising development of therapeutic interventions incorporating or aimed at hyperactivation and large-scale functional systems.

The utilisation of fMRI for pre-operative mapping in the paediatric population with central nervous system tumours: a systematic review.

Functional MRI (fMRI) is a well-established tool for pre-operative planning, providing neurosurgeons with a roadmap of critical functional areas to preserve during surgery. Despite its increasing use, there is a need to compare task-based (tb-fMRI) and resting-state fMRI (rs-fMRI) in the peadiatric population to comprehensively evaluate the existing literature on the use of fMRI for pre-operative mapping in pediatric patients, comparing tb-fMRI and rs-fMRI. Two databases were searched for relevant studies published before July 2024 following the PRISMA guidelines. Eleven studies were selected and comprised 431 participants: 377 patients with different types and locations of brain tumours, and 54 healthy controls (HC). Results indicate that tb-fMRI could reliably locate the eloquent cortex with more than an 80% success rate. Furthermore, results were comparable with intraoperative mapping. Two studies reported that 68-81% of patients did not develop deficits in the postoperative period. Results also found that rs-fMRI can fill the gap in the situation of paediatric patients when other techniques do not apply to younger patients. This study suggests that tb-fMRI is more effective for pre-operative mapping in pediatric patients, offering precise localisation of critical brain functions and enhancing surgical planning. Although rs-fMRI is less demanding and compatible with light sedation, it lacks the specificity needed for accurate identification of language, sensory, and motor areas, which limits its clinical relevance. rs-fMRI can aid in function-preserving treatments for brain tumour patients and reduce the need for invasive procedures. Combining tb-fMRI with intraoperative mapping optimizes precision and safety in pediatric-neurosurgery.

Neural, genetic, and cognitive signatures of creativity

Creativity is typically operationalized as divergent thinking (DT) ability, a form of higher-order cognition which relies on memory, attention, and other component processes. Despite recent advances, creativity neuroscience lacks a unified framework to model its complexity across neural, genetic, and cognitive scales. Using task-based fMRI from two independent samples and MVPA, we identified a neural pattern that predicts DT, validated through cognitive decoding, genetic data, and large-scale resting-state fMRI. Our findings reveal that DT neural patterns span brain regions associated with diverse cognitive functions, with positive weights in the default mode and frontoparietal control networks and negative weights in the visual network. The high correlation with the primary gradient of functional connectivity suggests that DT involves extensive integration from concrete sensory information to abstract, higher-level cognition, distinguishing it from other advanced cognitive functions. Moreover, neurobiological analyses show that the DT pattern is positively correlated with dopamine-related neurotransmitters and genes influencing neurotransmitter release, advancing the neurobiological understanding of creativity.

Neural Mechanisms of Social Interaction Perception: Observing Interpersonal Synchrony Modulates Action Observation Network Activation and Is Spared in Autism.

How the temporal dynamics of social interactions are perceived arguably plays an important role in how one engages in social interactions and how difficulties in establishing smooth social interactions may occur. One aspect of temporal dynamics in social interactions is the mutual coordination of individuals' behaviors during social interaction, otherwise known as behavioral interpersonal synchrony (IPS). Behavioral IPS has been studied increasingly in various contexts, such as a feature of the social interaction difficulties inherent to autism. To fully understand the temporal dynamics of social interactions, or reductions thereof in autism, the neural basis of IPS perception needs to be established. Thus, the current study's aim was twofold: to establish the basic neuro-perceptual processing of IPS in social interactions for typical observers and to test whether it might differ for autistic individuals. In a task-based fMRI paradigm, participants viewed short, silent video vignettes of humans during social interactions featuring a variation of behavioral IPS. The results show that observing behavioral IPS modulates the Action Observation Network (AON). Interestingly, autistic participants showed similar neural activation patterns as non-autistic participants which were modulated by the behavioral IPS they observed in the videos, suggesting that the perception oftemporal dynamics of social interactions is spared and may not underly reduced behavioral IPS often observed in autism. Nevertheless, a general difference in processing social interactions was found in autistic observers, characterized by decreased neural activation in the right middle frontal gyrus, angular gyrus, and superior temporal areas. These findings demonstrate that although the autistic and non-autistic groups indeed differed in the neural processing of social interaction perception, the temporal dynamics of these social interactions were not the reason for these differences in social interaction perception in autism. Hence, spared recruitment of the AON for processing temporal dynamics of social interactions in autism does not account for the widely reported attenuation of IPS in autism and for the widely reported and presently observed differences in social interaction perception in autism.

Staged intervention to enable the resection of a large left temporoinsular cystic glioblastoma with language preservation: illustrative case.

Resection of glioblastoma (GBM) in eloquent regions depends on functional mapping to limit perioperative neurological morbidity. When neurological deficits preclude reliable mapping, neurosurgeons should explore potential mitigation strategies. The authors present the case of a patient with a large left cystic temporoinsular GBM and aphasia, for whom the authors used intraoperative language mapping and a staged approach to enable safe tumor resection. A 49-year-old female presented with progressive mixed aphasia for 1 month and new-onset right facial droop. Magnetic resonance imaging (MRI) revealed a large, heterogeneously enhancing, left temporoinsular tumor with a significant cystic component. Her aphasia was profound, and resection without reliable language mapping was deemed unsafe. An initial stereotactic tumoral cyst aspiration was performed, which reduced local mass effect and improved her language function. Cyst decompression thereby enabled both task-based functional MRI and intraoperative awake speech mapping, resulting in a safe resection of her GBM. Safe resection of eloquently localized GBM is compromised when neurological deficits prohibit intraoperative functional mapping. This case demonstrates a mitigation strategy specific to cystic lesions in which an initial-stage stereotactic cyst aspiration is aimed at generating sufficient interval neurological improvement, such that intraoperative functional mapping can be performed during a second-stage resection. https://thejns.org/doi/10.3171/CASE24362.

Patterns of abnormal activations in severe mental disorders a transdiagnostic data-driven meta-analysis of task-based fMRI studies.

Studies suggest severe mental disorders (SMDs), such as schizophrenia, major depressive disorder and bipolar disorder, are associated with common alterations in brain activity, albeit with a graded level of impairment. However, discrepancies between study findings likely to results from both small sample sizes and the use of different functional magnetic resonance imaging (fMRI) tasks. To address these issues, data-driven meta-analytic approach designed to identify homogeneous brain co-activity patterns across tasks was conducted to better characterize the common and distinct alterations between these disorders. A hierarchical clustering analysis was conducted to identify groups of studies reporting similar neuroimaging results, independent of task type and psychiatric diagnosis. A traditional meta-analysis (activation likelihood estimation) was then performed within each of these groups of studies to extract their aberrant activation maps. A total of 762 fMRI study contrasts were targeted, comprising 13 991 patients with SMDs. Hierarchical clustering analysis identified 5 groups of studies (meta-analytic groupings; MAGs) being characterized by distinct aberrant activation patterns across SMDs: (1) emotion processing; (2) cognitive processing; (3) motor processes, (4) reward processing, and (5) visual processing. While MAG1 was mostly commonly impaired, MAG2 was more impaired in schizophrenia, while MAG3 and MAG5 revealed no differences between disorder. MAG4 showed the strongest between-diagnoses differences, particularly in the striatum, posterior cingulate cortex, and ventromedial prefrontal cortex. SMDs are characterized mostly by common deficits in brain networks, although differences between disorders are also present. This study highlights the importance of studying SMDs simultaneously rather than independently.

Investigating the effective temporal resolution in a task-based functional MRI experiment at 7 T MRI using a dynamic phantom

Abstract An increasing number of human fMRI studies aim to discern the time delays between evoked responses under different stimuli conditions in different brain regions. To achieve that, a primary goal is to acquire fMRI data with high sampling rates. This task is now possible with ultra-high field (≥7 T) MRI and the advancement of imaging acceleration methods. Consequently, it becomes imperative to understand what is the actual or effective temporal resolution (ETR) that is realized in given settings of an fMRI experiment. In this study, we utilized a dynamic phantom to reliably repeat a set of scans, generating a “ground truth” signal with controllable onset delays mimicking fMRI responses in a task-based block-designed fMRI. Here, we define the ETR and quantify a scan’s ETR using the dynamic phantom. The quantification was performed for various scanning parameters, including echo time (TE), repetition time (TR), voxel size, and contrast-to-noise ratio (CNR). We further show that combining data from multi-echo EPI can improve the ETR (i.e., reduce it). In addition, parameters of the fMRI paradigm were examined, including the blocks’ length and density. As tissue properties (e.g., level of iron deposition) affect the CNR and thus change the ETR, we examined the signal rise mimicking not only the cortex, but also the basal ganglia (known for its high iron deposition). Combining multi-echo data, the estimated ETR for the examined scans was 151 ms for a cortex-mimicking setup and 248 ms for a basal ganglia-mimicking setup, when scanning with a sampling time (i.e., TR) of 600 ms. Yet, a substantial penalty was paid when the CNR was low, in which case the ETR was even larger than the TR. A feasibility set of experiments was also designed to evaluate how the ETR is affected by physiological signal fluctuations and the variability of the hemodynamic response. This study shows the viability of studying time responses with fMRI, by demonstrating that a very short ETR can be achieved. However, it also emphasizes the need to examine the attainable ETR for a particular experiment.

Developmental Differences in a Hippocampal–Cingulate Pathway Involved in Learned Safety Following Interpersonal Trauma Exposure

Nearly 65% of youth experience trauma, and up to one-third of youth with trauma exposure face profound mental health sequelae. There remains a need to elucidate factors that contribute to psychopathology following trauma exposure, and to optimize interventions for youth who do not benefit sufficiently from existing treatments. Here, we probe safety signal learning (SSL), which is a mechanism of fear reduction that leverages learned safety to inhibit fear in the presence of threat-associated stimuli and has been shown to attenuate fear via a hippocampal-cingulate--specifically, a dorsal anterior cingulate cortex (dACC)--pathway. The present study used behavioral and task-based functional magnetic resonance imaging data to examine age-related associations between interpersonal trauma exposure and the behavioral and neural correlates (ie, activation and functional connectivity) of SSL in a group of 102 youth (aged 9-19 years; 46 female, 56 male) with (n= 52) and without (n= 50) interpersonal trauma exposure. Primary analyses examined anterior hippocampal activation and anterior hippocampus-dACC functional connectivity. Exploratory analyses examined centromedial (CMA) and laterobasal (LBA) amygdala activation and anterior hippocampal, CMA, and LBA functional connectivity with additional anterior cingulate subregions (ie, subgenual anterior cingulate cortex [sgACC] and rostral anterior cingulate cortex [rosACC]). Both youth with and without interpersonal trauma exposure successfully learned conditioned safety, which was determined by using self-report of contingency awareness. Youth with interpersonal trauma exposure (relative to youth in the comparison group) exhibited age-specific patterns of lower hippocampal activation (F2,96= 3.75, pFDR= .049, ηp2= 0.072), and, in exploratory analyses, showed heightened centromedial amygdala activation (F1,96= 5.37, pFDR= .046, ηp2= 0.053) and an age-related decrease in hippocampal-sgACC functional connectivity during SSL (F1,94= 10.68, pFDR= .015, ηp2= 0.102). We also show that hippocampal-sgACC functional connectivity mediated the association between interpersonal trauma exposure and post-traumatic stress disorder symptoms in an age-specific manner in the overall sample. Together, these findings suggest that although age- and trauma-specific differences in the neural correlates of SSL may relate to the development of psychopathology, youth with interpersonal trauma exposure demonstrate successful learning of conditioned safety over time. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. While citing references scientificall`y relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science.

Rehabilitation Response in Tremor- and Non-Tremor-Dominant Parkinson Disease: A Task-fMRI Study.

Tremor-dominant (TD) and nontremor-dominant (NTD) Parkinson's disease (PD) showed different responses to rehabilitation. However, the neural mechanism behind this remains unclear. This cohort study explores changes in motor function, brain activation, and functional connectivity following 2 weeks of rehabilitation in TD-PD and NTD-PD patients, respectively. A total of 11 TD-PD patients, 24 NTD-PD patients, and 21 age-matched healthy controls (HCs) were included. At baseline, all participants underwent functional magnetic resonance imaging (fMRI) while performing the foot tapping task. Motor symptoms, gait, balance, and task-based fMRI were then evaluated in patients before and after rehabilitation. Compared to HCs, TD-PD patients showed increased activity in the left inferior frontal gyrus and the right insula, and NTD-PD patients showed increased activations in the left postcentral gyrus and decreased within-cerebellar connectivity at baseline. Rehabilitation improved motor function in PD patients regardless of motor subtype. TD-PD patients showed increased recruitments of the sensorimotor cortex and the bilateral thalamus after rehabilitation, and NTD-PD patients showed increased cerebellar activation and within-cerebellar connectivity that was associated with better motor performance. This study demonstrated that rehabilitation-induced brain functional reorganization varied by motor subtypes in PD, which may have important implications for making individualized rehabilitation programs. ClinicalTrials.gov identifier: ChiCTR1900020771.

A hierarchical atlas of the human cerebellum for functional precision mapping

The human cerebellum is activated by a wide variety of cognitive and motor tasks. Previous functional atlases have relied on single task-based or resting-state fMRI datasets. Here, we present a functional atlas that integrates information from seven large-scale datasets, outperforming existing group atlases. The atlas has three further advantages. First, the atlas allows for precision mapping in individuals: the integration of the probabilistic group atlas with an individual localizer scan results in a marked improvement in prediction of individual boundaries. Second, we provide both asymmetric and symmetric versions of the atlas. The symmetric version, which is obtained by constraining the boundaries to be the same across hemispheres, is especially useful in studying functional lateralization. Finally, the regions are hierarchically organized across three levels, allowing analyses at the appropriate level of granularity. Overall, the present atlas is an important resource for the study of the interdigitated functional organization of the human cerebellum in health and disease.

Graded Intensity Aerobic Exercise to Improve Cerebrovascular Function and Performance in Older Veterans: Protocol for a Randomized Controlled Trial.

Growing health care challenges resulting from a rapidly expanding aging population necessitate examining effective rehabilitation techniques that mitigate age-related comorbidity and improve quality of life. To date, exercise is one of a few proven interventions known to attenuate age-related declines in cognitive and sensorimotor functions critical to sustained independence. This work aims to implement a multimodal imaging approach to better understand the mechanistic underpinnings of the beneficial exercise-induced adaptations to sedentary older adults' brains and behaviors. Due to the complex cerebral and vascular dynamics that encompass neuroplastic change with aging and exercise, we propose an imaging protocol that will model exercise-induced changes to cerebral perfusion, cerebral vascular reactivity (CVR), and cognitive and sensorimotor task-dependent functional magnetic resonance imaging (fMRI) after prescribed exercise. Sedentary older adults (aged 65-80 years) were randomly assigned to either a 12-week aerobic-based interval-based cycling intervention or a 12-week balance and stretching intervention. Assessments of cardiovascular fitness used the YMCA submaximal VO2 test, basal cerebral perfusion using arterial spin labeling (ASL), CVR using hypercapnic fMRI, and cortical activation using fMRI during verbal fluency and motor tapping tasks. A battery of cognitive-executive and motor function tasks outside the scanning environment will be performed before and after the interventions. Our studies and others show that improved cardiovascular fitness in older adults results in improved outcomes related to physical and cognitive health as well as quality of life. A consistent but unexplained finding in many of these studies is a change in cortical activation patterns during task-based fMRI, which corresponds with improved task performance (cognitive-executive and motor). We hypothesize that the 12-week aerobic exercise intervention will increase basal perfusion and improve CVR through a greater magnitude of reactivity in brain areas susceptible to neural and vascular decline (inferior frontal and motor cortices) in previously sedentary older adults. To differentiate between neural and vascular adaptations in these regions, we will map changes in basal perfusion and CVR over the inferior frontal and the motor cortices-regions we have previously shown to be beneficially altered during fMRI BOLD (blood oxygen level dependent), such as verbal fluency and motor tapping, through improved cardiovascular fitness. Exercise is one of the most impactful interventions for improving physical and cognitive health in aging. This study aims to better understand the mechanistic underpinnings of improved health and function of the cerebrovascular system. If our hypothesis of improved perfusion and cerebrovascular reactivity following a 12-week aerobic exercise intervention is supported, it would add critically important insights into the potential of exercise to improve brain health in aging and could inform exercise prescription for older adults at risk for neurodegenerative disease brought on by cerebrovascular dysfunction. ClinicalTrials.gov NCT05932069; https://clinicaltrials.gov/study/NCT05932069. DERR1-10.2196/58316.

Charting the brain networks of impulsivity: Meta-analytic synthesis, functional connectivity modelling, and neurotransmitter associations

Abstract Impulsivity is a multi-dimensional construct that plays a crucial role in human behaviour and is believed to be a transdiagnostic marker of several psychiatric disorders. However, given its multi-faceted nature, investigations of its neural correlates are challenging and systematic comparisons across dimensions are lacking. In this study, we used a comprehensive multi-modal approach to investigate the functional network organisation of two dimensions in which impulsivity manifests: decision-making and response inhibition. Activation likelihood estimation (ALE) meta-analyses of task-based fMRI studies within each dimension identified two distinct and non-overlapping functional systems. One located in the default-mode network, associated with value-based judgements and goal-directed decision-making, and the other distributed across higher-order networks associated with cognitive control. Resting-state functional connectivity revealed the two systems were organised into four specialised communities of default-mode, cingulo-insular, fronto-parietal, and temporal regions. Finally, given the widespread use of neurotransmitter-acting medication to treat conditions with impulsive symptoms, we investigated the association between this organisation and neurochemistry and found that integration across communities was associated with PET-derived serotonin receptor density. Our findings reinforce insights from previous behavioural research and provide substantial evidence for the multi-dimensional nature of impulsivity on the neural level. This highlights the necessity for a comprehensive dimensional ontology on all levels of investigation to address impulsivity in a transdiagnostic manner.

Effects of high-definition tDCS targeting individual motor hotspot with EMG-driven robotic hand training on upper extremity motor function: a pilot randomized controlled trial

BackgroundDelivering HD-tDCS on individual motor hotspot with optimal electric fields could overcome challenges of stroke heterogeneity, potentially facilitating neural activation and improving motor function for stroke survivors. However, the intervention effect of this personalized HD-tDCS has not been explored on post-stroke motor recovery. In this study, we aim to evaluate whether targeting individual motor hotspot with HD-tDCS followed by EMG-driven robotic hand training could further facilitate the upper extremity motor function for chronic stroke survivors.MethodsIn this pilot randomized controlled trial, eighteen chronic stroke survivors were randomly allocated into two groups. The HDtDCS-group (n = 8) received personalized HD-tDCS using task-based fMRI to guide the stimulation on individual motor hotspot. The Sham-group (n = 10) received only sham stimulation. Both groups underwent 20 sessions of training, each session began with 20 min of HD-tDCS and was then followed by 60 min of robotic hand training. Clinical scales (Fugl-meyer Upper Extremity scale, FMAUE; Modified Ashworth Scale, MAS), and neuroimaging modalities (fMRI and EEG-EMG) were conducted before, after intervention, and at 6-month follow-up. Two-way repeated measures analysis of variance was used to compare the training effect between HDtDCS- and Sham-group.ResultsHDtDCS-group demonstrated significantly better motor improvement than the Sham-group in terms of greater changes of FMAUE scores (F = 6.5, P = 0.004) and MASf (F = 3.6, P = 0.038) immediately and 6 months after the 20-session intervention. The task-based fMRI activation significantly shifted to the ipsilesional motor area in the HDtDCS-group, and this activation pattern increasingly concentrated on the motor hotspot being stimulated 6 months after training within the HDtDCS-group, whereas the increased activation is not sustainable in the Sham-group. The neuroimaging results indicate that neural plastic changes of the HDtDCS-group were guided specifically and sustained as an add-on effect of the stimulation.ConclusionsStimulating the individual motor hotspot before robotic hand training could further enhance brain activation in motor-related regions that promote better motor recovery for chronic stroke.Trial registrationThis study was retrospectively registered in ClinicalTrials.gov (ID NCT05638464).

From gut to brain: unveiling probiotic effects through a neuroimaging perspective-A systematic review of randomized controlled trials.

The gut-brain axis, a bidirectional communication network between the gastrointestinal system and the brain, significantly influences mental health and behavior. Probiotics, live microorganisms conferring health benefits, have garnered attention for their potential to modulate this axis. However, their effects on brain function through gut microbiota modulation remain controversial. This systematic review examines the effects of probiotics on brain activity and functioning, focusing on randomized controlled trials using both resting-state and task-based functional magnetic resonance imaging (fMRI) methodologies. Studies investigating probiotic effects on brain activity in healthy individuals and clinical populations (i.e., major depressive disorder and irritable bowel syndrome) were identified. In healthy individuals, task-based fMRI studies indicated that probiotics modulate brain activity related to emotional regulation and cognitive processing, particularly in high-order areas such as the amygdala, precuneus, and orbitofrontal cortex. Resting-state fMRI studies revealed changes in connectivity patterns, such as increased activation in the Salience Network and reduced activity in the Default Mode Network. In clinical populations, task-based fMRI studies showed that probiotics could normalize brain function in patients with major depressive disorder and irritable bowel syndrome. Resting-state fMRI studies further suggested improved connectivity in mood-regulating networks, specifically in the subcallosal cortex, amygdala and hippocampus. Despite promising findings, methodological variability and limited sample sizes emphasize the need for rigorous, longitudinal research to clarify the beneficial effects of probiotics on the gut-brain axis and mental health.

The Mindful Brain: A Systematic Review of the Neural Correlates of Trait Mindfulness.

Trait self-report mindfulness scales measure one's disposition to pay nonjudgmental attention to the present moment. Concerns have been raised about the validity of trait mindfulness scales. Despite this, there is extensive literature correlating mindfulness scales with objective brain measures, with the goal of providing insight into mechanisms of mindfulness, and insight into associated positive mental health outcomes. Here, we systematically examined the neural correlates of trait mindfulness. We assessed 68 correlational studies across structural magnetic resonance imaging, task-based fMRI, resting-state fMRI, and EEG. Several consistent findings were identified, associating greater trait mindfulness with decreased amygdala reactivity to emotional stimuli, increased cortical thickness in frontal regions and insular cortex regions, and decreased connectivity within the default-mode network. These findings converged with results from intervention studies and those that included mindfulness experts. On the other hand, the connections between trait mindfulness and EEG metrics remain inconclusive, as do the associations between trait mindfulness and between-network resting-state fMRI metrics. ERP measures from EEG used to measure attentional or emotional processing may not show reliable individual variation. Research on body awareness and self-relevant processing is scarce. For a more robust correlational neuroscience of trait mindfulness, we recommend larger sample sizes, data-driven, multivariate approaches to self-report and brain measures, and careful consideration of test-retest reliability. In addition, we should leave behind simplistic explanations of mindfulness, as there are many ways to be mindful, and leave behind simplistic explanations of the brain, as distributed networks of brain areas support mindfulness.

A Systematic Review of Air Pollution Exposure and Brain Structure and Function during Development.

Air pollutants are known neurotoxicants. In this updated systematic review, we evaluate new evidence since our 2019 systematic review on the effect of outdoor air pollution exposure on childhood and adolescent brain structure and function as measured by magnetic resonance imaging (MRI). Using PubMed and Web of Science, we conducted an updated literature search and systematic review of articles published through March 2024, using key terms for air pollution and functional and/or structural MRI. Two raters independently screened all articles using Covidence and implemented the risk of bias instrument for systematic reviews informing the World Health Organization Global Air Quality Guidelines. We identified 222 relevant papers, and 14 new studies met our inclusion criteria. Including six studies from our 2019 review, the 20 publications to date include study populations from the United States, Netherlands, Spain, and United Kingdom. Studies investigated exposure periods spanning pregnancy through early adolescence, and estimated air pollutant exposure levels via personal monitoring, geospatial residential estimates, or school courtyard monitors. Brain MRI occurred when children were on average 6-14.7 years old; however, one study assessed newborns. Several MRI modalities were leveraged, including structural morphology, diffusion tensor imaging, restriction spectrum imaging, arterial spin labeling, magnetic resonance spectroscopy, as well as resting-state and task-based functional MRI. Air pollutants were associated with widespread brain differences, although the magnitude and direction of findings are largely inconsistent, making it difficult to draw strong conclusions. Prenatal and childhood exposure to outdoor air pollution is associated with structural and functional brain variations. Compared to our initial 2019 review, publications doubled-an increase that testifies to the importance of this public health issue. Further research is needed to clarify the effects of developmental timing, along with the downstream implications of outdoor air pollution exposure on children's cognitive and mental health.

Amygdala Reactivity, Antidepressant Discontinuation, and Relapse

Antidepressant discontinuation substantially increases the risk of a depression relapse, but the neurobiological mechanisms through which this happens are not known. Amygdala reactivity to negative information is a marker of negative affective processes in depression that is reduced by antidepressant medication, but it is unknown whether amygdala reactivity is sensitive to antidepressant discontinuation or whether any change is related to the risk of relapse after antidepressant discontinuation. To investigate whether amygdala reactivity to negative facial emotions changes with antidepressant discontinuation and is associated with subsequent relapse. The Antidepressiva Absetzstudie (AIDA) study was a longitudinal, observational study in which adult patients with remitted major depressive disorder (MDD) and currently taking antidepressants underwent 2 task-based functional magnetic resonance imaging (fMRI) measurements of amygdala reactivity. Patients were randomized to discontinuing antidepressants either before or after the second fMRI measurement. Relapse was monitored over a 6-month follow-up period. Study recruitment took place from June 2015 to January 2018. Data were collected between July 1, 2015, and January 31, 2019, and statistical analyses were conducted between June 2021 and December 2023. The study took place in a university setting in Zurich, Switzerland, and Berlin, Germany. Of 123 recruited patients, 83 were included in analyses. Of 66 recruited healthy control individuals matched for age, sex, and education, 53 were included in analyses. Discontinuation of antidepressant medication. Task-based fMRI measurement of amygdala reactivity and MDD relapse within 6 months after discontinuation. Among patients with MDD, the mean (SD) age was 35.42 (11.41) years, and 62 (75%) were women. Among control individuals, the mean (SD) age was 33.57 (10.70) years, and 37 (70%) were women. Amygdala reactivity of patients with remitted MDD and taking medication did not initially differ from that of control individuals (t125.136 = 0.33; P = .74). An increase in amygdala reactivity after antidepressant discontinuation was associated with depression relapse (3-way interaction between group [12W (waited) vs 1W2 (discontinued)], time point [MA1 (first scan) vs MA2 (second scan)], and relapse: β, 18.9; 95% CI, 0.8-37.1; P = .04). Amygdala reactivity change was associated with shorter times to relapse (hazard ratio, 1.05; 95% CI, 1.01-1.09; P = .01) and predictive of relapse (leave-one-out cross-validation balanced accuracy, 67%; 95% posterior predictive interval, 53-80; P = .02). An increase in amygdala reactivity was associated with risk of relapse after antidepressant discontinuation and may represent a functional neuroimaging marker that could inform clinical decisions around antidepressant discontinuation.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

Tracking moment-to-moment variation in cognition with informational connectivity: Comment on “connectivity analyses for task-based fMRI” by Huang, De Brigard, Cabeza, & Davis

Tracking moment-to-moment variation in cognition with informational connectivity: Comment on “connectivity analyses for task-based fMRI” by Huang, De Brigard, Cabeza, & Davis